Visual Circular Analysis of 266 Years of Sunspot Counts.

Science.gov (United States)

Buelens, Bart

2016-06-01

Sunspots, colder areas that are visible as dark spots on the surface of the Sun, have been observed for centuries. Their number varies with a period of ∼11 years, a phenomenon closely related to the solar activity cycle. Recently, observation records dating back to 1749 have been reassessed, resulting in the release of a time series of sunspot numbers covering 266 years of observations. This series is analyzed using circular analysis to determine the periodicity of the occurrence of solar maxima. The circular analysis is combined with spiral graphs to provide a single visualization, simultaneously showing the periodicity of the series, the degree to which individual cycle lengths deviate from the average period, and differences in levels reached during the different maxima. This type of visualization of cyclic time series with varying cycle lengths in which significant events occur periodically is broadly applicable. It is aimed particularly at science communication, education, and public outreach.

Improvement of the photometric sunspot index and changes of the disk-integrated sunspot contrast with time

Science.gov (United States)

Froehlich, Claus; Pap, Judit M.; Hudson, Hugh S.

1994-06-01

The photometric sunspot index (PSI) was developed to study the effects of sunspots on solar irradiance. It is calculated from the sunspot data published in the Solar-Geophysical Data catalog. It has been shown that the former PSI models overestimate the effect of dark sunspots on solar irradiance; furthermore results of direct sunspot photometry indicate that the contrast of spots depends on their area. An improved PSI calculation is presented; it takes into account the area dependence of the contrast and calculates `true' daily means for each observation using the differential rotation of the spots. Moreover, the observations are screened for outliers which improves the homogeneity of the data set substantially, at least for the period after December 1981 when NOAA started to report data from a few instead of one to two stations. A detailed description of the method is provided. The correlation between the newly calculated PSI and total solar irradiance is studied for different phases of the solar cycles 21 and 22 using bi-variate spectral analysis. The results can be used as a `calibration' of PSI in terms of gain, the factor by which PSI has to be multiplied to yield the observed irradiance change. The factor changes with time from about 0.6 in 1980 to 1.1 in 1990. This unexpected result cannot be interpreted by a change of the contrast relative to the quiet Sun (as it is normally defined and determined by direct photometry) but rather as a change of the contrast between the spots and their surrounding as seen in total irradiance (integrated over the solar disk). This may partly be explained by a change in the ratio between the areas of the spots and the surrounding faculae.

Latitudinal migration of sunspots based on the ESAI database

Science.gov (United States)

Zhang, Juan; Li, Fu-Yu; Feng, Wen

2018-01-01

The latitudinal migration of sunspots toward the equator, which implies there is propagation of the toroidal magnetic flux wave at the base of the solar convection zone, is one of the crucial observational bases for the solar dynamo to generate a magnetic field by shearing of the pre-existing poloidal magnetic field through differential rotation. The Extended time series of Solar Activity Indices (ESAI) elongated the Greenwich observation record of sunspots by several decades in the past. In this study, ESAI’s yearly mean latitude of sunspots in the northern and southern hemispheres during the years 1854 to 1985 is utilized to statistically test whether hemispherical latitudinal migration of sunspots in a solar cycle is linear or nonlinear. It is found that a quadratic function is statistically significantly better at describing hemispherical latitudinal migration of sunspots in a solar cycle than a linear function. In addition, the latitude migration velocity of sunspots in a solar cycle decreases as the cycle progresses, providing a particular constraint for solar dynamo models. Indeed, the butterfly wing pattern with a faster latitudinal migration rate should present stronger solar activity with a shorter cycle period, and it is located at higher latitudinal position, giving evidence to support the Babcock-Leighton dynamo mechanism.

Multivariate Time Series Decomposition into Oscillation Components.

Science.gov (United States)

Matsuda, Takeru; Komaki, Fumiyasu

2017-08-01

Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the given multivariate time series and that each variable corresponds to a superposition of the projections of the oscillators. Thus, the oscillators superpose on each variable with amplitude and phase modulation. Based on this idea, we develop gaussian linear state-space models and use them to decompose the given multivariate time series. The model parameters are estimated from data using the empirical Bayes method, and the number of oscillators is determined using the Akaike information criterion. Therefore, the proposed method extracts underlying oscillators in a data-driven manner and enables investigation of phase dynamics in a given multivariate time series. Numerical results show the effectiveness of the proposed method. From monthly mean north-south sunspot number data, the proposed method reveals an interesting phase relationship.

Towards a first detailed reconstruction of sunspot information over the last 150 years

Science.gov (United States)

Lefevre, Laure; Clette, Frédéric

2013-04-01

With four centuries of solar evolution, the International Sunspot Number (SSN) forms the longest solar time series currently available. It provides an essential reference for understanding and quantifying how the solar output has varied over decades and centuries and thus for assessing the variations of the main natural forcing on the Earth climate. For such a quantitative use, this unique time-series must be closely monitored for any possible biases and drifts. This is the main objective of the Sunspot Workshops organized jointly by the National Solar Observatory (NSO) and the Royal Observatory of Belgium (ROB) since 2010. Here, we will report about some recent outcomes of past workshops, like diagnostics of scaling errors and their proposed corrections, or the recent disagreement between the sunspot sumber and other solar indices like the 10.7cm radio flux. Our most recent analyses indicate that while part of this divergence may be due to a calibration drift in the SSN, it also results from an intrinsic change in the global magnetic parameters of sunspots and solar active regions, suggesting a possible transition to a new activity regime. Going beyond the SSN series, in the framework of the SOTERIA, TOSCA and SOLID projects, we produced a survey of all existing catalogs providing detailed sunspot information and we also located different primary solar images and drawing collections that can be exploitable to complement the existing catalogs (COMESEP project). These are first steps towards the construction of a multi-parametric time series of multiple sunspot group properties over at least the last 150 years, allowing to reconstruct and extend the current 1-D SSN series. By bringing new spatial, morphological and evolutionary information, such a data set should bring major advances for the modeling of the solar dynamo and solar irradiance. We will present here the current status of this work. The catalog now extends over the last 3 cycles (Lefevre & Clette 2011

Revised Sunspot Numbers and the Effects on Understanding the Sunspot Cycle

Science.gov (United States)

Hathaway, D. H.

2014-12-01

While sunspot numbers provide only limited information about the sunspot cycle, they provide that information for at least twice as many sunspot cycles as any other direct solar observation. In particular, sunspot numbers are available before, during, and immediately after the Maunder Minimum (1645-1715). The instruments and methods used to count sunspots have changed over the last 400+ years. This leads to systematic changes in the sunspot number that can mask, or artificially introduce, characteristics of the sunspot cycle. The most widely used sunspot number is the International (Wolf/Zurich) sunspot number which is now calculated at the Solar Influences Data Center in Brussels, Belgium. These numbers extend back to 1749. The Group sunspot number extends back to the first telescopic observations of the Sun in 1610. There are well-known and significant differences between these two numbers where they overlap. Recent work has helped us to understand the sources of these differences and has led to proposed revisions in the sunspot numbers. Independent studies now support many of these revisions. These revised sunspot numbers suggest changes to our understanding of the sunspot cycle itself and to our understanding of its connection to climate change.

HELIOSEISMOLOGY OF A REALISTIC MAGNETOCONVECTIVE SUNSPOT SIMULATION

International Nuclear Information System (INIS)

Braun, D. C.; Birch, A. C.; Rempel, M.; Duvall, T. L. Jr.

2012-01-01

We compare helioseismic travel-time shifts measured from a realistic magnetoconvective sunspot simulation using both helioseismic holography and time-distance helioseismology, and measured from real sunspots observed with the Helioseismic and Magnetic Imager instrument on board the Solar Dynamics Observatory and the Michelson Doppler Imager instrument on board the Solar and Heliospheric Observatory. We find remarkable similarities in the travel-time shifts measured between the methodologies applied and between the simulated and real sunspots. Forward modeling of the travel-time shifts using either Born or ray approximation kernels and the sound-speed perturbations present in the simulation indicates major disagreements with the measured travel-time shifts. These findings do not substantially change with the application of a correction for the reduction of wave amplitudes in the simulated and real sunspots. Overall, our findings demonstrate the need for new methods for inferring the subsurface structure of sunspots through helioseismic inversions.

Sunspots

International Nuclear Information System (INIS)

Priest, E.R.

1982-01-01

The existence of sunspots has been known since ancient times, but it was only at the beginning of this century that they were found to be the sites of very strong magnetic fields, and it was realised that they represent the places where huge magnetic flux tubes burst through the solar surface. A theoretical understanding of sunspots has had to await the development of magnetohydrodynamics; however, even now, there is some controversy about answers to fundamental questions, such as: why is a sunspot cool, what is its equilibrium structure and how is it formed. Other topics that are discussed in the present chapter include magnetoconvection and the process of magnetic buoyancy whereby a flux tube deep within the Sun tends to rise towards the surface because it is lighter than its surroundings. Outside active regions the magnetic flux is not spread out uniformly to a weak field of a few Gauss, but instead it is mainly concentrated at supergranulation boundaries into intense flux tubes, whose properties are discussed. (Auth.)

Sunspots

International Nuclear Information System (INIS)

Moore, R.; Rabin, D.

1985-01-01

It is pointed out that the sun provides a close-up view of many astrophysically important phenomena, nearly all connected with the causes and effects of solar magnetic fields. The present article provides a review of the role of sunspots in a number of new areas of research. Connections with other solar phenomena are examined, taking into account flares, the solar magnetic cycle, global flows, luminosity variation, and global oscillations. A selective review of the structure and dynamic phenomena observed within sunspots is also presented. It is found that sunspots are usually contorted during the growth phase of an active region as magnetic field rapidly emerges and sunspots form, coalesce, and move past or even through each other. Attention is given to structure and flows, oscillations and waves, and plans for future studies. 145 references

Characterizing time series via complexity-entropy curves

Science.gov (United States)

Ribeiro, Haroldo V.; Jauregui, Max; Zunino, Luciano; Lenzi, Ervin K.

2017-06-01

The search for patterns in time series is a very common task when dealing with complex systems. This is usually accomplished by employing a complexity measure such as entropies and fractal dimensions. However, such measures usually only capture a single aspect of the system dynamics. Here, we propose a family of complexity measures for time series based on a generalization of the complexity-entropy causality plane. By replacing the Shannon entropy by a monoparametric entropy (Tsallis q entropy) and after considering the proper generalization of the statistical complexity (q complexity), we build up a parametric curve (the q -complexity-entropy curve) that is used for characterizing and classifying time series. Based on simple exact results and numerical simulations of stochastic processes, we show that these curves can distinguish among different long-range, short-range, and oscillating correlated behaviors. Also, we verify that simulated chaotic and stochastic time series can be distinguished based on whether these curves are open or closed. We further test this technique in experimental scenarios related to chaotic laser intensity, stock price, sunspot, and geomagnetic dynamics, confirming its usefulness. Finally, we prove that these curves enhance the automatic classification of time series with long-range correlations and interbeat intervals of healthy subjects and patients with heart disease.

SUNSPOT ROTATION AS A DRIVER OF MAJOR SOLAR ERUPTIONS IN THE NOAA ACTIVE REGION 12158

Energy Technology Data Exchange (ETDEWEB)

Vemareddy, P.; Ravindra, B. [Indian Institute of Astrophysics, Koramangala, Bangalore-560034 (India); Cheng, X., E-mail: vemareddy@iiap.res.in [School of Astronomy and Space Science, Nanjing University, Nanjing-210023 (China)

2016-09-20

We studied the development conditions of sigmoid structure under the influence of the magnetic non-potential characteristics of a rotating sunspot in the active region (AR) 12158. Vector magnetic field measurements from the Helioseismic Magnetic Imager and coronal EUV observations from the Atmospheric Imaging Assembly reveal that the erupting inverse-S sigmoid had roots at the location of the rotating sunspot. The sunspot rotates at a rate of 0°–5° h{sup −1} with increasing trend in the first half followed by a decrease. The time evolution of many non-potential parameters had a good correspondence with the sunspot rotation. The evolution of the AR magnetic structure is approximated by a time series of force-free equilibria. The non-linear force-free field magnetic structure around the sunspot manifests the observed sigmoid structure. Field lines from the sunspot periphery constitute the body of the sigmoid and those from the interior overlie the sigmoid, similar to a flux rope structure. While the sunspot was rotating, two major coronal mass ejection eruptions occurred in the AR. During the first (second) event, the coronal current concentrations were enhanced (degraded), consistent with the photospheric net vertical current; however, magnetic energy was released during both cases. The analysis results suggest that the magnetic connections of the sigmoid are driven by the slow motion of sunspot rotation, which transforms to a highly twisted flux rope structure in a dynamical scenario. Exceeding the critical twist in the flux rope probably leads to the loss of equilibrium, thus triggering the onset of the two eruptions.

Tracking the Magnetic Flux in and Around Sunspots

Energy Technology Data Exchange (ETDEWEB)

Sheeley, N. R. Jr.; Stauffer, J. R.; Thomassie, J. C.; Warren, H. P., E-mail: solsheeley@verizon.net, E-mail: harry.warren@nrl.navy.mil [Space Science Division, Naval Research Laboratory, Washington, DC 20375-5352 (United States)

2017-02-10

We have developed a procedure for tracking sunspots observed by the Helioseismic and Magnetic Imager on the Solar Dynamics Observatory and for making curvature-corrected space/time maps of the associated line-of-sight magnetic field and continuum intensity. We apply this procedure to 36 sunspots, each observed continuously for nine days around its central meridian passage time, and find that the proper motions separate into two distinct components depending on their speeds. Fast (∼3–5 km s{sup −1}) motions, comparable to Evershed flows, are produced by weak vertical fluctuations of the horizontal canopy field and recur on a timescale of 12–20 min. Slow (∼0.3–0.5 km s{sup −1}) motions diverge from a sunspot-centered ring whose location depends on the size of the sunspot, occurring in the mid-penumbra for large sunspots and at the outer edge of the penumbra for small sunspots. The slow ingoing features are contracting spokes of a quasi-vertical field of umbral polarity. These inflows disappear when the sunspot loses its penumbra, and may be related to inward-moving penumbral grain. The slow outgoing features may have either polarity depending on whether they originate from quasi-vertical fields of umbral polarity or from the outer edge of the canopy. When a sunspot decays, the penumbra and canopy disappear, and the moat becomes filled with slow outflows of umbral polarity. We apply our procedure to decaying sunspots, to long-lived sunspots, and to numerical simulations of a long-lived sunspot by Rempel.

The sunspot databases of the Debrecen Observatory

Science.gov (United States)

Baranyi, Tünde; Gyori, Lajos; Ludmány, András

2015-08-01

We present the sunspot data bases and online tools available in the Debrecen Heliophysical Observatory: the DPD (Debrecen Photoheliographic Data, 1974 -), the SDD (SOHO/MDI-Debrecen Data, 1996-2010), the HMIDD (SDO/HMI-Debrecen Data, HMIDD, 2010-), the revised version of Greenwich Photoheliographic Data (GPR, 1874-1976) presented together with the Hungarian Historical Solar Drawings (HHSD, 1872-1919). These are the most detailed and reliable documentations of the sunspot activity in the relevant time intervals. They are very useful for studying sunspot group evolution on various time scales from hours to weeks. Time-dependent differences between the available long-term sunspot databases are investigated and cross-calibration factors are determined between them. This work has received funding from the European Community's Seventh Framework Programme (FP7/2012-2015) under grant agreement No. 284461 (eHEROES).

Self-organising mixture autoregressive model for non-stationary time series modelling.

Science.gov (United States)

Ni, He; Yin, Hujun

2008-12-01

Modelling non-stationary time series has been a difficult task for both parametric and nonparametric methods. One promising solution is to combine the flexibility of nonparametric models with the simplicity of parametric models. In this paper, the self-organising mixture autoregressive (SOMAR) network is adopted as a such mixture model. It breaks time series into underlying segments and at the same time fits local linear regressive models to the clusters of segments. In such a way, a global non-stationary time series is represented by a dynamic set of local linear regressive models. Neural gas is used for a more flexible structure of the mixture model. Furthermore, a new similarity measure has been introduced in the self-organising network to better quantify the similarity of time series segments. The network can be used naturally in modelling and forecasting non-stationary time series. Experiments on artificial, benchmark time series (e.g. Mackey-Glass) and real-world data (e.g. numbers of sunspots and Forex rates) are presented and the results show that the proposed SOMAR network is effective and superior to other similar approaches.

Evaluation of nonlinearity and validity of nonlinear modeling for complex time series.

Science.gov (United States)

Suzuki, Tomoya; Ikeguchi, Tohru; Suzuki, Masuo

2007-10-01

Even if an original time series exhibits nonlinearity, it is not always effective to approximate the time series by a nonlinear model because such nonlinear models have high complexity from the viewpoint of information criteria. Therefore, we propose two measures to evaluate both the nonlinearity of a time series and validity of nonlinear modeling applied to it by nonlinear predictability and information criteria. Through numerical simulations, we confirm that the proposed measures effectively detect the nonlinearity of an observed time series and evaluate the validity of the nonlinear model. The measures are also robust against observational noises. We also analyze some real time series: the difference of the number of chickenpox and measles patients, the number of sunspots, five Japanese vowels, and the chaotic laser. We can confirm that the nonlinear model is effective for the Japanese vowel /a/, the difference of the number of measles patients, and the chaotic laser.

COMPARISON OF CHAOTIC AND FRACTAL PROPERTIES OF POLAR FACULAE WITH SUNSPOT ACTIVITY

Energy Technology Data Exchange (ETDEWEB)

Deng, L. H.; Xiang, Y. Y.; Dun, G. T. [Yunnan Observatories, Chinese Academy of Sciences, Kunming 650216 (China); Li, B., E-mail: wooden@escience.cn [Shandong Provincial Key Laboratory of Optical Astronomy and Solar-Terrestrial Environment, School of Space Science and Physics, Shandong University at Weihai, Weihai 264209 (China)

2016-01-15

The solar magnetic activity is governed by a complex dynamo mechanism and exhibits a nonlinear dissipation behavior in nature. The chaotic and fractal properties of solar time series are of great importance to understanding the solar dynamo actions, especially with regard to the nonlinear dynamo theories. In the present work, several nonlinear analysis approaches are proposed to investigate the nonlinear dynamical behavior of the polar faculae and sunspot activity for the time interval from 1951 August to 1998 December. The following prominent results are found: (1) both the high- and the low-latitude solar activity are governed by a three-dimensional chaotic attractor, and the chaotic behavior of polar faculae is the most complex, followed by that of the sunspot areas, and then the sunspot numbers; (2) both the high- and low-latitude solar activity exhibit a high degree of persistent behavior, and their fractal nature is due to such long-range correlation; (3) the solar magnetic activity cycle is predictable in nature, but the high-accuracy prediction should only be done for short- to mid-term due to its intrinsically dynamical complexity. With the help of the Babcock–Leighton dynamo model, we suggest that the nonlinear coupling of the polar magnetic fields with strong active-region fields exhibits a complex manner, causing the statistical similarities and differences between the polar faculae and the sunspot-related indicators.

A Standard Law for the Equatorward Drift of the Sunspot Zones

Science.gov (United States)

Hathaway, David H.

2012-01-01

The latitudinal location of the sunspot zones in each hemisphere is determined by calculating the centroid position of sunspot areas for each solar rotation from May 1874 to June 2012. When these centroid positions are plotted and analyzed as functions of time from each sunspot cycle maximum there appears to be systematic differences in the positions and equatorward drift rates as a function of sunspot cycle amplitude. If, instead, these centroid positions are plotted and analyzed as functions of time from each sunspot cycle minimum then most of the differences in the positions and equatorward drift rates disappear. The differences that remain disappear entirely if curve fitting is used to determine the starting times (which vary by as much as 8 months from the times of minima). The sunspot zone latitudes and equatorward drift measured relative to this starting time follow a standard path for all cycles with no dependence upon cycle strength or hemispheric dominance. Although Cycle 23 was peculiar in its length and the strength of the polar fields it produced, it too shows no significant variation from this standard. This standard law, and the lack of variation with sunspot cycle characteristics, is consistent with Dynamo Wave mechanisms but not consistent with current Flux Transport Dynamo models for the equatorward drift of the sunspot zones.

DYNAMICS IN SUNSPOT UMBRA AS SEEN IN NEW SOLAR TELESCOPE AND INTERFACE REGION IMAGING SPECTROGRAPH DATA

Energy Technology Data Exchange (ETDEWEB)

Yurchyshyn, V.; Abramenko, V. [Big Bear Solar Observatory, New Jersey Institute of Technology, Big Bear City, CA 92314 (United States); Kilcik, A. [Department of Space Science and Technologies, Akdeniz University, 07058 Antalya (Turkey)

2015-01-10

We analyze sunspot oscillations using Interface Region Imaging Spectrograph (IRIS) slit-jaw and spectral data and narrow-band chromospheric images from the New Solar Telescope (NST) for the main sunspot in NOAA AR 11836. We report that the difference between the shock arrival times as measured by the Mg II k 2796.35 Å and Si IV 1393.76 Å line formation levels changes during the observed period, and peak-to-peak delays may range from 40 s to zero. The intensity of chromospheric shocks also displays long-term (about 20 min) variations. NST's high spatial resolution Hα data allowed us to conclude that, in this sunspot, umbral flashes (UFs) appeared in the form of narrow bright lanes stretched along the light bridges and around clusters of umbral bright points. The time series also suggested that UFs preferred to appear on the sunspot-center side of light bridges, which may indicate the existence of a compact sub-photospheric driver of sunspot oscillations. The sunspot's umbra as seen in the IRIS chromospheric and transition region data appears bright above the locations of light bridges and the areas where the dark umbra is dotted with clusters of umbral dots. Co-spatial and co-temporal data from the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory showed that the same locations were associated with bright footpoints of coronal loops suggesting that the light bridges may play an important role in heating the coronal sunspot loops. Finally, the power spectra analysis showed that the intensity of chromospheric and transition region oscillations significantly vary across the umbra and with height, suggesting that umbral non-uniformities and the structure of sunspot magnetic fields may play a role in wave propagation and heating of umbral loops.

Sunspot Positions and Areas from Observations by Galileo Galilei

Science.gov (United States)

Vokhmyanin, M. V.; Zolotova, N. V.

2018-02-01

Sunspot records in the seventeenth century provide important information on the solar activity before the Maunder minimum, yielding reliable sunspot indices and the solar butterfly diagram. Galilei's letters to Cardinal Francesco Barberini and Marcus Welser contain daily solar observations on 3 - 11 May, 2 June - 8 July, and 19 - 21 August 1612. These historical archives do not provide the time of observation, which results in uncertainty in the sunspot coordinates. To obtain them, we present a method that minimizes the discrepancy between the sunspot latitudes. We provide areas and heliographic coordinates of 82 sunspot groups. In contrast to Sheiner's butterfly diagram, we found only one sunspot group near the Equator. This provides a higher reliability of Galilei's drawings. Large sunspot groups are found to emerge at the same longitude in the northern hemisphere from 3 May to 21 August, which indicates an active longitude.

The visibility function and its effect on the observed characteristics of sunspot groups. 1

International Nuclear Information System (INIS)

Kopecky, M.; Kuklin, G.V.; Starkova, I.P.

1985-01-01

The paper is an introductory study to a series dealing with the visibility function, the function of foreshortening of sunspot group areas, and with the effect of these functions on the results of the statistical processing of observations, which has to be taken into account in interpreting the results. A ''diagram of observational conditions'' is described, which enables a number of statistical problems of sunspot groups on the rotating Sun to be solved by computer modelling or by graphical methods. Examples are given of the use of this diagram in studying the distribution of the observed lifetime of sunspot groups with a given actual lifetime, of the decrease in the number of sunspot groups towards the limb of the solar disc, of the east-west asymmetry of sunspot group appearance and disappearance. (author)

SEISMIC DISCRIMINATION OF THERMAL AND MAGNETIC ANOMALIES IN SUNSPOT UMBRAE

International Nuclear Information System (INIS)

Lindsey, C.; Cally, P. S.; Rempel, M.

2010-01-01

Efforts to model sunspots based on helioseismic signatures need to discriminate between the effects of (1) a strong magnetic field that introduces time-irreversible, vantage-dependent phase shifts, apparently connected to fast- and slow-mode coupling and wave absorption and (2) a thermal anomaly that includes cool gas extending an indefinite depth beneath the photosphere. Helioseismic observations of sunspots show travel times considerably reduced with respect to equivalent quiet-Sun signatures. Simulations by Moradi and Cally of waves skipping across sunspots with photospheric magnetic fields of order 3 kG show travel times that respond strongly to the magnetic field and relatively weakly to the thermal anomaly by itself. We note that waves propagating vertically in a vertical magnetic field are relatively insensitive to the magnetic field, while remaining highly responsive to the attendant thermal anomaly. Travel-time measurements for waves with large skip distances into the centers of axially symmetric sunspots are therefore a crucial resource for discrimination of the thermal anomaly beneath sunspot umbrae from the magnetic anomaly. One-dimensional models of sunspot umbrae based on compressible-radiative-magnetic-convective simulations such as by Rempel et al. can be fashioned to fit observed helioseismic travel-time spectra in the centers of sunspot umbrae. These models are based on cooling of the upper 2-4 Mm of the umbral subphotosphere with no significant anomaly beneath 4.5 Mm. The travel-time reductions characteristic of these models are primarily a consequence of a Wilson depression resulting from a strong downward buoyancy of the cooled umbral medium.

Long-term periodicities in the sunspot record

International Nuclear Information System (INIS)

Wilson, R.M.

1984-07-01

Sunspot records are systematically maintained, with the knowledge that an 11 year average period exists since about 1850. Thus, the sunspot record of highest quality and considered to be the most reliable is that of cycle eight through the present. On the basis of cycles 8 through 20, various combinations of sine curves were used to approximate the observed R sub MAX values (where R sub MAX is the smoothed sunspot number at cycle maximum). It is found that a three component sinusoidal function, having an 11 cycle and a 2 cycle variation on a 90 cycle periodicity, yields computed R sub MAX values which fit, reasonably well, observed R sub MAX values for the modern sunspot cycles. Extrapolation of the empirical functions forward in time allows for the projection of values of R sub MAX for cycles 21 and 22. For cycle 21, the function projects a value of 157.3, very close to the actually observed value of 164.5. For cycle 22, the function projects a value of about 107. Linear regressions applied to cycle 22 indicate a long-period cycle (cycle duration 132 months). An extensive bibliography on techniques used to estimate the time dependent behavior of sunspot cycles is provided

Sunspot activity and influenza pandemics: a statistical assessment of the purported association.

Science.gov (United States)

Towers, S

2017-10-01

Since 1978, a series of papers in the literature have claimed to find a significant association between sunspot activity and the timing of influenza pandemics. This paper examines these analyses, and attempts to recreate the three most recent statistical analyses by Ertel (1994), Tapping et al. (2001), and Yeung (2006), which all have purported to find a significant relationship between sunspot numbers and pandemic influenza. As will be discussed, each analysis had errors in the data. In addition, in each analysis arbitrary selections or assumptions were also made, and the authors did not assess the robustness of their analyses to changes in those arbitrary assumptions. Varying the arbitrary assumptions to other, equally valid, assumptions negates the claims of significance. Indeed, an arbitrary selection made in one of the analyses appears to have resulted in almost maximal apparent significance; changing it only slightly yields a null result. This analysis applies statistically rigorous methodology to examine the purported sunspot/pandemic link, using more statistically powerful un-binned analysis methods, rather than relying on arbitrarily binned data. The analyses are repeated using both the Wolf and Group sunspot numbers. In all cases, no statistically significant evidence of any association was found. However, while the focus in this particular analysis was on the purported relationship of influenza pandemics to sunspot activity, the faults found in the past analyses are common pitfalls; inattention to analysis reproducibility and robustness assessment are common problems in the sciences, that are unfortunately not noted often enough in review.

Period and phase comparisons of near-decadal oscillations in solar, geomagnetic, and cosmic ray time series

Science.gov (United States)

Juckett, David A.

2001-09-01

A more complete understanding of the periodic dynamics of the Sun requires continued exploration of non-11-year oscillations in addition to the benchmark 11-year sunspot cycle. In this regard, several solar, geomagnetic, and cosmic ray time series were examined to identify common spectral components and their relative phase relationships. Several non-11-year oscillations were identified within the near-decadal range with periods of ~8, 10, 12, 15, 18, 22, and 29 years. To test whether these frequency components were simply low-level noise or were related to a common source, the phases were extracted for each component in each series. The phases were nearly identical across the solar and geomagnetic series, while the corresponding components in four cosmic ray surrogate series exhibited inverted phases, similar to the known phase relationship with the 11-year sunspot cycle. Cluster analysis revealed that this pattern was unlikely to occur by chance. It was concluded that many non-11-year oscillations truly exist in the solar dynamical environment and that these contribute to the complex variations observed in geomagnetic and cosmic ray time series. Using the different energy sensitivities of the four cosmic ray surrogate series, a preliminary indication of the relative intensities of the various solar-induced oscillations was observed. It provides evidence that many of the non-11-year oscillations result from weak interplanetary magnetic field/solar wind oscillations that originate from corresponding variations in the open-field regions of the Sun.

Planetary tides during the Maunder sunspot minimum

International Nuclear Information System (INIS)

Smythe, C.M.; Eddy, J.A.

1977-01-01

Sun-centered planetary conjunctions and tidal potentials are here constructed for the AD1645 to 1715 period of sunspot absence, referred to as the 'Maunder Minimum'. These are found to be effectively indistinguishable from patterns of conjunctions and power spectra of tidal potential in the present era of a well established 11 year sunspot cycle. This places a new and difficult restraint on any tidal theory of sunspot formation. Problems arise in any direct gravitational theory due to the apparently insufficient forces and tidal heights involved. Proponents of the tidal hypothesis usually revert to trigger mechanisms, which are difficult to criticise or test by observation. Any tidal theory rests on the evidence of continued sunspot periodicity and the substantiation of a prolonged period of solar anomaly in the historical past. The 'Maunder Minimum' was the most drastic change in the behaviour of solar activity in the last 300 years; sunspots virtually disappeared for a 70 year period and the 11 year cycle was probably absent. During that time, however, the nine planets were all in their orbits, and planetary conjunctions and tidal potentials were indistinguishable from those of the present era, in which the 11 year cycle is well established. This provides good evidence against the tidal theory. The pattern of planetary tidal forces during the Maunder Minimum was reconstructed to investigate the possibility that the multiple planet forces somehow fortuitously cancelled at the time, that is that the positions of the slower moving planets in the 17th and early 18th centuries were such that conjunctions and tidal potentials were at the time reduced in number and force. There was no striking dissimilarity between the time of the Maunder Minimum and any period investigated. The failure of planetary conjunction patterns to reflect the drastic drop in sunspots during the Maunder Minimum casts doubt on the tidal theory of solar activity, but a more quantitative test

Coordination failure caused by sunspots

DEFF Research Database (Denmark)

Beugnot, Julie; Gürgüç, Zeynep; Øvlisen, Frederik Roose

2012-01-01

on the efficient equilibrium, we consider sunspots as a potential reason for coordination failure. We conduct an experiment with a three player 2x2x2 game in which coordination on the efficient equilibrium is easy and should normally occur. In the control session, we find almost perfect coordination on the payoff......-dominant equilibrium, but in the sunspot treatment, dis-coordination is frequent. Sunspots lead to significant inefficiency, and we conclude that sunspots can indeed cause coordination failure....

INTERFERENCE FRINGES OF SOLAR ACOUSTIC WAVES AROUND SUNSPOTS

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Chou, Dean-Yi; Zhao Hui; Yang, Ming-Hsu; Liang, Zhi-Chao, E-mail: chou@phys.nthu.edu.tw [Physics Department, National Tsing Hua University, Hsinchu, Taiwan (China)

2012-10-20

Solar acoustic waves are scattered by a sunspot due to the interaction between the acoustic waves and the sunspot. The sunspot, excited by the incident wave, generates the scattered wave. The scattered wave is added to the incident wave to form the total wave around the sunspot. The interference fringes between the scattered wave and the incident wave are visible in the intensity of the total wave because the coherent time of the incident wave is of the order of a wave period. The strength of the interference fringes anti-correlates with the width of temporal spectra of the incident wave. The separation between neighboring fringes increases with the incident wavelength and the sunspot size. The strength of the fringes increases with the radial order n of the incident wave from n = 0 to n = 2, and then decreases from n = 2 to n = 5. The interference fringes play a role analogous to holograms in optics. This study suggests the feasibility of using the interference fringes to reconstruct the scattered wavefields of the sunspot, although the quality of the reconstructed wavefields is sensitive to the noise and errors in the interference fringes.

STOCHASTIC DESCRIPTION OF THE HIGH-FREQUENCY CONTENT OF DAILY SUNSPOTS AND EVIDENCE FOR REGIME CHANGES

International Nuclear Information System (INIS)

Shapoval, A.; Le Mouël, J.-L.; Courtillot, V.; Shnirman, M.

2015-01-01

The irregularity index λ is applied to the high-frequency content of daily sunspot numbers ISSN. This λ is a modification of the standard maximal Lyapunov exponent. It is computed here as a function of embedding dimension m, within four-year time windows centered at the maxima of Schwabe cycles. The λ(m) curves form separate clusters (pre-1923 and post-1933). This supports a regime transition and narrows its occurrence to cycle 16, preceding the growth of activity leading to the Modern Maximum. The two regimes are reproduced by a simple autoregressive process AR(1), with the mean of Poisson noise undergoing 11 yr modulation. The autocorrelation a of the process (linked to sunspot lifetime) is a ≈ 0.8 for 1850-1923 and ≈0.95 for 1933-2013. The AR(1) model suggests that groups of spots appear with a Poisson rate and disappear at a constant rate. We further applied the irregularity index to the daily sunspot group number series for the northern and southern hemispheres, provided by the Greenwich Royal Observatory (RGO), in order to study a possible desynchronization. Correlations between the north and south λ(m) curves vary quite strongly with time and indeed show desynchronization. This may reflect a slow change in the dimension of an underlying dynamical system. The ISSN and RGO series of group numbers do not imply an identical mechanism, but both uncover a regime change at a similar time. Computation of the irregularity index near the maximum of cycle 24 will help in checking whether yet another regime change is under way

Nature's third cycle a story of sunspots

CERN Document Server

Choudhuri, Arnab Rai

2015-01-01

The cycle of day and night and the cycle of seasons are two familiar natural cycles around which many human activities are organized. But is there a third natural cycle of importance for us humans? On 13 March 1989, six million people in Canada went without electricity for many hours: a large explosion on the sun was discovered as the cause of this blackout. Such explosions occur above sunspots, dark features on the surface of the Sun that have been observed through telescopes since the time of Galileo. The number of sunspots has been found to wax and wane over a period of 11 years. Although this cycle was discovered less than two centuries ago, it is becoming increasingly important for us as human society becomes more dependent on technology. For nearly a century after its discovery, the cause of the sunspot cycle remained completely shrouded in mystery. The 1908 discovery of strong magnetic fields in sunspots made it clear that the 11-year cycle is the magnetic cycle of the sun. It is only during the last ...

Diode laser heterodyne observations of silicon monoxide in sunspots - A test of three sunspot models

Science.gov (United States)

Glenar, D. A.; Deming, D.; Jennings, D. E.; Kostiuk, T.; Mumma, M. J.

1983-01-01

Absorption features from the 8 micron SiO fundamental (upsilon = 1-0) and hot bands (upsilon = 2-1) have been observed in sunspots at sub-Doppler resolution using a ground-based tunable diode laser heterodyne spectrometer. The observed line widths suggest an upper limit of 0.5 km/s for the microturbulent velocity in sunspot umbrae. Since the silicon monoxide abundance is very sensitive to sunspot temperature, the measured equivalent widths permit an unambiguous determination of the temperature-pressure relation in the upper layers of the umbral atmosphere. In the region of SiO line formation (log P sub g = 3.0-4.5), the results support the sunspot model suggested by Stellmacher and Wiehr (1970).

Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting.

Science.gov (United States)

Waheeb, Waddah; Ghazali, Rozaida; Herawan, Tutut

2016-01-01

Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN) and the Dynamic Ridge Polynomial Neural Network (DRPNN). Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE) with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network.

Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting.

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Waddah Waheeb

Full Text Available Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN and the Dynamic Ridge Polynomial Neural Network (DRPNN. Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network.

Worldwide variation in atmospheric noise intensities with sunspot number: an in-depth look at the 20 to 24 hour seasonal time block

International Nuclear Information System (INIS)

Joglekar, P.J.; Sathiamurthy, T.S.

1975-01-01

Comparisons of the variation of atmospheric radio noise intensities for 20 to 24 hr to sunspot numbers have been completed. Statistical dependence between the noise intensities and sunspot numbers was found for different seasons at a number of frequencies for many locations in the global network of ARN-2 noise recorders. The noise intensities generally tended to decrease with sunspot number in the range from 50 kHz to 5 MHz, which is presumed to be due to increases in residual ionospheric absorption during nighttime. At frequencies greater than 5 MHz, noise intensities increased with sunspot number in many cases, which would be expected from our present knowledge of ionospheric behavior in the HF range. By convention, CCIR treats year-to-year variation in the noise intensities as random and includes them in the prediction uncertainty sigma /sub Fam/ (for which one value is given at a frequency for a seasonal time block for all locations) in system performance evaluation. An error analysis on a global basis shows that a large portion of the year-to-year variability is due to sunspot variation. This suggests the possibility of improved noise estimates. (auth)

TIME DISTRIBUTIONS OF LARGE AND SMALL SUNSPOT GROUPS OVER FOUR SOLAR CYCLES

International Nuclear Information System (INIS)

Kilcik, A.; Yurchyshyn, V. B.; Abramenko, V.; Goode, P. R.; Cao, W.; Ozguc, A.; Rozelot, J. P.

2011-01-01

Here we analyze solar activity by focusing on time variations of the number of sunspot groups (SGs) as a function of their modified Zurich class. We analyzed data for solar cycles 20-23 by using Rome (cycles 20 and 21) and Learmonth Solar Observatory (cycles 22 and 23) SG numbers. All SGs recorded during these time intervals were separated into two groups. The first group includes small SGs (A, B, C, H, and J classes by Zurich classification), and the second group consists of large SGs (D, E, F, and G classes). We then calculated small and large SG numbers from their daily mean numbers as observed on the solar disk during a given month. We report that the time variations of small and large SG numbers are asymmetric except for solar cycle 22. In general, large SG numbers appear to reach their maximum in the middle of the solar cycle (phases 0.45-0.5), while the international sunspot numbers and the small SG numbers generally peak much earlier (solar cycle phases 0.29-0.35). Moreover, the 10.7 cm solar radio flux, the facular area, and the maximum coronal mass ejection speed show better agreement with the large SG numbers than they do with the small SG numbers. Our results suggest that the large SG numbers are more likely to shed light on solar activity and its geophysical implications. Our findings may also influence our understanding of long-term variations of the total solar irradiance, which is thought to be an important factor in the Sun-Earth climate relationship.

Is sunspot activity a factor in influenza pandemics?

Science.gov (United States)

Qu, Jiangwen

2016-09-01

The 2009 AH1N1 pandemic became a global health concern, although fortunately, its worst anticipated effects were not realised. While the origins of such outbreaks remain poorly understood, it is very important to identify the precipitating factors in their emergence so that future pandemics can be detected as quickly as possible. Methords: Descriptive epidemiology was used to analyse the association between influenza pandemics and possible pandemics and relative number of sunspots. Non-conditional logistic regression was performed to analyse the statistical association between sunspot extremes and influenza pandemics to within plus or minus 1 year. Almost all recorded influenza/possible pandemics have occurred in time frames corresponding to sunspot extremes, or +/- 1 year within such extremes. These periods were identified as important risk factors in both possible and confirmed influenza pandemics (odds ratio: 3.87; 95% confidence interval: 1.08 to 13.85). Extremes of sunspot activity to within plus or minus 1 year may precipitate influenza pandemics. Mechanisms of epidemic initiation and early spread are discussed including primary causation by externally derived viral variants (from space via cometary dust). Efforts to construct a comprehensive early warning system for potential influenza and other viral pandemics that include analysis of sunspot activity and stratospheric sampling for viral variants should be supported. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Predicting physical time series using dynamic ridge polynomial neural networks.

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Dhiya Al-Jumeily

Full Text Available Forecasting naturally occurring phenomena is a common problem in many domains of science, and this has been addressed and investigated by many scientists. The importance of time series prediction stems from the fact that it has wide range of applications, including control systems, engineering processes, environmental systems and economics. From the knowledge of some aspects of the previous behaviour of the system, the aim of the prediction process is to determine or predict its future behaviour. In this paper, we consider a novel application of a higher order polynomial neural network architecture called Dynamic Ridge Polynomial Neural Network that combines the properties of higher order and recurrent neural networks for the prediction of physical time series. In this study, four types of signals have been used, which are; The Lorenz attractor, mean value of the AE index, sunspot number, and heat wave temperature. The simulation results showed good improvements in terms of the signal to noise ratio in comparison to a number of higher order and feedforward neural networks in comparison to the benchmarked techniques.

Featured Image: Bright Dots in a Sunspot

Science.gov (United States)

Kohler, Susanna

2018-03-01

This image of a sunspot, located in in NOAA AR 12227, was captured in December 2014 by the 0.5-meter Solar Optical Telescope on board the Hinode spacecraft. This image was processed by a team of scientists led by Rahul Yadav (Udaipur Solar Observatory, Physical Research Laboratory Dewali, India) in order to examine the properties of umbral dots: transient, bright features observed in the umbral region (the central, darkest part) of a sunspot. By exploring these dots, Yadav and collaborators learned how their properties relate to the large-scale properties of the sunspots in which they form for instance, how do the number, intensities, or filling factors of dots relate to the size of a sunspots umbra? To find out more about the authors results, check out the article below.Sunspot in NOAA AR 11921. Left: umbralpenumbral boundary. Center: the isolated umbra from the sunspot. Right: The umbra with locations of umbral dots indicated by yellow plus signs. [Adapted from Yadav et al. 2018]CitationRahul Yadav et al 2018 ApJ 855 8. doi:10.3847/1538-4357/aaaeba

A New Revision of the Solar Irradiance Climate Data Record Incorporates Recent Research into Proxies of Sunspot Darkening and the Sunspot Number Record

Science.gov (United States)

Coddington, O.; Lean, J.; Pilewskie, P.; Baranyi, T.; Snow, M. A.; Kopp, G.; Richard, E. C.; Lindholm, C.

2017-12-01

An operational climate data record (CDR) of total and spectral solar irradiance became available in November 2015 as part of the National Oceanographic and Atmospheric Administration's National Centers for Environmental Information Climate Data Record Program. The data record, which is updated quarterly, is available from 1610 to the present as yearly-average values and from 1882 to the present as monthly- and daily-averages, with associated time and wavelength-dependent uncertainties. It was developed jointly by the University of Colorado at Boulder's Laboratory for Atmospheric and Space Physics and the Naval Research Laboratory, and, together with the source code and supporting documentation, is available at https://www.ncdc.noaa.gov/cdr/. In the Solar Irradiance CDR, total solar irradiance (TSI) and solar spectral irradiance (SSI) are estimated from models that determine the changes from quiet Sun conditions arising from bright faculae and dark sunspots on the solar disk. The models are constructed using linear regression of proxies of solar sunspot and facular features with the approximately decade-long irradiance observations from the SOlar Radiation and Climate Experiment. A new revision of this data record was recently released in an ongoing effort to reduce solar irradiance uncertainties in two ways. First, the sunspot darkening proxy was revised using a new cross calibration of the current sunspot region observations made by the Solar Observing Optical Network with the historical records of the Royal Greenwich Observatory. This implementation affects modeled irradiances from 1882 - 1978. Second, the impact of a revised record of sunspot number by the Sunspot Index and Long-term Solar Observations center on modeled irradiances was assessed. This implementation provides two different reconstructions of historical, yearly-averaged irradiances from 1610-1881. Additionally, we show new, preliminary results that demonstrate improvements in modeled TSI by using

Identification of possible intense historical geomagnetic storms using combined sunspot and auroral observations from East Asia

Directory of Open Access Journals (Sweden)

D. M. Willis

2005-03-01

Full Text Available Comprehensive catalogues of ancient sunspot and auroral observations from East Asia are used to identify possible intense historical geomagnetic storms in the interval 210 BC-AD 1918. There are about 270 entries in the sunspot catalogue and about 1150 entries in the auroral catalogue. Special databases have been constructed in which the scientific information in these two catalogues is placed in specified fields. For the purposes of this study, an historical geomagnetic storm is defined in terms of an auroral observation that is apparently associated with a particular sunspot observation, in the sense that the auroral observation occurred within several days of the sunspot observation. More precisely, a selection criterion is formulated for the automatic identification of such geomagnetic storms, using the oriental records stored in the sunspot and auroral databases. The selection criterion is based on specific assumptions about the duration of sunspot visibility with the unaided eye, the likely range of heliographic longitudes of an energetic solar feature, and the likely range of transit times for ejected solar plasma to travel from the Sun to the Earth. This selection criterion results in the identification of nineteen putative historical geomagnetic storms, although two of these storms are spurious in the sense that there are two examples of a single sunspot observation being associated with two different auroral observations separated by more than half a (synodic solar rotation period. The literary and scientific reliabilities of the East Asian sunspot and auroral records that define the nineteen historical geomagnetic storms are discussed in detail in a set of appendices. A possible time sequence of events is presented for each geomagnetic storm, including possible dates for both the central meridian passage of the sunspot and the occurrence of the energetic solar feature, as well as likely transit times for the ejected solar plasma

Observational Evidence of a Flux Rope within a Sunspot Umbra

Energy Technology Data Exchange (ETDEWEB)

Guglielmino, Salvo L.; Zuccarello, Francesca [Dipartimento di Fisica e Astronomia—Sezione Astrofisica, Università di Catania, Via S. Sofia 78, I-95125 Catania (Italy); Romano, Paolo, E-mail: salvo.guglielmino@oact.inaf.it [INAF—Osservatorio Astrofisico di Catania, Via S. Sofia 78, I-95125 Catania (Italy)

2017-09-10

We observed an elongated filamentary bright structure inside the umbra of the big sunspot in active region NOAA 12529, which differs from the light bridges usually observed in sunspots for its morphology, magnetic configuration, and velocity field. We used observations taken with the Solar Dynamic Observatory satellite to characterize this feature. Its lifetime is 5 days, during which it reaches a maximum length of about 30″. In the maps of the vertical component of the photospheric magnetic field, a portion of the feature has a polarity opposite to that of the hosting sunspot. At the same time, in the entire feature the horizontal component of the magnetic field is about 2000 G, substantially stronger than in the surrounding penumbral filaments. Doppler velocity maps reveal the presence of both upward and downward plasma motions along the structure at the photospheric level. Moreover, looking at the chromospheric level, we noted that it is located in a region corresponding to the edge of a small filament that seems rooted in the sunspot umbra. Therefore, we interpreted the bright structure as the photospheric counterpart of a flux rope touching the sunspot and giving rise to penumbral-like filaments in the umbra.

Sunspot drawings handwritten character recognition method based on deep learning

Science.gov (United States)

Zheng, Sheng; Zeng, Xiangyun; Lin, Ganghua; Zhao, Cui; Feng, Yongli; Tao, Jinping; Zhu, Daoyuan; Xiong, Li

2016-05-01

High accuracy scanned sunspot drawings handwritten characters recognition is an issue of critical importance to analyze sunspots movement and store them in the database. This paper presents a robust deep learning method for scanned sunspot drawings handwritten characters recognition. The convolution neural network (CNN) is one algorithm of deep learning which is truly successful in training of multi-layer network structure. CNN is used to train recognition model of handwritten character images which are extracted from the original sunspot drawings. We demonstrate the advantages of the proposed method on sunspot drawings provided by Chinese Academy Yunnan Observatory and obtain the daily full-disc sunspot numbers and sunspot areas from the sunspot drawings. The experimental results show that the proposed method achieves a high recognition accurate rate.

Towards the automatic detection and analysis of sunspot rotation

Science.gov (United States)

Brown, Daniel S.; Walker, Andrew P.

2016-10-01

Torsional rotation of sunspots have been noted by many authors over the past century. Sunspots have been observed to rotate up to the order of 200 degrees over 8-10 days, and these have often been linked with eruptive behaviour such as solar flares and coronal mass ejections. However, most studies in the literature are case studies or small-number studies which suffer from selection bias. In order to better understand sunspot rotation and its impact on the corona, unbiased large-sample statistical studies are required (including both rotating and non-rotating sunspots). While this can be done manually, a better approach is to automate the detection and analysis of rotating sunspots using robust methods with well characterised uncertainties. The SDO/HMI instrument provide long-duration, high-resolution and high-cadence continuum observations suitable for extracting a large number of examples of rotating sunspots. This presentation will outline the analysis of SDI/HMI data to determine the rotation (and non-rotation) profiles of sunspots for the complete duration of their transit across the solar disk, along with how this can be extended to automatically identify sunspots and initiate their analysis.

Aurorae, sunspots and weather, mainly since A.D. 1200

International Nuclear Information System (INIS)

Schove, D.J.

1981-01-01

Auroral records recieved for the Spectrum of Time project were used in 1955 to estimate sunspot activity and the dates maxima and minima back to 649 B.C. An additional set of rules has been developed and has made possible further improvements utilizing the separate auroral maxima associated with flares and coronal holes on the sun. A further set can now be given. 1) The time between sunspot maxima depends especially on the ratio of the amplitudes: the time between minima is high if the next cycle is very weak and low when the two consecutive cycles are both strong. 2) The time of rise is usually dependent on the strength of the next maxima, and the time of fall is low when a moderate cycle is followed by a strong one. (orig./WL)

Vertical gradients of sunspot magnetic fields

Science.gov (United States)

Hagyard, M. J.; Teuber, D.; West, E. A.; Tandberg-Hanssen, E.; Henze, W., Jr.; Beckers, J. M.; Bruner, M.; Hyder, C. L.; Woodgate, B. E.

1983-01-01

The results of a Solar Maximum Mission (SMM) guest investigation to determine the vertical gradients of sunspot magnetic fields for the first time from coordinated observations of photospheric and transition-region fields are described. Descriptions are given of both the photospheric vector field of a sunspot, derived from observations using the NASA Marshall Space Flight Center vector magnetograph, and of the line-of-sight component in the transition region, obtained from the SMM Ultraviolet Spectrometer and Polarimeter instrument. On the basis of these data, vertical gradients of the line-of-sight magnetic field component are calculated using three methods. It is found that the vertical gradient of Bz is lower than values from previous studies and that the transition-region field occurs at a height of approximately 4000-6000 km above the photosphere.

Oscillations and Waves in Sunspots

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Elena Khomenko

2015-11-01

Full Text Available A magnetic field modifies the properties of waves in a complex way. Significant advances have been made recently in our understanding of the physics of sunspot waves with the help of high-resolution observations, analytical theories, as well as numerical simulations. We review the current ideas in the field, providing the most coherent picture of sunspot oscillations as by present understanding.

New reconstruction of the sunspot group numbers since 1739 using direct calibration and "backbone" methods

Science.gov (United States)

Chatzistergos, Theodosios; Usoskin, Ilya G.; Kovaltsov, Gennady A.; Krivova, Natalie A.; Solanki, Sami K.

2017-06-01

Context. The group sunspot number (GSN) series constitute the longest instrumental astronomical database providing information on solar activity. This database is a compilation of observations by many individual observers, and their inter-calibration has usually been performed using linear rescaling. There are multiple published series that show different long-term trends for solar activity. Aims: We aim at producing a GSN series, with a non-linear non-parametric calibration. The only underlying assumptions are that the differences between the various series are due to different acuity thresholds of the observers, and that the threshold of each observer remains constant throughout the observing period. Methods: We used a daisy chain process with backbone (BB) observers and calibrated all overlapping observers to them. We performed the calibration of each individual observer with a probability distribution function (PDF) matrix constructed considering all daily values for the overlapping period with the BB. The calibration of the BBs was carried out in a similar manner. The final series was constructed by merging different BB series. We modelled the propagation of errors straightforwardly with Monte Carlo simulations. A potential bias due to the selection of BBs was investigated and the effect was shown to lie within the 1σ interval of the produced series. The exact selection of the reference period was shown to have a rather small effect on our calibration as well. Results: The final series extends back to 1739 and includes data from 314 observers. This series suggests moderate activity during the 18th and 19th century, which is significantly lower than the high level of solar activity predicted by other recent reconstructions applying linear regressions. Conclusions: The new series provides a robust reconstruction, based on modern and non-parametric methods, of sunspot group numbers since 1739, and it confirms the existence of the modern grand maximum of solar

The Strongest Magnetic Field in Sunspots

Science.gov (United States)

Okamoto, J.; Sakurai, T.

2017-12-01

Sunspots are concentrations of magnetic fields on the solar surface. Generally, the strongest magnetic field in each sunspot is located in the dark umbra in most cases. A typical field strength in sunspots is around 3,000 G. On the other hand, some exceptions also have been found in complex sunspots with bright regions such as light bridges that separate opposite polarity umbrae, for instance with a strength of 4,300 G. However, the formation mechanism of such strong fields outside umbrae is still puzzling. Here we report an extremely strong magnetic field in a sunspot, which was located in a bright region sandwiched by two opposite-polarity umbrae. The strength is 6,250 G, which is the largest ever observed since the discovery of magnetic field on the Sun in 1908 by Hale. We obtained 31 scanned maps of the active region observed by Hinode/SOT/SP with a cadence of 3 hours over 5 days (February 1-6, 2014). Considering the spatial and temporal evolution of the vector magnetic field and the Doppler velocity in the bright region, we suggested that this strong field region was generated as a result of compression of one umbra pushed by the outward flow from the other umbra (Evershed flow), like the subduction of the Earth's crust in plate tectonics.

Solar rotation and meridional motions derived from sunspot groups

International Nuclear Information System (INIS)

Tuominen, J.; Tuominen, I.; Kyroelaeinen, J.

1982-01-01

Latitudinal and longitudinal motions of sunspot groups have been studied using the positions of recurrent sunspot groups of 103 years published by Greenwich observatory. In order to avoid any limb effects, only positions close to the central meridian have been used. The data were divided into two parts: those belonging to the years around sunspot maxima and those belonging to the years around sunspot minima. Using several different criteria it was ascertained that sunspot groups show meridional motions and that their drift curves as a function of latitude are different around maxima and around minima. In addition, also the angular velocity, as a function of latitude, was found to be different around maxima and minima. (Auth.)

Sunspot Modeling: From Simplified Models to Radiative MHD Simulations

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Rolf Schlichenmaier

2011-09-01

Full Text Available We review our current understanding of sunspots from the scales of their fine structure to their large scale (global structure including the processes of their formation and decay. Recently, sunspot models have undergone a dramatic change. In the past, several aspects of sunspot structure have been addressed by static MHD models with parametrized energy transport. Models of sunspot fine structure have been relying heavily on strong assumptions about flow and field geometry (e.g., flux-tubes, "gaps", convective rolls, which were motivated in part by the observed filamentary structure of penumbrae or the necessity of explaining the substantial energy transport required to maintain the penumbral brightness. However, none of these models could self-consistently explain all aspects of penumbral structure (energy transport, filamentation, Evershed flow. In recent years, 3D radiative MHD simulations have been advanced dramatically to the point at which models of complete sunspots with sufficient resolution to capture sunspot fine structure are feasible. Here overturning convection is the central element responsible for energy transport, filamentation leading to fine-structure and the driving of strong outflows. On the larger scale these models are also in the progress of addressing the subsurface structure of sunspots as well as sunspot formation. With this shift in modeling capabilities and the recent advances in high resolution observations, the future research will be guided by comparing observation and theory.

A new look at sunspot formation using theory and observations

Science.gov (United States)

Losada, I. R.; Warnecke, J.; Glogowski, K.; Roth, M.; Brandenburg, A.; Kleeorin, N.; Rogachevskii, I.

2017-10-01

Sunspots are of basic interest in the study of the Sun. Their relevance ranges from them being an activity indicator of magnetic fields to being the place where coronal mass ejections and flares erupt. They are therefore also an important ingredient of space weather. Their formation, however, is still an unresolved problem in solar physics. Observations utilize just 2D surface information near the spot, but it is debatable how to infer deep structures and properties from local helioseismology. For a long time, it was believed that flux tubes rising from the bottom of the convection zone are the origin of the bipolar sunspot structure seen on the solar surface. However, this theory has been challenged, in particular recently by new surface observation, helioseismic inversions, and numerical models of convective dynamos. In this article we discuss another theoretical approach to the formation of sunspots: the negative effective magnetic pressure instability. This is a large-scale instability, in which the total (kinetic plus magnetic) turbulent pressure can be suppressed in the presence of a weak large-scale magnetic field, leading to a converging downflow, which eventually concentrates the magnetic field within it. Numerical simulations of forced stratified turbulence have been able to produce strong super-equipartition flux concentrations, similar to sunspots at the solar surface. In this framework, sunspots would only form close to the surface due to the instability constraints on stratification and rotation. Additionally, we present some ideas from local helioseismology, where we plan to use the Hankel analysis to study the pre-emergence phase of a sunspot and to constrain its deep structure and formation mechanism.

WAVES AS THE SOURCE OF APPARENT TWISTING MOTIONS IN SUNSPOT PENUMBRAE

International Nuclear Information System (INIS)

Bharti, L.; Cameron, R. H.; Hirzberger, J.; Solanki, S. K.; Rempel, M.

2012-01-01

The motion of dark striations across bright filaments in a sunspot penumbra has become an important new diagnostic of convective gas flows in penumbral filaments. The nature of these striations has, however, remained unclear. Here, we present an analysis of small-scale motions in penumbral filaments in both simulations and observations. The simulations, when viewed from above, show fine structure with dark lanes running outward from the dark core of the penumbral filaments. The dark lanes either occur preferentially on one side or alternate between both sides of the filament. We identify this fine structure with transverse (kink) oscillations of the filament, corresponding to a sideways swaying of the filament. These oscillations have periods in the range of 5-7 minutes and propagate outward and downward along the filament. Similar features are found in observed G-band intensity time series of penumbral filaments in a sunspot located near disk center obtained by the Broadband Filter Imager on board the Hinode. We also find that some filaments show dark striations moving to both sides of the filaments. Based on the agreement between simulations and observations we conclude that the motions of these striations are caused by transverse oscillations of the underlying bright filaments.

Iwahashi Zenbei's Sunspot Drawings in 1793 in Japan

Science.gov (United States)

Hayakawa, Hisashi; Iwahashi, Kiyomi; Tamazawa, Harufumi; Toriumi, Shin; Shibata, Kazunari

2018-01-01

Three Japanese sunspot drawings associated with Iwahashi Zenbei (1756 - 1811) are shown here from contemporary manuscripts and woodprint documents with the relevant texts. We reveal the observational date of one of the drawings to be 26 August 1793, and the overall observations lasted for over a year. Moreover, we identify the observational site for the dated drawing as Fushimi in Japan. We then compare Zenbei's observations with the group sunspot number and the raw group count from the Sunspot Index and Long-term Solar Observations (SILSO) to reveal the context of the data, and we conclude that these drawings fill gaps in our understanding that are due to the fragmental sunspot observations around 1793. These drawings are important as a clue to evaluate astronomical knowledge of contemporary Japan in the late eighteenth century and are valuable as a non-European observation, considering that most sunspot observations up to the middle of the nineteenth century are from Europe.

Sunspots During the Maunder Minimum from Machina Coelestis by Hevelius

Science.gov (United States)

Carrasco, V. M. S.; Álvarez, J. Villalba; Vaquero, J. M.

2015-10-01

We revisited the sunspot observations published by Johannes Hevelius in his book Machina Coelestis (1679) corresponding to the period of 1653 - 1675 (just in the middle of the Maunder Minimum). We show detailed translations of the original Latin texts describing the sunspot records and provide the general context of these sunspot observations. From this source, we present an estimate of the annual values of the group sunspot number based only on the records that explicitly inform us of the presence or absence of sunspots. Although we obtain very low values of the group sunspot number, in accordance with a grand minimum of solar activity, these values are significantly higher in general than the values provided by Hoyt and Schatten ( Solar Phys. 179, 189, 1998) for the same period.

Physical Properties of Umbral Dots Observed in Sunspots: A Hinode Observation

Science.gov (United States)

Yadav, Rahul; Mathew, Shibu K.

2018-04-01

Umbral dots (UDs) are small-scale bright features observed in the umbral part of sunspots and pores. It is well established that they are manifestations of magnetoconvection phenomena inside umbrae. We study the physical properties of UDs in different sunspots and their dependence on decay rate and filling factor. We have selected high-resolution, G-band continuum filtergrams of seven sunspots from Hinode to study their physical properties. We have also used Michelson Doppler Imager (MDI) continuum images to estimate the decay rate of selected sunspots. An identification and tracking algorithm was developed to identify the UDs in time sequences. The statistical analysis of UDs exhibits an averaged maximum intensity and effective diameter of 0.26 I_{QS} and 270 km. Furthermore, the lifetime, horizontal speed, trajectory length, and displacement length (birth-death distance) of UDs are 8.19 minutes, 0.5 km s-1, 284 km, and 155 km, respectively. We also find a positive correlation between intensity-diameter, intensity-lifetime, and diameter-lifetime of UDs. However, UD properties do not show any significant relation with the decay rate or filling factor.

On the insignificance of Herschel's sunspot correlation

Science.gov (United States)

Love, Jeffrey J.

2013-08-01

We examine William Herschel's hypothesis that solar-cycle variation of the Sun's irradiance has a modulating effect on the Earth's climate and that this is, specifically, manifested as an anticorrelation between sunspot number and the market price of wheat. Since Herschel first proposed his hypothesis in 1801, it has been regarded with both interest and skepticism. Recently, reports have been published that either support Herschel's hypothesis or rely on its validity. As a test of Herschel's hypothesis, we seek to reject a null hypothesis of a statistically random correlation between historical sunspot numbers, wheat prices in London and the United States, and wheat farm yields in the United States. We employ binary-correlation, Pearson-correlation, and frequency-domain methods. We test our methods using a historical geomagnetic activity index, well known to be causally correlated with sunspot number. As expected, the measured correlation between sunspot number and geomagnetic activity would be an unlikely realization of random data; the correlation is "statistically significant." On the other hand, measured correlations between sunspot number and wheat price and wheat yield data would be very likely realizations of random data; these correlations are "insignificant." Therefore, Herschel's hypothesis must be regarded with skepticism. We compare and contrast our results with those of other researchers. We discuss procedures for evaluating hypotheses that are formulated from historical data.

RE-EXAMINING SUNSPOT TILT ANGLE TO INCLUDE ANTI-HALE STATISTICS

Energy Technology Data Exchange (ETDEWEB)

McClintock, B. H. [University of Southern Queensland, Toowoomba, 4350 (Australia); Norton, A. A. [HEPL, Stanford University, Palo Alto, CA 94305 (United States); Li, J., E-mail: u1049686@umail.usq.edu.au, E-mail: aanorton@stanford.edu, E-mail: jli@igpp.ucla.edu [Department of Earth, Planetary, and Space Sciences, University of California at Los Angeles, Los Angeles, CA 90095 (United States)

2014-12-20

Sunspot groups and bipolar magnetic regions (BMRs) serve as an observational diagnostic of the solar cycle. We use Debrecen Photohelographic Data (DPD) from 1974-2014 that determined sunspot tilt angles from daily white light observations, and data provided by Li and Ulrich that determined sunspot magnetic tilt angle using Mount Wilson magnetograms from 1974-2012. The magnetograms allowed for BMR tilt angles that were anti-Hale in configuration, so tilt values ranged from 0 to 360° rather than the more common ±90°. We explore the visual representation of magnetic tilt angles on a traditional butterfly diagram by plotting the mean area-weighted latitude of umbral activity in each bipolar sunspot group, including tilt information. The large scatter of tilt angles over the course of a single cycle and hemisphere prevents Joy's law from being visually identified in the tilt-butterfly diagram without further binning. The average latitude of anti-Hale regions does not differ from the average latitude of all regions in both hemispheres. The distribution of anti-Hale sunspot tilt angles are broadly distributed between 0 and 360° with a weak preference for east-west alignment 180° from their expected Joy's law angle. The anti-Hale sunspots display a log-normal size distribution similar to that of all sunspots, indicating no preferred size for anti-Hale sunspots. We report that 8.4% ± 0.8% of all bipolar sunspot regions are misclassified as Hale in traditional catalogs. This percentage is slightly higher for groups within 5° of the equator due to the misalignment of the magnetic and heliographic equators.

RE-EXAMINING SUNSPOT TILT ANGLE TO INCLUDE ANTI-HALE STATISTICS

International Nuclear Information System (INIS)

McClintock, B. H.; Norton, A. A.; Li, J.

2014-01-01

Sunspot groups and bipolar magnetic regions (BMRs) serve as an observational diagnostic of the solar cycle. We use Debrecen Photohelographic Data (DPD) from 1974-2014 that determined sunspot tilt angles from daily white light observations, and data provided by Li and Ulrich that determined sunspot magnetic tilt angle using Mount Wilson magnetograms from 1974-2012. The magnetograms allowed for BMR tilt angles that were anti-Hale in configuration, so tilt values ranged from 0 to 360° rather than the more common ±90°. We explore the visual representation of magnetic tilt angles on a traditional butterfly diagram by plotting the mean area-weighted latitude of umbral activity in each bipolar sunspot group, including tilt information. The large scatter of tilt angles over the course of a single cycle and hemisphere prevents Joy's law from being visually identified in the tilt-butterfly diagram without further binning. The average latitude of anti-Hale regions does not differ from the average latitude of all regions in both hemispheres. The distribution of anti-Hale sunspot tilt angles are broadly distributed between 0 and 360° with a weak preference for east-west alignment 180° from their expected Joy's law angle. The anti-Hale sunspots display a log-normal size distribution similar to that of all sunspots, indicating no preferred size for anti-Hale sunspots. We report that 8.4% ± 0.8% of all bipolar sunspot regions are misclassified as Hale in traditional catalogs. This percentage is slightly higher for groups within 5° of the equator due to the misalignment of the magnetic and heliographic equators

Sunspots Resource--From Ancient Cultures to Modern Research

Science.gov (United States)

Craig, N.

2000-10-01

Sunspots is a web-based lesson that was developed by the Science Education Gateway (SEGway) program with participants from the Exploratorium, a well known science Museum in San Francisco, UC Berkeley Space Sciences Laboratory, and teachers from several California schools. This space science resource allows 8-12 grade students to explore the nature of sunspots and the history of solar physics in its effort to understand their nature. Interviews with solar physicists and archeo-astronomers, historic images, cutting-edge NASA images, movies, and research results, as well as a student-centered sunspot research activity using NASA space science data defines this lesson. The sunspot resource is aligned with the NCTM and National Science Education Standards. It emphasizes inquiry-based methods and mathematical exercises through measurement, graphic data representation, analysis of NASA data, lastly, interpreting results and drawing conclusions. These resources have been successfully classroom tested in 4 middle schools in the San Francisco Unified School District as part of the 3-week Summer School Science curricula. Lessons learned from the Summer School 1999 will be explained. This resource includes teacher-friendly lesson plans, space science background material and student worksheets. There will be Sunspots lesson CD-ROM and printed version of the relevant classroom-ready materials and a teacher resource booklet available. Sunspot resource is brought to you by, The Science Education Gateway - SEGway - Project, and the HESSI satellite and NASA's Office of Space Science Sun-Earth Connection Education Forum.

Outflow of chromospheric emission features from the rim of a sunspot

Science.gov (United States)

Liu, S.-Y.

1973-01-01

In viewing a 16 mm movie made from a time sequence of spectroheliograms, some of these emission features are found to move outward from the rim of the sunspot until they are eventually lost in the small plage. There are two interpretations for the streaming of the magnetic features. It is possible that kinks in the line of force propagate along a horizontal extension of the penumbral magnetic field. Alternatively, fragments of the sunspot magnetic field are carried away by the photospheric velocity field.

The chromosphere above a δ-sunspot in the presence of fan-shaped jets

Science.gov (United States)

Robustini, Carolina; Leenaarts, Jorrit; de la Cruz Rodríguez, Jaime

2018-01-01

Context. Delta-sunspots are known to be favourable locations for fast and energetic events like flares and coronal mass ejections. The photosphere of this sunspot type has been thoroughly investigated in the past three decades. The atmospheric conditions in the chromosphere are not as well known, however. Aims: This study is focused on the chromosphere of a δ-sunspot that harbours a series of fan-shaped jets in its penumbra. The aim of this study is to establish the magnetic field topology and the temperature distribution in the presence of jets in the photosphere and the chromosphere. Methods: We use data from the Swedish 1m Solar Telescope (SST) and the Solar Dynamics Observatory. We invert the spectropolarimetric Fe I 6302 Å and Ca II 8542 Å data from the SST using the non-LTE inversion code NICOLE to estimate the magnetic field configuration, temperature, and velocity structure in the chromosphere. Results: A loop-like magnetic structure is observed to emerge in the penumbra of the sunspot. The jets are launched from this structure. Magnetic reconnection between this emerging field and the pre-existing vertical field is suggested by hot plasma patches on the interface between the two fields. The height at which the reconnection takes place is located between log τ500 = -2 and log τ500 = -3. The magnetic field vector and the atmospheric temperature maps show a stationary configuration during the whole observation. Movies associated to Figs. 3-5 are available at http://www.aanda.org

On the evolution of magnetic and velocity fields of an originating sunspot group

International Nuclear Information System (INIS)

Bachmann, G.

1978-01-01

Magnetographic measurements were made to derive longitudinal magnetic field strengths, line-of-sight velocities and the brightness distribution in an originating sunspot group. These results and photographs of the group are used to compare the evaluation of a relatively simple active region with our present ideas about the evolution of active regions in general. We found that the total magnetic flux increased from about 4 to 20x10 20 Mx over three days. The downward flow of gas in regions with stronger magnetic fields is formed only after the magnetic field has already been bipolar for two days. The maximum velocity always occurred in the main spots of the preceding and the subsequent parts of the sunspot group. Transformation into a flow pattern, which looks like Evershed motion, is observed in the main preceding sunspot after the formation of the penumbra. The generation of new active regions by concentration and amplification of magnetic fields, under the action of supergranulation flow in photospheric layers, cannot play an important role. On the contrary, the behaviour of the active region is in agreement with the conception of rising flux tubes, out of which the gas flows down. Our observations confirm that a magnetic field strength, leading to the generation of sunspots, is attained earlier in the preceding part of the originating active region than in its subsequent part. A series of subflares occurred in the active region, when short-lived small magnetic structure elements emerged in the larger bipolar magnetic field. (author)

Relationship between geomagnetic classes’ activity phases and their occurrence during the sunspot cycle

Directory of Open Access Journals (Sweden)

Frédéric Ouattara

2009-06-01

Full Text Available Four well known geomagnetic classes of activity such as quiet days activity, fluctuating activity, recurrent activity
and shock activity time occurrences have been determined not only by using time profile of sunspot number
Rz but also by using aa index values.
We show that recurrent wind stream activity and fluctuating activity occur in opposite phase and slow solar wind
activity during minimum phase and shock activity at the maximum phase.
It emerges from this study that fluctuating activity precedes the sunspot cycle by π/2 and the latter also precedes
recurrent activity by π/2. Thus in the majority the activities do not happen at random; the sunspot cycle starts
with quiet days activity, continues with fluctuating activity and during its maximum phase arrives shock activity.
The descending phase is characterized by the manifestation of recurrent wind stream activity.

Studies of kinematic elements in two multicenter sunspot groups

International Nuclear Information System (INIS)

Korobova, Z.B.

1983-01-01

Some features of kinematic elements (KE) in two multicenter sunspot groups were studied using Tashkent full-disc white light heliograms. KE and morphological elements do not reveal any relationship. A KE coincides with a unipolar or multipolar spot or with part of a spot. It may also contain an extended stream including several spots. Relation of KE to large-scale photospheric magnetic fields is less clear. The line of polarity reversal is, in most cases, the deviding line between two adjacent KE. At the same time, a KE can contain spots of both polarities. Sunspot trajectories in the leading polarity regions show the best similarity. Interactions of KE are greatly influenced by the meridional drift. (author)

Long-term Modulation of Cosmic Ray Intensity in relation to Sunspot ...

Indian Academy of Sciences (India)

it should be more closely connected with cosmic ray modulation than with other solar characteristics (sunspot numbers or coronal emission intensity). The intensity of galactic cosmic rays varies inversely with sunspot numbers, having their maximum intensity at the minimum of the 11-year sunspot cycle (Forbush 1954, 1958) ...

Photometric measurements of solar irradiance variations due to sunspots

International Nuclear Information System (INIS)

Chapman, G.A.; Herzog, A.D.; Laico, D.E.; Lawrence, J.K.; Templer, M.S.

1989-01-01

A photometric telescope constructed to obtain photometric sunspot areas and deficits on a daily basis is described. Data from this Cartesian full disk telescope (CFDT) are analyzed with attention given to the period between June 4 and June 17, 1985 because of the availability of overlapping sunspot area and irradiance deficit data from high-resolution digital spectroheliograms made with the San Fernando Observatory 28 cm vacuum solar telescope and spectroheliograph. The CFDT sunspot deficits suggest a substantial irradiance contribution from faculae and active region plage. 23 refs

The Flares Associated with the Dynamics of the Sunspots K. M. ...

Indian Academy of Sciences (India)

tional theory of magnetic reconnection is briefly discussed. ... between changes in the sunspots' dynamics, emerging flux region, twisting of the field ... the eventual triggering of the flares is due to proper motion of the sunspots. Using .... rotation rates obtained from the daily motion of sunspot groups with respect to their life.

Wings of the butterfly: Sunspot groups for 1826-2015

Science.gov (United States)

Leussu, R.; Usoskin, I. G.; Senthamizh Pavai, V.; Diercke, A.; Arlt, R.; Denker, C.; Mursula, K.

2017-03-01

The spatio-temporal evolution of sunspot activity, the so-called Maunder butterfly diagram, has been continously available since 1874 using data from the Royal Greenwich Observatory, extended by SOON network data after 1976. Here we present a new extended butterfly diagram of sunspot group occurrence since 1826, using the recently digitized data from Schwabe (1826-1867) and Spörer (1866-1880). The wings of the diagram are separated using a recently developed method based on an analysis of long gaps in sunspot group occurrence in different latitude bands. We define characteristic latitudes, corresponding to the start, end, and the largest extent of the wings (the F, L, and H latitudes). The H latitudes (30°-45°) are highly significantly correlated with the strength of the wings (quantified by the total sum of the monthly numbers of sunspot groups). The F latitudes (20°-30°) depict a weak tendency, especially in the southern hemisphere, to follow the wing strength. The L latitudes (2°-10°) show no clear relation to the wing strength. Overall, stronger cycle wings tend to start at higher latitudes and have a greater wing extent. A strong (5-6)-cycle periodic oscillation is found in the start and end times of the wings and in the overlap and gaps between successive wings of one hemisphere. While the average wing overlap is zero in the southern hemisphere, it is two to three months in the north. A marginally significant oscillation of about ten solar cycles is found in the asymmetry of the L latitudes. The new long database of butterfly wings provides new observational constraints to solar dynamo models that discuss the spatio-temporal distribution of sunspot occurrence over the solar cycle and longer. Digital data for Fig. 1 are available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/599/A131

Sunspot splitting triggering an eruptive flare

Science.gov (United States)

Louis, Rohan E.; Puschmann, Klaus G.; Kliem, Bernhard; Balthasar, Horst; Denker, Carsten

2014-02-01

Aims: We investigate how the splitting of the leading sunspot and associated flux emergence and cancellation in active region NOAA 11515 caused an eruptive M5.6 flare on 2012 July 2. Methods: Continuum intensity, line-of-sight magnetogram, and dopplergram data of the Helioseismic and Magnetic Imager were employed to analyse the photospheric evolution. Filtergrams in Hα and He I 10830 Å of the Chromospheric Telescope at the Observatorio del Teide, Tenerife, track the evolution of the flare. The corresponding coronal conditions were derived from 171 Å and 304 Å images of the Atmospheric Imaging Assembly. Local correlation tracking was utilized to determine shear flows. Results: Emerging flux formed a neutral line ahead of the leading sunspot and new satellite spots. The sunspot splitting caused a long-lasting flow towards this neutral line, where a filament formed. Further flux emergence, partly of mixed polarity, as well as episodes of flux cancellation occurred repeatedly at the neutral line. Following a nearby C-class precursor flare with signs of interaction with the filament, the filament erupted nearly simultaneously with the onset of the M5.6 flare and evolved into a coronal mass ejection. The sunspot stretched without forming a light bridge, splitting unusually fast (within about a day, complete ≈6 h after the eruption) in two nearly equal parts. The front part separated strongly from the active region to approach the neighbouring active region where all its coronal magnetic connections were rooted. It also rotated rapidly (by 4.9° h-1) and caused significant shear flows at its edge. Conclusions: The eruption resulted from a complex sequence of processes in the (sub-)photosphere and corona. The persistent flows towards the neutral line likely caused the formation of a flux rope that held the filament. These flows, their associated flux cancellation, the emerging flux, and the precursor flare all contributed to the destabilization of the flux rope. We

On the relation between activity-related frequency shifts and the sunspot distribution over the solar cycle 23

Directory of Open Access Journals (Sweden)

Santos Ângela R. G.

2017-01-01

Full Text Available The activity-related variations in the solar acoustic frequencies have been known for 30 years. However, the importance of the different contributions is still not well established. With this in mind, we developed an empirical model to estimate the spot-induced frequency shifts, which takes into account the sunspot properties, such as area and latitude. The comparison between the model frequency shifts obtained from the daily sunspot records and those observed suggests that the contribution from a stochastic component to the total frequency shifts is about 30%. The remaining 70% is related to a global, long-term variation. We also propose a new observable to investigate the short-and mid-term variations of the frequency shifts, which is insensitive to the long-term variations contained in the data. On the shortest time scales the variations in the frequency shifts are strongly correlated with the variations in the total area covered by sunspots. However, a significant loss of correlation is still found, which cannot be fully explained by ignoring the invisible side of the Sun when accounting for the total sunspot area. We also verify that the times when the frequency shifts and the sunspot areas do not vary in a similar way tend to coincide with the times of the maximum amplitude of the quasi-biennial variations found in the seismic data.

Trend Estimation and Regression Analysis in Climatological Time Series: An Application of Structural Time Series Models and the Kalman Filter.

Science.gov (United States)

Visser, H.; Molenaar, J.

1995-05-01

The detection of trends in climatological data has become central to the discussion on climate change due to the enhanced greenhouse effect. To prove detection, a method is needed (i) to make inferences on significant rises or declines in trends, (ii) to take into account natural variability in climate series, and (iii) to compare output from GCMs with the trends in observed climate data. To meet these requirements, flexible mathematical tools are needed. A structural time series model is proposed with which a stochastic trend, a deterministic trend, and regression coefficients can be estimated simultaneously. The stochastic trend component is described using the class of ARIMA models. The regression component is assumed to be linear. However, the regression coefficients corresponding with the explanatory variables may be time dependent to validate this assumption. The mathematical technique used to estimate this trend-regression model is the Kaiman filter. The main features of the filter are discussed.Examples of trend estimation are given using annual mean temperatures at a single station in the Netherlands (1706-1990) and annual mean temperatures at Northern Hemisphere land stations (1851-1990). The inclusion of explanatory variables is shown by regressing the latter temperature series on four variables: Southern Oscillation index (SOI), volcanic dust index (VDI), sunspot numbers (SSN), and a simulated temperature signal, induced by increasing greenhouse gases (GHG). In all analyses, the influence of SSN on global temperatures is found to be negligible. The correlations between temperatures and SOI and VDI appear to be negative. For SOI, this correlation is significant, but for VDI it is not, probably because of a lack of volcanic eruptions during the sample period. The relation between temperatures and GHG is positive, which is in agreement with the hypothesis of a warming climate because of increasing levels of greenhouse gases. The prediction performance of

Sunspot Oscillations From The Chromosphere To The Corona

Science.gov (United States)

Brynildsen, N.; Maltby, P.; Fredvik, T.; Kjeldseth-Moe, O.

The behavior of the 3 minute sunspot oscillations is studied as a function of temper- ature through the transition region using observations with CDS/SOHO and TRACE. The oscillations occur above the umbra, with amplitudes increasing to a maximum near 200 000 K, then decreasing towards higher temperatures. Deviations from pure linear oscillations are present in several cases. Power spectra of the oscillations are remarkably similar in the chromosphere and through the transition region in contra- diction to the predictions of the sunspot filter theory. The 3 minute oscillations pene- trate to the low temperature end of the corona, where they are channeled into smaller areas coinciding with the endpoints of sunspot coronal loops. This differs from the transition zone where the oscillating region covers the umbra.

Interactions between nested sunspots. 1: The formation and breakup of a delta-type sunspot

Science.gov (United States)

Gaizauskas, V.; Harvey, K. L.; Proulx, M.

1994-01-01

We investigate a nest of sunspots in which three ordinary bipolar pairs of sunspots are aligned collinearly. The usual spreading action of the growing regions brings two spots of leading polarity together (p-p collision) and forces the leading and trailing spots of the two interior regions to overlap inot a single penumbra (p-f collision), thus forming a delta-spot. We examine digitally processed images from the Ottawa River Solar Observatory of two related events inside the delta-spot 5 days after the p-f collision begins: the violent disruption of the f-umbra, and the formation in less than a day of an hydrogen-alpha filament. The evolutionary changes in shape, area, relative motions, and brightness that we measure for each spot in the elongated nest are more compatible with Parker's (1979a) hypothesis of a sunspot as a cluster of flux tubes held together by downdrafts than with the notion of a sunspot as a monolithic plug of magnetic flux. From chromospheric developments over the delta-spot, we show that a shearing motion along a polarity inversion is more effective than convergence for creating a chromospheric filament. We invoke the release of an instability, triggered by a sequence of processes lasting 1 day or more, to explain the disruption of the f-umbra in this delta-spot. We show that the sequence is initiated when the colliding p-f umbrae reach a critical separation around 3200 +/- 200 km. We present a descriptive model in which the reconnected magnetic fields block vertical transport of convective heat flux just beneath the photosphere. We observe the formation of an unusual type of penumbra adjacent to the f-polarity portion of this delta-spot just before its disruption. A tangential penumbral band grows out of disordered matter connected to the f-umbra. We present this as evidence for the extrusion of umbral magnetic flux by thermal plumes rising through a loosely bound umbra.

Observations of the birth and fine structure of sunspot penumbrae

International Nuclear Information System (INIS)

Collados, M.; Garcia de la Rosa, J.I.; Moreno-Insertis, F.; Vazquez, M.

1985-01-01

High resolution white-light pictures of sunspot penumbrae are presented. These include pictures showing details of their filamentary structure and some instances of birth of a penumbra. The observations are discussed in the framework of current penumbra theories. A series of pictures have been presented, which give additional evidence of the existence of dark penumbral filaments as individual structures. With respect to the birth of the penumbra some new observational aspects can be seen. The existence of the filamentary penumbra even in the first moments, its non uniformity and its short length are the major aspects derived from the pictures

Application of Avco data analysis and prediction techniques (ADAPT) to prediction of sunspot activity

Science.gov (United States)

Hunter, H. E.; Amato, R. A.

1972-01-01

The results are presented of the application of Avco Data Analysis and Prediction Techniques (ADAPT) to derivation of new algorithms for the prediction of future sunspot activity. The ADAPT derived algorithms show a factor of 2 to 3 reduction in the expected 2-sigma errors in the estimates of the 81-day running average of the Zurich sunspot numbers. The report presents: (1) the best estimates for sunspot cycles 20 and 21, (2) a comparison of the ADAPT performance with conventional techniques, and (3) specific approaches to further reduction in the errors of estimated sunspot activity and to recovery of earlier sunspot historical data. The ADAPT programs are used both to derive regression algorithm for prediction of the entire 11-year sunspot cycle from the preceding two cycles and to derive extrapolation algorithms for extrapolating a given sunspot cycle based on any available portion of the cycle.

LONG-TERM MEASUREMENTS OF SUNSPOT MAGNETIC TILT ANGLES

Energy Technology Data Exchange (ETDEWEB)

Li Jing [Department of Earth and Space Sciences, University of California at Los Angeles, Los Angeles, CA 90095-1567 (United States); Ulrich, Roger K., E-mail: jli@igpp.ucla.edu [Department of Physics and Astronomy, University of California at Los Angeles, Los Angeles, CA 90095-1567 (United States)

2012-10-20

Tilt angles of close to 30,600 sunspots are determined using Mount Wilson daily averaged magnetograms taken from 1974 to 2012, and SOHO/MDI magnetograms taken from 1996 to 2010. Within a cycle, more than 90% of sunspots have a normal polarity alignment along the east-west direction following Hale's law. The median tilts increase with increasing latitude (Joy's law) at a rate of {approx}0.{sup 0}5 per degree of latitude. Tilt angles of spots appear largely invariant with respect to time at a given latitude, but they decrease by {approx}0.{sup 0}9 per year on average, a trend that largely reflects Joy's law following the butterfly diagram. We find an asymmetry between the hemispheres in the mean tilt angles. On average, the tilts are greater in the Southern than in the Northern Hemisphere for all latitude zones, and the differences increase with increasing latitude.

The magnetic nature of umbra-penumbra boundary in sunspots

Science.gov (United States)

Jurčák, J.; Rezaei, R.; González, N. Bello; Schlichenmaier, R.; Vomlel, J.

2018-03-01

Context. Sunspots are the longest-known manifestation of solar activity, and their magnetic nature has been known for more than a century. Despite this, the boundary between umbrae and penumbrae, the two fundamental sunspot regions, has hitherto been solely defined by an intensity threshold. Aim. Here, we aim at studying the magnetic nature of umbra-penumbra boundaries in sunspots of different sizes, morphologies, evolutionary stages, and phases of the solar cycle. Methods: We used a sample of 88 scans of the Hinode/SOT spectropolarimeter to infer the magnetic field properties in at the umbral boundaries. We defined these umbra-penumbra boundaries by an intensity threshold and performed a statistical analysis of the magnetic field properties on these boundaries. Results: We statistically prove that the umbra-penumbra boundary in stable sunspots is characterised by an invariant value of the vertical magnetic field component: the vertical component of the magnetic field strength does not depend on the umbra size, its morphology, and phase of the solar cycle. With the statistical Bayesian inference, we find that the strength of the vertical magnetic field component is, with a likelihood of 99%, in the range of 1849-1885 G with the most probable value of 1867 G. In contrast, the magnetic field strength and inclination averaged along individual boundaries are found to be dependent on the umbral size: the larger the umbra, the stronger and more horizontal the magnetic field at its boundary. Conclusions: The umbra and penumbra of sunspots are separated by a boundary that has hitherto been defined by an intensity threshold. We now unveil the empirical law of the magnetic nature of the umbra-penumbra boundary in stable sunspots: it is an invariant vertical component of the magnetic field.

The EUV Spectrum of Sunspot Plumes Observed by SUMER on ...

Indian Academy of Sciences (India)

tribpo

Abstract. We present results from sunspot observations obtained by. SUMER on SOHO. In sunspot plumes the EUV spectrum differs from the quiet Sun; continua are observed with different slopes and intensities; emission lines from molecular hydrogen and many unidentified species indicate unique plasma conditions ...

Investigation of Quasi-periodic Solar Oscillations in Sunspots Based on SOHO/MDI Magnetograms

Science.gov (United States)

Kallunki, J.; Riehokainen, A.

2012-10-01

In this work we study quasi-periodic solar oscillations in sunspots, based on the variation of the amplitude of the magnetic field strength and the variation of the sunspot area. We investigate long-period oscillations between three minutes and ten hours. The magnetic field synoptic maps were obtained from the SOHO/MDI. Wavelet (Morlet), global wavelet spectrum (GWS) and fast Fourier transform (FFT) methods are used in the periodicity analysis at the 95 % significance level. Additionally, the quiet Sun area (QSA) signal and an instrumental effect are discussed. We find several oscillation periods in the sunspots above the 95 % significance level: 3 - 5, 10 - 23, 220 - 240, 340 and 470 minutes, and we also find common oscillation periods (10 - 23 minutes) between the sunspot area variation and that of the magnetic field strength. We discuss possible mechanisms for the obtained results, based on the existing models for sunspot oscillations.

Photoelectric observations of propagating sunspot oscillations

International Nuclear Information System (INIS)

Lites, B.W.; White, O.R.; Packman, D.

1982-01-01

The Sacramento Park Observatory Vacuum Tower Telescope and diode array were used to make repeated intensity and velocity images of a large, isolated sunspot in both a chromospheric (lambda8542 Ca II) and a photospheric (lambda5576 Fe I) line. The movie of the digital data for the chromospheric line shows clearly a relationship between the propagating umbral disturbances and the running penumbral waves. The velocities for transverse propagating of the umbral and penumbral disturbances are 60--70 km s -1 and 20--35 km s -1 , respectively. Power spectra of the oscillations show a sharp peak at a period of about 170 s in both the velocity and intensity signals. The rms velocity fluctuation of this power peak is 0.26 km s -1 . The oscillations at any given point in the sunspot are very regular, and the phase relationship between the velocity and intensity of the chromospheric oscillations is radically different than that for the quiet Sun. Our preliminary interpretation of the phase relationship involves acoustic waves with wave vector directed downwards along the magnetic field lines; however, this interpretation relies on assumptions involved in the data reduction scheme. The mechanical energy flux carried by the observed umbral disturbances does not appear to be a significant contributor to the overall energy budget of the sunspot or the surrounding active region

On the chromospheric network structure around deVeloped groups of sunspots

International Nuclear Information System (INIS)

Kartashova, L.G.

1980-01-01

The chromospheric network structure around several developed groups of sunspots were studied on the basis of the observations in the Hsub(α) line. The resolution on the filtergrams was of 2. The following was found: 1) in the neighbourhood of the groups of sunspots 70% (from 870) of network cells stretch along fibrils direction (with accuracy 30 deg), and 15% of cells stretch approximately across that (at angles 70-90 deg); 2) out of the boundary of the main radial fibrils structure the groups of sunspots is often rounded by the system of network cells stretched approximately perpendicular to radial direction

The photospheric vector magnetic field of a sunspot and its vertical gradient

Science.gov (United States)

Hagyard, M. J.; West, E. A.; Tandberg-Hanssen, E.; Smith, J. E.; Henze, W., Jr.; Beckers, J. M.; Bruner, E. C.; Hyder, C. L.; Gurman, J. B.; Shine, R. A.

1981-01-01

The results of direct comparisons of photospheric and transition region line-of-sight field observations of sunspots using the SMM UV spectrometer and polarimeter are reported. The analysis accompanying the data is concentrated on demonstrating that the sunspot concentrated magnetic field extends into the transition region. An observation of a sunspot on Oct. 23, 1980 at the S 18 E 03 location is used as an example. Maximum field strengths ranged from 2030-2240 gauss for large and small umbrae viewed and inclination of the field to the line-of-sight was determined for the photosphere and transition region. The distribution of the magnetic field over the sunspot and variation of the line-of-sight gradient are discussed, as are the magnitudes and gradients of the photospheric field across the penumbral-photospheric boundaries.

Effect of solar flare ans sunspot numbers on the intensity of 5577A line in the night airglow

International Nuclear Information System (INIS)

Kundu, N.; Ghosh, S.N.

1981-01-01

The effects of solar flare and sunspot number on the intensity of 5577 A line emission are presented. The time lag between the occurrence of a flare and the enhancement of 5577 A line intensity is determined by observing the intensity of the line on three successive nights- the night preceding the flare and the two nights following it. The velocity of the solar corpuscles is then calculated. The value obtained at Allahabad (2400 Km/sec) is in agreement with the De Jager's prediction for explosive flare. Day-to-day analyses of the observations taken at Allahabad exhibit high correlation of the intensity of 5577 A line emission with sunspot number. Also, correlation is found for the intensity of 5577 A with the change in sunspot number (DELTA R) from the day preceding the night of observation to the day following it. The intensity appears to vary with the magnetic field produced by the sunspot and not with the spot area. (author)

Sunspot Equilibria in a Production Economy: Do Rational Animal Spirits Cause Overproduction?

OpenAIRE

Kajii, Atsushi

2008-01-01

We study a standard two period economy with one nominal bond and one firm. The input of the firm is done in the first period and financed with the nominal bond, and its profits are distributed to the shareholders in the second period. We show that a sunspot equilibrium exists around each efficient equilibrium. The interest rate is lower than optimal and there is over production in sunspot equilibria, under some conditions. But a sunspot equilibrium does not exist if the profit share can be tr...

Prediction on sunspot activity based on fuzzy information granulation and support vector machine

Science.gov (United States)

Peng, Lingling; Yan, Haisheng; Yang, Zhigang

2018-04-01

In order to analyze the range of sunspots, a combined prediction method of forecasting the fluctuation range of sunspots based on fuzzy information granulation (FIG) and support vector machine (SVM) was put forward. Firstly, employing the FIG to granulate sample data and extract va)alid information of each window, namely the minimum value, the general average value and the maximum value of each window. Secondly, forecasting model is built respectively with SVM and then cross method is used to optimize these parameters. Finally, the fluctuation range of sunspots is forecasted with the optimized SVM model. Case study demonstrates that the model have high accuracy and can effectively predict the fluctuation of sunspots.

Corona magnetic field over sunspots estimated by m-wave observation

International Nuclear Information System (INIS)

Kurihara, Masahiro

1974-01-01

The shape of the magnetic field in corona was estimated from the observation of the type I storm occurred in the last decade of August, 1971. It was found from the observation with a 160 MHz interferometer at Mt. Nobeyama that at most three storm sources, which are called radio wave source, were produced. The radio wave sources were fixed above sunspots. The height of the radio wave sources was estimated to be 0.45 R from the photosphere. The sunspots under the radio wave sources can be classified to four sub-groups. Weakening of the magnetic field on the photosphere was found from the reduction of the area of some sub-group. The relation between the activity of type I storm and the intensity of the magnetic field of sunspots is qualitatively suggested. It is considered that the radio wave sources and the sunspots were connected by common magnetic force lines. The probable magnetic field in corona was presumed and is shown in a figure. An interesting point is that the direction of magnetic force lines inclined by about 30 0 outward to the vertical line to the photosphere surface. (Kato, T.)

The use of solar faculae in studies of the sunspot cycle

International Nuclear Information System (INIS)

Brown, G.M.; Evans, R.

1980-01-01

Comparison of the long-term variation of photospheric faculae areas with that of sunspots shows that studies of faculae provide both complementary and supplementary information on the behaviour of the solar cycle. Detailed studies of the development of sunspots with respect to faculae show that there is a high degree of order over much of a given cycle, but marked differences from cycle to cycle. Within a cycle the relationship between spot and faculae areas appears to be similar for the N and S solar hemispheres, and over the early stages of a cycle it is directly related to the magnitude of the maximum sunspot number subsequently attained in that cycle. This result may well have predictive applications, and formulae are given relating the peak sunspot number to simple parameters derived from this early developmental stage. Full application to the current cycle 21 is denied due to the cessation of the Greenwich daily photoheliographic measurements, but use of the cruder weekly data suggests a maximum smoothed sunspot number of 150 +- 22. The effects of the incompatibility of the spot and faculae data, in that faculae are unobservable over a large fraction of the solar disc and also do not always develop associated spots, have been examined in a detailed study of two cycles and shown not to vitiate the results. (orig.)

Period of sunspot numbers is 11. 02653720 years (11 years 9 days 16 hours 18 minutes 0 seconds)

Energy Technology Data Exchange (ETDEWEB)

Norita, S [Miyazaki Univ. (Japan). Faculty of Engineering

1976-09-01

In the statistical analysis of time series there have been applied usually the stationary stochastic process or the Markov stochastic process and recently there are applied remarkably an autoregressive process, a stochastic difference equation, an autoregressive-moving average process, a moving average process, the Whittaker periodogram, the correlogram, Schuster periodogram, chi-squared periodogram, level crossings, harmonic process, difference method, spectral density and first order vector equation, but in special case it is desirable to apply the nonstationary stocastic process. In this paper we introduce a stationarity into the autoregressive process and then it is the first purpose to compute precisely the period of sunspot numbers. The result up to the eighth places at the decimal point was obtained that its period is 11.02653720 years, that is, 11 years 9 days 16 hours 18 minutes 0 seconds. This is considered to be more relevant than numerical values by which Schuster (1906) and Yule (1927) had calculated the respective 11.125 years and 10.60 years in the past. We revised the theoretical expression in the thesis of Anderson, Shaman, Lindgren, Brillinger, Newbold, Parzen, Kingman, Van Ness and Kenneth, etc. and executed the numerical analysis of period of sunspot numbers investigated now.

Period of sunspot numbers is 11.02653720 years (11 years 9 days 16 hours 18 minutes 0 seconds)

International Nuclear Information System (INIS)

Norita, Sadataka

1976-01-01

In the statistical analysis of time series there have been applied usually the stationary stochastic process or the Markov stochastic process and recently there are applied remarkably an autoregressive process, a stochastic difference equation, an autoregressive-moving average process, a moving average process, the Whittaker periodogram, the correlogram, Schuster periodogram, chi-squared periodogram, level crossings, harmonic process, difference method, spectral density and first order vector equation, but in special case it is desirable to apply the nonstationary stocastic process. In this paper we introduce an stationarity into the autoregressive process and then it is the first purpose to compute precisely period of sunspot numbers. The result up to the eighth places at the decimal point was obtained that its period is 11.02653720 years, that is, 11 years 9 days 16 hours 18 minutes 0 seconds. This is considered to be more relevant than numerical values by which Schuster (1906) and Yule (1927) had calculated the respective 11.125 years and 10.60 years in the past. We revised the theoretical expression in the thesis of Anderson, Shaman, Lindgren, Brillinger, Newbold, Parzen, Kingman, Van Ness and Kenneth, etc. and executed the numerical analysis of period of sunspot numbers investigated now. (auth.)

SUNSPOT CYCLES IMPACTS ON TOURISM AND QUALITY OF LIFE

Directory of Open Access Journals (Sweden)

Tadeja Jere Jakulin

2017-09-01

Full Text Available We live under the influence of natural cycles caused by the rotation of our planet and its revolution around the sun. The nature of our nearest star is also subject to cyclical change. This article presents a study of a correlation between sunspot cycles and foreign tourists arrivals in Slovenia, based on historical data between sunspot cycles and sea salt production in Slovenia's Municipality of Piran during the Maunder Minimum period (1645-1715. The production of salt by the solar evaporation of brine in salt pans and tourist industry are seasonal economic activities that are affected by changes to the weather. The paper looks at sea salt production in Piran during a particular period in the past. The repetition of the sea salt production in the past is not possible. For this reason, the study uses mathematical tools and an additional case study, which analyses arrivals of foreign tourists to Slovenia over the past 65 years (1948-2012. The study has two purposes: to identify a linear correlation coefficient, which provides evidence of a correlation between arrivals of foreign tourists to Slovenia and sunspot cycles and to develop a causal loop diagram (CLD or so called qualitative model of a complex tourism system, which shows the interdependency of sunspot cycles, tourism system, and quality of life.

SUNSPOT AND STARSPOT LIFETIMES IN A TURBULENT EROSION MODEL

Energy Technology Data Exchange (ETDEWEB)

Litvinenko, Yuri E. [Department of Mathematics, University of Waikato, P. B. 3105, Hamilton (New Zealand); Wheatland, M. S. [Sydney Institute for Astronomy, School of Physics, The University of Sydney, NSW 2006 (Australia)

2017-01-10

Quantitative models of sunspot and starspot decay predict the timescale of magnetic diffusion and may yield important constraints in stellar dynamo models. Motivated by recent measurements of starspot lifetimes, we investigate the disintegration of a magnetic flux tube by nonlinear diffusion. Previous theoretical studies are extended by considering two physically motivated functional forms for the nonlinear diffusion coefficient D : an inverse power-law dependence D ∝ B {sup −ν} and a step-function dependence of D on the magnetic field magnitude B . Analytical self-similar solutions are presented for the power-law case, including solutions exhibiting “super fast” diffusion. For the step-function case, the heat-balance integral method yields approximate solutions, valid for moderately suppressed diffusion in the spot. The accuracy of the resulting solutions is confirmed numerically, using a method which provides an accurate description of long-time evolution by imposing boundary conditions at infinite distance from the spot. The new models may allow insight into the differences and similarities between sunspots and starspots.

On the structure of small sunspots

International Nuclear Information System (INIS)

Ringnes, T.S.

1984-01-01

The smallest and most short-lived sunspots are decribed differently at the observatories in Zuerich and Greenwich. These differences which seem to originate both from the observing procedure and from the definitions of penumbra and umbra adopted, are further discussed

GPS Position Time Series @ JPL

Science.gov (United States)

Owen, Susan; Moore, Angelyn; Kedar, Sharon; Liu, Zhen; Webb, Frank; Heflin, Mike; Desai, Shailen

2013-01-01

Different flavors of GPS time series analysis at JPL - Use same GPS Precise Point Positioning Analysis raw time series - Variations in time series analysis/post-processing driven by different users. center dot JPL Global Time Series/Velocities - researchers studying reference frame, combining with VLBI/SLR/DORIS center dot JPL/SOPAC Combined Time Series/Velocities - crustal deformation for tectonic, volcanic, ground water studies center dot ARIA Time Series/Coseismic Data Products - Hazard monitoring and response focused center dot ARIA data system designed to integrate GPS and InSAR - GPS tropospheric delay used for correcting InSAR - Caltech's GIANT time series analysis uses GPS to correct orbital errors in InSAR - Zhen Liu's talking tomorrow on InSAR Time Series analysis

Time series analysis time series analysis methods and applications

CERN Document Server

Rao, Tata Subba; Rao, C R

2012-01-01

The field of statistics not only affects all areas of scientific activity, but also many other matters such as public policy. It is branching rapidly into so many different subjects that a series of handbooks is the only way of comprehensively presenting the various aspects of statistical methodology, applications, and recent developments. The Handbook of Statistics is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with Volume 30 dealing with time series. The series is addressed to the entire community of statisticians and scientists in various disciplines who use statistical methodology in their work. At the same time, special emphasis is placed on applications-oriented techniques, with the applied statistician in mind as the primary audience. Comprehensively presents the various aspects of statistical methodology Discusses a wide variety of diverse applications and recent developments Contributors are internationally renowened experts in their respect...

Sunspots sketches during the solar eclipses of 9th January and 29th December of 1777 in Mexico

Science.gov (United States)

Domínguez-Castro, Fernando; Gallego, María Cruz; Vaquero, José Manuel

2017-06-01

Two sunspot observations recorded by the Mexican Felipe de Zúñiga y Ontiveros have been revealed from a manuscript. One sunspot group was recorded on 9th January 1777 and four sunspot groups on 29th December 1777. Both records were taken during the observation of solar eclipses from Mexico City and their description also included sketches of the solar disk with sunspots. The sunspot group corresponding to 9th January was also observed by Erasmus Lievog. The observation on 29th December 1777 is the only record corresponding to this date.

Highly comparative time-series analysis: the empirical structure of time series and their methods.

Science.gov (United States)

Fulcher, Ben D; Little, Max A; Jones, Nick S

2013-06-06

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.

Statistics of the largest sunspot and facular areas per solar cycle

International Nuclear Information System (INIS)

Willis, D.M.; Kabasakal Tulunay, Y.

1979-01-01

The statistics of extreme values is used to investigate the statistical properties of the largest areas sunspots and photospheric faculae per solar cycle. The largest values of the synodic-solar-rotation mean areas of umbrae, whole spots and faculae, which have been recorded for nine solar cycles, are each shown to comply with the general form of the extreme value probability function. Empirical expressions are derived for the three extreme value populations from which the characteristic statistical parameters, namely the mode, median, mean and standard deviation, can be calculated for each population. These three extreme value populations are also used to find the expected ranges of the extreme areas in a group of solar cycles as a function of the number of cycles in the group. The extreme areas of umbrae and whole spots have a dispersion comparable to that found by Siscoe for the extreme values of sunspot number, whereas the extreme areas of faculae have a smaller dispersion which is comparable to that found by Siscoe for the largest geomagnetic storm per solar cycle. The expected range of the largest sunspot area per solar cycle for a group of one hundred cycles appears to be inconsistent with the existence of the prolonged periods of sunspot minima that have been inferred from the historical information on solar variability. This inconsistency supports the contention that there are temporal changes of solar-cycle statistics during protracted periods of sunspot minima (or maxima). Indeed, without such temporal changes, photospheric faculae should have been continually observable throughout the lifetime of the Sun. (orig.)

SOLAR CYCLE 24: CURIOUS CHANGES IN THE RELATIVE NUMBERS OF SUNSPOT GROUP TYPES

International Nuclear Information System (INIS)

Kilcik, A.; Yurchyshyn, V. B.; Ozguc, A.; Rozelot, J. P.

2014-01-01

Here, we analyze different sunspot group (SG) behaviors from the points of view of both the sunspot counts (SSCs) and the number of SGs, in four categories, for the time period of 1982 January-2014 May. These categories include data from simple (A and B), medium (C), large (D, E, and F), and decaying (H) SGs. We investigate temporal variations of all data sets used in this study and find the following results. (1) There is a very significant decrease in the large groups' SSCs and the number of SGs in solar cycle 24 (cycle 24) compared to cycles 21-23. (2) There is no strong variation in the decaying groups' data sets for the entire investigated time interval. (3) Medium group data show a gradual decrease for the last three cycles. (4) A significant decrease occurred in the small groups during solar cycle 23, while no strong changes show in the current cycle (cycle 24) compared to the previous ones. We confirm that the temporal behavior of all categories is quite different from cycle to cycle and it is especially flagrant in solar cycle 24. Thus, we argue that the reduced absolute number of the large SGs is largely, if not solely, responsible for the weak cycle 24. These results might be important for long-term space weather predictions to understand the rate of formation of different groups of sunspots during a solar cycle and the possible consequences for the long-term geomagnetic activity

On two populations of sunspot groups

International Nuclear Information System (INIS)

Kuklin, G.V.

1980-01-01

The principal component method was applied studying the sunspot groups distribution in respect to the maximum area for the individual 11-year cycles 12 to 19 (Lopez Arroyo and Lahulla, 1974) and for the years 1900 to 1964 (Mandrykina, 1974). The existence of two populations of sunspot groups is confirmed. The variations of the importance parameter q, which determines the population shares, in the 80-, 22- and 11-year cycles are considered. The obtained maximal area distributions for populations I and II are approximated by linear combination of logarithmic-normal distributions, the subpopulations Ia, Ib, Ic by the most probable maximum areas of 22, 298 and 90 mvh, respectively, and the subpopulations IIa, IIb, IIc by the most probable maximal areas of 6, 142 and 754 mvh, respectively. The characteristic distinction between populations I and II is apparently the magnetic structure of the groups belonging to them (bipolar and unipolar ones). (author)

Introduction to Time Series Modeling

CERN Document Server

Kitagawa, Genshiro

2010-01-01

In time series modeling, the behavior of a certain phenomenon is expressed in relation to the past values of itself and other covariates. Since many important phenomena in statistical analysis are actually time series and the identification of conditional distribution of the phenomenon is an essential part of the statistical modeling, it is very important and useful to learn fundamental methods of time series modeling. Illustrating how to build models for time series using basic methods, "Introduction to Time Series Modeling" covers numerous time series models and the various tools f

MEASUREMENTS OF THE ABSORPTION AND SCATTERING CROSS SECTIONS FOR THE INTERACTION OF SOLAR ACOUSTIC WAVES WITH SUNSPOTS

International Nuclear Information System (INIS)

Zhao, Hui; Chou, Dean-Yi

2016-01-01

The solar acoustic waves are modified by the interaction with sunspots. The interaction can be treated as a scattering problem: an incident wave propagating toward a sunspot is scattered by the sunspot into different modes. The absorption cross section and scattering cross section are two important parameters in the scattering problem. In this study, we use the wavefunction of the scattered wave, measured with a deconvolution method, to compute the absorption cross section σ ab and the scattering cross section σ sc for the radial order n = 0–5 for two sunspots, NOAA 11084 and NOAA 11092. In the computation of the cross sections, the random noise and dissipation in the measured acoustic power are corrected. For both σ ab and σ sc , the value of NOAA 11092 is greater than that of NOAA 11084, but their overall n dependence is similar: decreasing with n . The ratio of σ ab of NOAA 11092 to that of NOAA 11084 approximately equals the ratio of sunspot radii for all n , while the ratio of σ sc of the two sunspots is greater than the ratio of sunspot radii and increases with n . This suggests that σ ab is approximately proportional to the sunspot radius, while the dependence of σ sc on radius is faster than the linear increase.

MEASUREMENTS OF THE ABSORPTION AND SCATTERING CROSS SECTIONS FOR THE INTERACTION OF SOLAR ACOUSTIC WAVES WITH SUNSPOTS

Energy Technology Data Exchange (ETDEWEB)

Zhao, Hui [National Astronomical Observatories, Chinese Academy of Sciences, Beijing, 200012 (China); Chou, Dean-Yi, E-mail: chou@phys.nthu.edu.tw [Physics Department, National Tsing Hua University, Hsinchu, Taiwan (China)

2016-05-01

The solar acoustic waves are modified by the interaction with sunspots. The interaction can be treated as a scattering problem: an incident wave propagating toward a sunspot is scattered by the sunspot into different modes. The absorption cross section and scattering cross section are two important parameters in the scattering problem. In this study, we use the wavefunction of the scattered wave, measured with a deconvolution method, to compute the absorption cross section σ {sub ab} and the scattering cross section σ {sub sc} for the radial order n = 0–5 for two sunspots, NOAA 11084 and NOAA 11092. In the computation of the cross sections, the random noise and dissipation in the measured acoustic power are corrected. For both σ {sub ab} and σ {sub sc}, the value of NOAA 11092 is greater than that of NOAA 11084, but their overall n dependence is similar: decreasing with n . The ratio of σ {sub ab} of NOAA 11092 to that of NOAA 11084 approximately equals the ratio of sunspot radii for all n , while the ratio of σ {sub sc} of the two sunspots is greater than the ratio of sunspot radii and increases with n . This suggests that σ {sub ab} is approximately proportional to the sunspot radius, while the dependence of σ {sub sc} on radius is faster than the linear increase.

Frequently Occurring Reconnection Jets from Sunspot Light Bridges

Science.gov (United States)

Tian, Hui; Yurchyshyn, Vasyl; Peter, Hardi; Solanki, Sami K.; Young, Peter R.; Ni, Lei; Cao, Wenda; Ji, Kaifan; Zhu, Yingjie; Zhang, Jingwen; Samanta, Tanmoy; Song, Yongliang; He, Jiansen; Wang, Linghua; Chen, Yajie

2018-02-01

Solid evidence of magnetic reconnection is rarely reported within sunspots, the darkest regions with the strongest magnetic fields and lowest temperatures in the solar atmosphere. Using the world’s largest solar telescope, the 1.6 m Goode Solar Telescope, we detect prevalent reconnection through frequently occurring fine-scale jets in the Hα line wings at light bridges, the bright lanes that may divide the dark sunspot core into multiple parts. Many jets have an inverted Y-shape, shown by models to be typical of reconnection in a unipolar field environment. Simultaneous spectral imaging data from the Interface Region Imaging Spectrograph show that the reconnection drives bidirectional flows up to 200 km s‑1, and that the weakly ionized plasma is heated by at least an order of magnitude up to ∼80,000 K. Such highly dynamic reconnection jets and efficient heating should be properly accounted for in future modeling efforts of sunspots. Our observations also reveal that the surge-like activity previously reported above light bridges in some chromospheric passbands such as the Hα core has two components: the ever-present short surges likely to be related to the upward leakage of magnetoacoustic waves from the photosphere, and the occasionally occurring long and fast surges that are obviously caused by the intermittent reconnection jets.

SPECTROPOLARIMETRICALLY ACCURATE MAGNETOHYDROSTATIC SUNSPOT MODEL FOR FORWARD MODELING IN HELIOSEISMOLOGY

Energy Technology Data Exchange (ETDEWEB)

Przybylski, D.; Shelyag, S.; Cally, P. S. [Monash Center for Astrophysics, School of Mathematical Sciences, Monash University, Clayton, Victoria 3800 (Australia)

2015-07-01

We present a technique to construct a spectropolarimetrically accurate magnetohydrostatic model of a large-scale solar magnetic field concentration, mimicking a sunspot. Using the constructed model we perform a simulation of acoustic wave propagation, conversion, and absorption in the solar interior and photosphere with the sunspot embedded into it. With the 6173 Å magnetically sensitive photospheric absorption line of neutral iron, we calculate observable quantities such as continuum intensities, Doppler velocities, as well as the full Stokes vector for the simulation at various positions at the solar disk, and analyze the influence of non-locality of radiative transport in the solar photosphere on helioseismic measurements. Bisector shapes were used to perform multi-height observations. The differences in acoustic power at different heights within the line formation region at different positions at the solar disk were simulated and characterized. An increase in acoustic power in the simulated observations of the sunspot umbra away from the solar disk center was confirmed as the slow magnetoacoustic wave.

SPECTROPOLARIMETRICALLY ACCURATE MAGNETOHYDROSTATIC SUNSPOT MODEL FOR FORWARD MODELING IN HELIOSEISMOLOGY

International Nuclear Information System (INIS)

Przybylski, D.; Shelyag, S.; Cally, P. S.

2015-01-01

We present a technique to construct a spectropolarimetrically accurate magnetohydrostatic model of a large-scale solar magnetic field concentration, mimicking a sunspot. Using the constructed model we perform a simulation of acoustic wave propagation, conversion, and absorption in the solar interior and photosphere with the sunspot embedded into it. With the 6173 Å magnetically sensitive photospheric absorption line of neutral iron, we calculate observable quantities such as continuum intensities, Doppler velocities, as well as the full Stokes vector for the simulation at various positions at the solar disk, and analyze the influence of non-locality of radiative transport in the solar photosphere on helioseismic measurements. Bisector shapes were used to perform multi-height observations. The differences in acoustic power at different heights within the line formation region at different positions at the solar disk were simulated and characterized. An increase in acoustic power in the simulated observations of the sunspot umbra away from the solar disk center was confirmed as the slow magnetoacoustic wave

Estimation of the order of an autoregressive time series: a Bayesian approach

International Nuclear Information System (INIS)

Robb, L.J.

1980-01-01

Finite-order autoregressive models for time series are often used for prediction and other inferences. Given the order of the model, the parameters of the models can be estimated by least-squares, maximum-likelihood, or Yule-Walker method. The basic problem is estimating the order of the model. The problem of autoregressive order estimation is placed in a Bayesian framework. This approach illustrates how the Bayesian method brings the numerous aspects of the problem together into a coherent structure. A joint prior probability density is proposed for the order, the partial autocorrelation coefficients, and the variance; and the marginal posterior probability distribution for the order, given the data, is obtained. It is noted that the value with maximum posterior probability is the Bayes estimate of the order with respect to a particular loss function. The asymptotic posterior distribution of the order is also given. In conclusion, Wolfer's sunspot data as well as simulated data corresponding to several autoregressive models are analyzed according to Akaike's method and the Bayesian method. Both methods are observed to perform quite well, although the Bayesian method was clearly superior, in most cases

International Work-Conference on Time Series

CERN Document Server

Pomares, Héctor; Valenzuela, Olga

2017-01-01

This volume of selected and peer-reviewed contributions on the latest developments in time series analysis and forecasting updates the reader on topics such as analysis of irregularly sampled time series, multi-scale analysis of univariate and multivariate time series, linear and non-linear time series models, advanced time series forecasting methods, applications in time series analysis and forecasting, advanced methods and online learning in time series and high-dimensional and complex/big data time series. The contributions were originally presented at the International Work-Conference on Time Series, ITISE 2016, held in Granada, Spain, June 27-29, 2016. The series of ITISE conferences provides a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting.  It focuses on interdisciplinary and multidisciplinary rese arch encompassing the disciplines of comput...

Sunspot variation and selected associated phenomena: a look at solar cycle 21 and beyond

International Nuclear Information System (INIS)

Wilson, R.M.

1982-02-01

Solar sunspot cycles 8 through 21 are reviewed. Mean time intervals are calculated for maximum to maximum, minimum to minimum, minimum to maximum, and maximum to minimum phases for cycles 8 through 20 and 8 through 21. Simple cosine functions with a period of 132 years are compared to, and found to be representative of, the variation of smoothed sunspot numbers at solar maximum and minimum. A comparison of cycles 20 and 21 is given, leading to a projection for activity levels during the Spacelab 2 era (tentatively, November 1984). A prediction is made for cycle 22. Major flares are observed to peak several months subsequent to the solar maximum during cycle 21 and to be at minimum level several months after the solar minimum. Additional remarks are given for flares, gradual rise and fall radio events and 2800 MHz radio emission. Certain solar activity parameters, especially as they relate to the near term Spacelab 2 time frame are estimated

An essay on sunspots and solar flares

International Nuclear Information System (INIS)

Akasofu, S.-I.

1984-01-01

The presently prevailing theories of sunspots and solar flares rely on the hypothetical presence of magnetic flux tubes beneath the photosphere and the two subsequent hypotheses, their emergence above the photosphere and explosive magnetic reconnection, converting magnetic energy carried by the flux tubes for solar flare energy. In this paper, attention is paid to the fact that there are large-scale magnetic fields which divide the photosphere into positive and negative (line-of-sight) polarity regions and that they are likely to be more fundamental than sunspot fields, as emphasized most recently by McIntosh. A new phenomenological model of the sunspot pair formation is then constructed by considering an amplification process of these large-scale fields near their boundaries by shear flows, including localized vortex motions. The amplification results from a dynamo process associated with such vortex flows and the associated convergence flow in the large-scale fields. This dynamo process generates also some of the familiar ''force-free'' fields or the ''sheared'' magnetic fields in which the magnetic field-aligned currents are essential. Upward field-aligned currents generated by the dynamo process are carried by downward streaming electrons which are expected to be accelerated by an electric potential structure; a similar structure is responsible for accelerating auroral electrons in the magnetosphere. Depending on the magnetic field configuration and the shear flows, the current-carrying electrons precipitate into different geometrical patterns, causing circular flares, umbral flares, two-ribbon flares, etc. Thus, it is suggested that ''low temperature flares'' are directly driven by the photospheric dynamo process. (author)

From Networks to Time Series

Science.gov (United States)

Shimada, Yutaka; Ikeguchi, Tohru; Shigehara, Takaomi

2012-10-01

In this Letter, we propose a framework to transform a complex network to a time series. The transformation from complex networks to time series is realized by the classical multidimensional scaling. Applying the transformation method to a model proposed by Watts and Strogatz [Nature (London) 393, 440 (1998)], we show that ring lattices are transformed to periodic time series, small-world networks to noisy periodic time series, and random networks to random time series. We also show that these relationships are analytically held by using the circulant-matrix theory and the perturbation theory of linear operators. The results are generalized to several high-dimensional lattices.

Photospheric Origin of Three-minute Oscillations in a Sunspot

Energy Technology Data Exchange (ETDEWEB)

Chae, Jongchul; Lee, Jeongwoo; Cho, Kyuhyoun; Song, Donguk [Astronomy Program, Department of Physics and Astronomy, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826 (Korea, Republic of); Cho, Kyungsuk; Yurchyshyn, Vasyl [Korea Astronomy and Space Science Institute, 776 Daedeokdae-ro, Yuseong-gu, Daejeon 34055 (Korea, Republic of)

2017-02-10

The origin of the three-minute oscillations of intensity and velocity observed in the chromosphere of sunspot umbrae is still unclear. We investigated the spatio-spectral properties of the 3 minute oscillations of velocity in the photosphere of a sunspot umbra as well as those in the low chromosphere using the spectral data of the Ni i λ 5436, Fe i λ 5435, and Na i D{sub 2} λ 5890 lines taken by the Fast Imaging Solar Spectrograph of the 1.6 m New Solar Telescope at the Big Bear Solar Observatory. As a result, we found a local enhancement of the 3 minute oscillation power in the vicinities of a light bridge (LB) and numerous umbral dots (UDs) in the photosphere. These 3 minute oscillations occurred independently of the 5 minute oscillations. Through wavelet analysis, we determined the amplitudes and phases of the 3 minute oscillations at the formation heights of the spectral lines, and they were found to be consistent with the upwardly propagating slow magnetoacoustic waves in the photosphere with energy flux large enough to explain the chromospheric oscillations. Our results suggest that the 3 minute chromospheric oscillations in this sunspot may have been generated by magnetoconvection occurring in the LB and UDs.

Duality between Time Series and Networks

Science.gov (United States)

Campanharo, Andriana S. L. O.; Sirer, M. Irmak; Malmgren, R. Dean; Ramos, Fernando M.; Amaral, Luís A. Nunes.

2011-01-01

Studying the interaction between a system's components and the temporal evolution of the system are two common ways to uncover and characterize its internal workings. Recently, several maps from a time series to a network have been proposed with the intent of using network metrics to characterize time series. Although these maps demonstrate that different time series result in networks with distinct topological properties, it remains unclear how these topological properties relate to the original time series. Here, we propose a map from a time series to a network with an approximate inverse operation, making it possible to use network statistics to characterize time series and time series statistics to characterize networks. As a proof of concept, we generate an ensemble of time series ranging from periodic to random and confirm that application of the proposed map retains much of the information encoded in the original time series (or networks) after application of the map (or its inverse). Our results suggest that network analysis can be used to distinguish different dynamic regimes in time series and, perhaps more importantly, time series analysis can provide a powerful set of tools that augment the traditional network analysis toolkit to quantify networks in new and useful ways. PMID:21858093

TILT ANGLE AND FOOTPOINT SEPARATION OF SMALL AND LARGE BIPOLAR SUNSPOT REGIONS OBSERVED WITH HMI

International Nuclear Information System (INIS)

McClintock, B. H.; Norton, A. A.

2016-01-01

We investigate bipolar sunspot regions and how tilt angle and footpoint separation vary during emergence and decay. The Helioseismic and Magnetic Imager on board the Solar Dynamic Observatory collects data at a higher cadence than historical records and allows for a detailed analysis of regions over their lifetimes. We sample the umbral tilt angle, footpoint separation, and umbral area of 235 bipolar sunspot regions in Helioseismic and Magnetic Imager—Debrecen Data with an hourly cadence. We use the time when the umbral area peaks as time zero to distinguish between the emergence and decay periods of each region and we limit our analysis of tilt and separation behavior over time to within ±96 hr of time zero. Tilt angle evolution is distinctly different for regions with small (≈30 MSH), midsize (≈50 MSH), and large (≈110 MSH) maximum umbral areas, with 45 and 90 MSH being useful divisions for separating the groups. At the peak umbral area, we determine median tilt angles for small (7.°6), midsize (5.°9), and large (9.°3) regions. Within ±48 hr of the time of peak umbral area, large regions steadily increase in tilt angle, midsize regions are nearly constant, and small regions show evidence of negative tilt during emergence. A period of growth in footpoint separation occurs over a 72-hr period for all of the regions from roughly 40 to 70 Mm. The smallest bipoles (<9 MSH) are outliers in that they do not obey Joy's law and have a much smaller footpoint separation. We confirm the Muñoz-Jaramillo et al. results that the sunspots appear to be two distinct populations

HIGH RESOLUTION He i 10830 Å NARROW-BAND IMAGING OF AN M-CLASS FLARE. I. ANALYSIS OF SUNSPOT DYNAMICS DURING FLARING

Energy Technology Data Exchange (ETDEWEB)

Wang, Ya; Su, Yingna; Hong, Zhenxiang; Ji, Haisheng [Key Laboratory of DMSA, Purple Mountain Observatory, CAS, Nanjing, 210008 (China); Zeng, Zhicheng; Goode, Philip R.; Cao, Wenda [Big Bear Solar Observatory, 40386 North Shore Lane, Big Bear City, CA 92314 (United States); Ji, Kaifan [Yunnan Astronomical Observatories, Kunming 650011 (China)

2016-12-20

In this paper, we report our first-step results of high resolution He i 10830 Å narrow-band imaging (bandpass: 0.5 Å) of an M1.8 class two-ribbon flare on 2012 July 5. The flare was observed with the 1.6 m aperture New Solar Telescope at Big Bear Solar Observatory. For this unique data set, sunspot dynamics during flaring were analyzed for the first time. By directly imaging the upper chromosphere, running penumbral waves are clearly seen as an outward extension of umbral flashes; both take the form of absorption in the 10830 Å narrow-band images. From a space–time image made of a slit cutting across a flare ribbon and the sunspot, we find that the dark lanes for umbral flashes and penumbral waves are obviously broadened after the flare. The most prominent feature is the sudden appearance of an oscillating absorption strip inside the ribbon when it sweeps into the sunspot’s penumbral and umbral regions. During each oscillation, outwardly propagating umbral flashes and subsequent penumbral waves rush out into the inwardly sweeping ribbon, followed by a return of the absorption strip with similar speed. We tentatively explain the phenomena as the result of a sudden increase in the density of ortho-helium atoms in the area of the sunspot being excited by the flare’s extreme ultraviolet illumination. This explanation is based on the observation that 10830 Å absorption around the sunspot area gets enhanced during the flare. Nevertheless, questions are still open and we need further well-devised observations to investigate the behavior of sunspot dynamics during flares.

Long time series

DEFF Research Database (Denmark)

Hisdal, H.; Holmqvist, E.; Hyvärinen, V.

Awareness that emission of greenhouse gases will raise the global temperature and change the climate has led to studies trying to identify such changes in long-term climate and hydrologic time series. This report, written by the......Awareness that emission of greenhouse gases will raise the global temperature and change the climate has led to studies trying to identify such changes in long-term climate and hydrologic time series. This report, written by the...

Prediction of solar cycle 24 using fourier series analysis

International Nuclear Information System (INIS)

Khalid, M.; Sultana, M.; Zaidi, F.

2014-01-01

Predicting the behavior of solar activity has become very significant. It is due to its influence on Earth and the surrounding environment. Apt predictions of the amplitude and timing of the next solar cycle will aid in the estimation of the several results of Space Weather. In the past, many prediction procedures have been used and have been successful to various degrees in the field of solar activity forecast. In this study, Solar cycle 24 is forecasted by the Fourier series method. Comparative analysis has been made by auto regressive integrated moving averages method. From sources, January 2008 was the minimum preceding solar cycle 24, the amplitude and shape of solar cycle 24 is approximate on monthly number of sunspots. This forecast framework approximates a mean solar cycle 24, with the maximum appearing during May 2014 (+- 8 months), with most sunspot of 98 +- 10. Solar cycle 24 will be ending in June 2020 (+- 7 months). The difference between two consecutive peak values of solar cycles (i.e. solar cycle 23 and 24 ) is 165 months(+- 6 months). (author)

A Course in Time Series Analysis

CERN Document Server

Peña, Daniel; Tsay, Ruey S

2011-01-01

New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, a

Kolmogorov Space in Time Series Data

OpenAIRE

Kanjamapornkul, K.; Pinčák, R.

2016-01-01

We provide the proof that the space of time series data is a Kolmogorov space with $T_{0}$-separation axiom using the loop space of time series data. In our approach we define a cyclic coordinate of intrinsic time scale of time series data after empirical mode decomposition. A spinor field of time series data comes from the rotation of data around price and time axis by defining a new extradimension to time series data. We show that there exist hidden eight dimensions in Kolmogorov space for ...

On the Use of Running Trends as Summary Statistics for Univariate Time Series and Time Series Association

OpenAIRE

Trottini, Mario; Vigo, Isabel; Belda, Santiago

2015-01-01

Given a time series, running trends analysis (RTA) involves evaluating least squares trends over overlapping time windows of L consecutive time points, with overlap by all but one observation. This produces a new series called the “running trends series,” which is used as summary statistics of the original series for further analysis. In recent years, RTA has been widely used in climate applied research as summary statistics for time series and time series association. There is no doubt that ...

The Formation of a Sunspot Penumbra Sector in Active Region NOAA 12574

Science.gov (United States)

Li, Qiaoling; Yan, Xiaoli; Wang, Jincheng; Kong, DeFang; Xue, Zhike; Yang, Liheng; Cao, Wenda

2018-04-01

We present a particular case of the formation of a penumbra sector around a developing sunspot in the active region NOAA 12574 on 2016 August 11 by using the high-resolution data observed by the New Solar Telescope at the Big Bear Solar Observatory and the data acquired by the Helioseismic and Magnetic Imager and the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory satellite. Before the new penumbra sector formed, the developing sunspot already had two umbrae with some penumbral filaments. The penumbra sector gradually formed at the junction of two umbrae. We found that the formation of the penumbra sector can be divided into two stages. First, during the initial stage of penumbral formation, the region where the penumbra sector formed always appeared blueshifted in a Dopplergram. The area, mean transverse magnetic field strength, and total magnetic flux of the umbra and penumbra sector all increased with time. The initial penumbral formation was associated with magnetic emergence. Second, when the penumbra sector appeared, the magnetic flux and area of the penumbra sector increased after the umbra’s magnetic flux and area decreased. These results indicate that the umbra provided magnetic flux for penumbral development after the penumbra sector appeared. We also found that the newly formed penumbra sector was associated with sunspot rotation. Based on these findings, we suggest that the penumbra sector was the result of the emerging flux that was trapped in the photosphere at the initial stage of penumbral formation, and when the rudimentary penumbra formed, the penumbra sector developed at the cost of the umbra.

Long-term variations in the geomagnetic activity level Part II: Ascending phases of sunspot cycles

Directory of Open Access Journals (Sweden)

V. Mussino

1994-08-01

Full Text Available Monthly averages of the Helsinki Ak-values have been reduced to the equivalent aa-indices to extend the aa-data set back to 1844. A periodicity of about five cycles was found for the correlation coefficient (r between geomagnetic indices and sunspot numbers for the ascending phases of sunspot cycles 9 to 22, confirming previous findings based on a minor number of sunspot cycles. The result is useful to researchers in topics related to solar-terrestrial physics, particularly for the interpretation of long-term trends in geomagnetic activity during the past, and to forecast geomagnetic activity levels in the future.

Multiple Indicator Stationary Time Series Models.

Science.gov (United States)

Sivo, Stephen A.

2001-01-01

Discusses the propriety and practical advantages of specifying multivariate time series models in the context of structural equation modeling for time series and longitudinal panel data. For time series data, the multiple indicator model specification improves on classical time series analysis. For panel data, the multiple indicator model…

Observations of Running Penumbral Waves Emerging in a Sunspot

Science.gov (United States)

Priya, T. G.; Wenda, Cao; Jiangtao, Su; Jie, Chen; Xinjie, Mao; Yuanyong, Deng; Robert, Erdélyi

2018-01-01

We present results from the investigation of 5 minute umbral oscillations in a single-polarity sunspot of active region NOAA 12132. The spectra of TiO, Hα, and 304 Å are used for corresponding atmospheric heights from the photosphere to lower corona. Power spectrum analysis at the formation height of Hα – 0.6 Å to the Hα center resulted in the detection of 5 minute oscillation signals in intensity interpreted as running waves outside the umbral center, mostly with vertical magnetic field inclination >15°. A phase-speed filter is used to extract the running wave signals with speed v ph > 4 km s‑1, from the time series of Hα – 0.4 Å images, and found twenty-four 3 minute umbral oscillatory events in a duration of one hour. Interestingly, the initial emergence of the 3 minute umbral oscillatory events are noticed closer to or at umbral boundaries. These 3 minute umbral oscillatory events are observed for the first time as propagating from a fraction of preceding running penumbral waves (RPWs). These fractional wavefronts rapidly separate from RPWs and move toward the umbral center, wherein they expand radially outwards suggesting the beginning of a new umbral oscillatory event. We found that most of these umbral oscillatory events develop further into RPWs. We speculate that the waveguides of running waves are twisted in spiral structures and hence the wavefronts are first seen at high latitudes of umbral boundaries and later at lower latitudes of the umbral center.

Parametric Identification of Solar Series based on an Adaptive ...

Indian Academy of Sciences (India)

R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

Department of Computer Science, University of Extremadura, Campus ... els, applying it to the case of sunspot series. .... inspired on the concept of artificial evolution (Goldberg 1989) (Rechenberg 1973) and ... benchmark when na = 5. ... clusion is that an accurate tuning for general purposes could be from na = 5, although.

REGULARITY OF THE NORTH–SOUTH ASYMMETRY OF SOLAR ACTIVITY: REVISITED

International Nuclear Information System (INIS)

Zhang, J.; Feng, W.

2015-01-01

Extended time series of Solar Activity Indices (ESAI) extended the Greenwich series of sunspot area from the year 1874 back to 1821. The ESAI's yearly sunspot area in the northern and southern hemispheres from 1821 to 2013 is utilized to investigate characteristics of the north–south hemispherical asymmetry of sunspot activity. Periodical behavior of about 12 solar cycles is also confirmed from the ESAI data set to exist in dominant hemispheres, linear regression lines of yearly asymmetry values, and cumulative counts of yearly sunspot areas in the hemispheres for solar cycles. The period is also inferred to appear in both the cumulative difference in the yearly sunspot areas in the hemispheres over the entire time interval and in its statistical Student's t-test. The hemispherical bias of sunspot activity should be regarded as an impossible stochastic phenomenon over a long time period

Time Series Momentum

DEFF Research Database (Denmark)

Moskowitz, Tobias J.; Ooi, Yao Hua; Heje Pedersen, Lasse

2012-01-01

We document significant “time series momentum” in equity index, currency, commodity, and bond futures for each of the 58 liquid instruments we consider. We find persistence in returns for one to 12 months that partially reverses over longer horizons, consistent with sentiment theories of initial...... under-reaction and delayed over-reaction. A diversified portfolio of time series momentum strategies across all asset classes delivers substantial abnormal returns with little exposure to standard asset pricing factors and performs best during extreme markets. Examining the trading activities...

International Work-Conference on Time Series

CERN Document Server

Pomares, Héctor

2016-01-01

This volume presents selected peer-reviewed contributions from The International Work-Conference on Time Series, ITISE 2015, held in Granada, Spain, July 1-3, 2015. It discusses topics in time series analysis and forecasting, advanced methods and online learning in time series, high-dimensional and complex/big data time series as well as forecasting in real problems. The International Work-Conferences on Time Series (ITISE) provide a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting. It focuses on interdisciplinary and multidisciplinary research encompassing the disciplines of computer science, mathematics, statistics and econometrics.

Stochastic models for time series

CERN Document Server

Doukhan, Paul

2018-01-01

This book presents essential tools for modelling non-linear time series. The first part of the book describes the main standard tools of probability and statistics that directly apply to the time series context to obtain a wide range of modelling possibilities. Functional estimation and bootstrap are discussed, and stationarity is reviewed. The second part describes a number of tools from Gaussian chaos and proposes a tour of linear time series models. It goes on to address nonlinearity from polynomial or chaotic models for which explicit expansions are available, then turns to Markov and non-Markov linear models and discusses Bernoulli shifts time series models. Finally, the volume focuses on the limit theory, starting with the ergodic theorem, which is seen as the first step for statistics of time series. It defines the distributional range to obtain generic tools for limit theory under long or short-range dependences (LRD/SRD) and explains examples of LRD behaviours. More general techniques (central limit ...

Absorption of acoustic waves by sunspots. II - Resonance absorption in axisymmetric fibril models

Science.gov (United States)

Rosenthal, C. S.

1992-01-01

Analytical calculations of acoustic waves scattered by sunspots which concentrate on the absorption at the magnetohydrodynamic Alfven resonance are extended to the case of a flux-tube embedded in a uniform atmosphere. The model is based on a flux-tubes of varying radius that are highly structured, translationally invariant, and axisymmetric. The absorbed fractional energy is determined for different flux-densities and subphotospheric locations with attention given to the effects of twist. When the flux is highly concentrated into annuli efficient absorption is possible even when the mean magnetic flux density is low. The model demonstrates low absorption at low azimuthal orders even in the presence of twist which generally increases the range of wave numbers over which efficient absorption can occur. Resonance absorption is concluded to be an efficient mechanism in monolithic sunspots, fibril sunspots, and plage fields.

On the determination of heliographic positions and rotation velocities of sunspots. Pt. 2

International Nuclear Information System (INIS)

Balthasar, H.

1983-01-01

Using sunspot positions of small sunspots observed at Debrecen and Locarno as well as positions of recurrent sunspots taken from the Greenwich Photoheliographic Results (1940-1976) the influence of the Wilson depression on the rotation velocities was investigated. It was found that the Wilson depression can be determined by minimizing errors of the rotation velocities or minimizing the differences of rotation velocities determined from disk passages and central meridian passages. The Wilson depressions found were between 765 km and 2500 km for the first sample while they were between 0 km and several 1000 km for the second sample. The averaged Wilson depression for the second sample is between 500 km and 965 km depending on the reduction method. A dependence of the Wilson depression on the age of the spots investigated seems not to exist. (orig.)

Graphical Data Analysis on the Circle: Wrap-Around Time Series Plots for (Interrupted) Time Series Designs.

Science.gov (United States)

Rodgers, Joseph Lee; Beasley, William Howard; Schuelke, Matthew

2014-01-01

Many data structures, particularly time series data, are naturally seasonal, cyclical, or otherwise circular. Past graphical methods for time series have focused on linear plots. In this article, we move graphical analysis onto the circle. We focus on 2 particular methods, one old and one new. Rose diagrams are circular histograms and can be produced in several different forms using the RRose software system. In addition, we propose, develop, illustrate, and provide software support for a new circular graphical method, called Wrap-Around Time Series Plots (WATS Plots), which is a graphical method useful to support time series analyses in general but in particular in relation to interrupted time series designs. We illustrate the use of WATS Plots with an interrupted time series design evaluating the effect of the Oklahoma City bombing on birthrates in Oklahoma County during the 10 years surrounding the bombing of the Murrah Building in Oklahoma City. We compare WATS Plots with linear time series representations and overlay them with smoothing and error bands. Each method is shown to have advantages in relation to the other; in our example, the WATS Plots more clearly show the existence and effect size of the fertility differential.

Application of the Markov chain approximation to the sunspot observations

International Nuclear Information System (INIS)

Onal, M.

1988-01-01

The positions of the 13,588 sunspot groups observed during the cycle of 1950-1960 at the Istanbul University Observatory have been corrected for the effect of differential rotation. The evolution probability of a sunspot group to the other one in the same region have been determined. By using the Markov chain approximation, the types of these groups and their transition probabilities during the following activity cycle (1950-1960), and the concentration of active regions during 1950-1960 have been estimated. The transition probabilities from the observations of the activity cycle 1960-1970 have been compared with the predicted transition probabilities and a good correlation has been noted. 5 refs.; 2 tabs

MODELING THE CHROMOSPHERE OF A SUNSPOT AND THE QUIET SUN

Energy Technology Data Exchange (ETDEWEB)

Avrett, E.; Tian, H. [Smithsonian Astrophysical Observatory, Cambridge, MA 02138 (United States); Landi, E. [Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, MI 48109 (United States); Curdt, W. [Max Planck Institut für Sonnensystemfoschung, Goettingen (Germany); Wülser, J.-P. [Lockheed Martin Advanced Techonology Center (United States)

2015-10-01

Semiempirical atmospheric modeling attempts to match an observed spectrum by finding the temperature distribution and other physical parameters along the line of sight through the emitting region such that the calculated spectrum agrees with the observed one. In this paper we take the observed spectrum of a sunspot and the quiet Sun in the EUV wavelength range 668–1475 Å from the 2001 SUMER atlas of Curdt et al. to determine models of the two atmospheric regions, extending from the photosphere through the overlying chromosphere into the transition region. We solve the coupled statistical equilibrium and optically thick radiative transfer equations for a set of 32 atoms and ions. The atoms that are part of molecules are treated separately, and are excluded from the atomic abundances and atomic opacities. We compare the Mg ii k line profile observations from the Interface Region Imaging Spectrograph with the profiles calculated from the two models. The calculated profiles for the sunspot are substantially lower than the observed ones, based on the SUMER models. The only way we have found to raise the calculated Mg ii lines to agree with the observations is to introduce illumination of the sunspot from the surrounding active region.

Time Series with Long Memory

OpenAIRE

西埜, 晴久

2004-01-01

The paper investigates an application of long-memory processes to economic time series. We show properties of long-memory processes, which are motivated to model a long-memory phenomenon in economic time series. An FARIMA model is described as an example of long-memory model in statistical terms. The paper explains basic limit theorems and estimation methods for long-memory processes in order to apply long-memory models to economic time series.

Visibility Graph Based Time Series Analysis.

Science.gov (United States)

Stephen, Mutua; Gu, Changgui; Yang, Huijie

2015-01-01

Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it's microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq) and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks.

Visibility Graph Based Time Series Analysis.

Directory of Open Access Journals (Sweden)

Mutua Stephen

Full Text Available Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it's microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks.

Response of Solar Irradiance to Sunspot-area Variations

Science.gov (United States)

Dudok de Wit, T.; Kopp, G.; Shapiro, A.; Witzke, V.; Kretzschmar, M.

2018-02-01

One of the important open questions in solar irradiance studies is whether long-term variability (i.e., on timescales of years and beyond) can be reconstructed by means of models that describe short-term variability (i.e., days) using solar proxies as inputs. Preminger & Walton showed that the relationship between spectral solar irradiance and proxies of magnetic-flux emergence, such as the daily sunspot area, can be described in the framework of linear system theory by means of the impulse response. We significantly refine that empirical model by removing spurious solar-rotational effects and by including an additional term that captures long-term variations. Our results show that long-term variability cannot be reconstructed from the short-term response of the spectral irradiance, which questions the extension of solar proxy models to these timescales. In addition, we find that the solar response is nonlinear in a way that cannot be corrected simply by applying a rescaling to a sunspot area.

CHROMOSPHERIC SUNSPOTS IN THE MILLIMETER RANGE AS OBSERVED BY THE NOBEYAMA RADIOHELIOGRAPH

Energy Technology Data Exchange (ETDEWEB)

Iwai, Kazumasa [National Institute of Information and Communications Technology, Koganei 184-8795, Tokyo (Japan); Koshiishi, Hideki [Aerospace Research and Development Directorate, Japan Aerospace Exploration Agency, Tsukuba 305-8505 (Japan); Shibasaki, Kiyoto [Nobeyama Solar Radio Observatory, National Astronomical Observatory of Japan, Minamimaki, Nagano 384-1305 (Japan); Nozawa, Satoshi; Miyawaki, Shun; Yoneya, Takuro, E-mail: kazumasa.iwai@nict.go.jp [Department of Science, Ibaraki University, Mito, Ibaraki 310-8512 (Japan)

2016-01-10

We investigate the upper chromosphere and the transition region of the sunspot umbra using the radio brightness temperature at 34 GHz (corresponding to 8.8 mm observations) as observed by the Nobeyama Radioheliograph (NoRH). Radio free–free emission in the longer millimeter range is generated around the transition region, and its brightness temperature yields the region's temperature and density distribution. We use the NoRH data at 34 GHz by applying the Steer-CLEAN image synthesis. These data and the analysis method enable us to investigate the chromospheric structures in the longer millimeter range with high spatial resolution and sufficient visibilities. We also perform simultaneous observations of one sunspot using the NoRH and the Nobeyama 45 m telescope operating at 115 GHz. We determine that 115 GHz emission mainly originates from the lower chromosphere while 34 GHz emission mainly originates from the upper chromosphere and transition region. These observational results are consistent with the radio emission characteristics estimated from current atmospheric models of the chromosphere. On the other hand, the observed brightness temperature of the umbral region is almost the same as that of the quiet region. This result is inconsistent with current sunspot models, which predict a considerably higher brightness temperature of the sunspot umbra at 34 GHz. This inconsistency suggests that the temperature of the region at which the 34 GHz radio emission becomes optically thick should be lower than that predicted by the models.

Network structure of multivariate time series.

Science.gov (United States)

Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

2015-10-21

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

What marketing scholars should know about time series analysis : time series applications in marketing

NARCIS (Netherlands)

Horváth, Csilla; Kornelis, Marcel; Leeflang, Peter S.H.

2002-01-01

In this review, we give a comprehensive summary of time series techniques in marketing, and discuss a variety of time series analysis (TSA) techniques and models. We classify them in the sets (i) univariate TSA, (ii) multivariate TSA, and (iii) multiple TSA. We provide relevant marketing

Data mining in time series databases

CERN Document Server

Kandel, Abraham; Bunke, Horst

2004-01-01

Adding the time dimension to real-world databases produces Time SeriesDatabases (TSDB) and introduces new aspects and difficulties to datamining and knowledge discovery. This book covers the state-of-the-artmethodology for mining time series databases. The novel data miningmethods presented in the book include techniques for efficientsegmentation, indexing, and classification of noisy and dynamic timeseries. A graph-based method for anomaly detection in time series isdescribed and the book also studies the implications of a novel andpotentially useful representation of time series as strings. Theproblem of detecting changes in data mining models that are inducedfrom temporal databases is additionally discussed.

Models for dependent time series

CERN Document Server

Tunnicliffe Wilson, Granville; Haywood, John

2015-01-01

Models for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled. Whether you work in the economic, physical, or life sciences, the book shows you how to draw meaningful, applicable, and statistically valid conclusions from multivariate (or vector) time series data.The first four chapters discuss the two main pillars of the subject that have been developed over the last 60 years: vector autoregressive modeling and multivariate spectral analysis. These chapters provide the foundational mater

Visual time series analysis

DEFF Research Database (Denmark)

Fischer, Paul; Hilbert, Astrid

2012-01-01

We introduce a platform which supplies an easy-to-handle, interactive, extendable, and fast analysis tool for time series analysis. In contrast to other software suits like Maple, Matlab, or R, which use a command-line-like interface and where the user has to memorize/look-up the appropriate...... commands, our application is select-and-click-driven. It allows to derive many different sequences of deviations for a given time series and to visualize them in different ways in order to judge their expressive power and to reuse the procedure found. For many transformations or model-ts, the user may...... choose between manual and automated parameter selection. The user can dene new transformations and add them to the system. The application contains efficient implementations of advanced and recent techniques for time series analysis including techniques related to extreme value analysis and filtering...

On Solar Granulations, Limb Darkening, and Sunspots: Brief Insights in Remembrance of Father Angelo Secchi

Directory of Open Access Journals (Sweden)

Robitaille P.-M.

2011-07-01

support the idea of a condensed Sun. With respect to sunspots, the decrease in emis- sivity with increasing magnetic field strength lends powerful observational support to the idea that these structures are comprised of liquid metallic hydrogen. In this model, the inter-atomic lattice dimensions within sunspots are reduced. This increases the den- sity and metallic character relative to photospheric material, while at the same time decreasing emissivity. Metals are well known to have lowered directional emissivities with respect to non-metals. Greater metallicity produces lower emissivity. The idea that density is increased within sunspots is supported by helioseismology. Thus, a liq- uid metallic hydrogen model brings with it many advantages in understanding both the emissivity of the solar surface and its vast array of structures. These realities reveal that Father Secchi, like Herbert Spencer and Gustav Kirchhoff, was correct in his insistence that condensed matter is present on the photosphere. Secchi and his contemporaries were well aware that gases are unable to impart the observed structure.

A Review of Subsequence Time Series Clustering

Directory of Open Access Journals (Sweden)

Seyedjamal Zolhavarieh

2014-01-01

Full Text Available Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.

A review of subsequence time series clustering.

Science.gov (United States)

Zolhavarieh, Seyedjamal; Aghabozorgi, Saeed; Teh, Ying Wah

2014-01-01

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.

A Review of Subsequence Time Series Clustering

Science.gov (United States)

Teh, Ying Wah

2014-01-01

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies. PMID:25140332

Analysis of Heavy-Tailed Time Series

DEFF Research Database (Denmark)

Xie, Xiaolei

This thesis is about analysis of heavy-tailed time series. We discuss tail properties of real-world equity return series and investigate the possibility that a single tail index is shared by all return series of actively traded equities in a market. Conditions for this hypothesis to be true...... are identified. We study the eigenvalues and eigenvectors of sample covariance and sample auto-covariance matrices of multivariate heavy-tailed time series, and particularly for time series with very high dimensions. Asymptotic approximations of the eigenvalues and eigenvectors of such matrices are found...... and expressed in terms of the parameters of the dependence structure, among others. Furthermore, we study an importance sampling method for estimating rare-event probabilities of multivariate heavy-tailed time series generated by matrix recursion. We show that the proposed algorithm is efficient in the sense...

Adaptive time-variant models for fuzzy-time-series forecasting.

Science.gov (United States)

Wong, Wai-Keung; Bai, Enjian; Chu, Alice Wai-Ching

2010-12-01

A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.

Molecular Diagnostics of the Internal Structure of Starspots and Sunspots

Science.gov (United States)

Afram, N.; Berdyugina, S. V.; Fluri, D. M.; Solanki, S. K.; Lagg, A.; Petit, P.; Arnaud, J.

2006-12-01

We have analyzed the usefulness of molecules as a diagnostic tool for studying solar and stellar magnetism with the molecular Zeeman and Paschen-Back effects. In the first part we concentrate on molecules that are observed in sunspots such as MgH and TiO. We present calculated molecular line profiles obtained by assuming magnetic fields of 2-3 kG and compare these synthetic Stokes profiles with spectro-polarimetric observations in sunspots. The good agreement between the theory and observations allows us to turn our attention in the second part to starspots to gain insight into their internal structure. We investigate the temperature range in which the selected molecules can serve as indicators for magnetic fields on highly active cool stars and compare synthetic Stokes profiles with our recent observations.

Time Series Analysis and Forecasting by Example

CERN Document Server

Bisgaard, Soren

2011-01-01

An intuition-based approach enables you to master time series analysis with ease Time Series Analysis and Forecasting by Example provides the fundamental techniques in time series analysis using various examples. By introducing necessary theory through examples that showcase the discussed topics, the authors successfully help readers develop an intuitive understanding of seemingly complicated time series models and their implications. The book presents methodologies for time series analysis in a simplified, example-based approach. Using graphics, the authors discuss each presented example in

Time series with tailored nonlinearities

Science.gov (United States)

Räth, C.; Laut, I.

2015-10-01

It is demonstrated how to generate time series with tailored nonlinearities by inducing well-defined constraints on the Fourier phases. Correlations between the phase information of adjacent phases and (static and dynamic) measures of nonlinearities are established and their origin is explained. By applying a set of simple constraints on the phases of an originally linear and uncorrelated Gaussian time series, the observed scaling behavior of the intensity distribution of empirical time series can be reproduced. The power law character of the intensity distributions being typical for, e.g., turbulence and financial data can thus be explained in terms of phase correlations.

Records of auroral candidates and sunspots in Rikkokushi, chronicles of ancient Japan from early 7th century to 887

Science.gov (United States)

Hayakawa, Hisashi; Iwahashi, Kiyomi; Tamazawa, Harufumi; Ebihara, Yusuke; Kawamura, Akito Davis; Isobe, Hiroaki; Namiki, Katsuko; Shibata, Kazunari

2017-12-01

We present the results of the surveys on sunspots and auroral candidates in Rikkokushi, Japanese official histories from the early 7th century to 887, to review the solar and auroral activities. In total, we found one sunspot record and 13 auroral candidates in Rikkokushi. We then examine the records of the sunspots and auroral candidates, compare the auroral candidates with the lunar phase to estimate their reliability, and compare the records of the sunspots and auroral candidates with the contemporary total solar irradiance reconstructed from radioisotope data. We also identify the locations of the observational sites to review possible equatorward expansion of the auroral oval. These discussions suggest a major gap in auroral candidates from the late 7th to early 9th centuries, which includes the candidate of the grand minimum reconstructed from the radioisotope data, a similar tendency as the distributions of sunspot records in contemporary China, and a relatively high magnetic latitude of observational sites with a higher potential for observing aurorae more frequently than at present.

Clustering of financial time series

Science.gov (United States)

D'Urso, Pierpaolo; Cappelli, Carmela; Di Lallo, Dario; Massari, Riccardo

2013-05-01

This paper addresses the topic of classifying financial time series in a fuzzy framework proposing two fuzzy clustering models both based on GARCH models. In general clustering of financial time series, due to their peculiar features, needs the definition of suitable distance measures. At this aim, the first fuzzy clustering model exploits the autoregressive representation of GARCH models and employs, in the framework of a partitioning around medoids algorithm, the classical autoregressive metric. The second fuzzy clustering model, also based on partitioning around medoids algorithm, uses the Caiado distance, a Mahalanobis-like distance, based on estimated GARCH parameters and covariances that takes into account the information about the volatility structure of time series. In order to illustrate the merits of the proposed fuzzy approaches an application to the problem of classifying 29 time series of Euro exchange rates against international currencies is presented and discussed, also comparing the fuzzy models with their crisp version.

Occurrences of flares with type II and IV radio events in interacting sunspot groups in the course of revolutions

International Nuclear Information System (INIS)

Klimes, J.; Krivsky, L.

1984-01-01

Using data from 11-year solar cycle No. 20, it was found that flares with type II radio bursts are more than twice as frequent and flares with type IV bursts nearly twice as frequent in sunspot groups which developed close to each other or which merged in the course of revolutions than in isolated sunspot groups. With both types the occurrence of these flares is concentrated in the revolution of the so-called sunspot group interaction (their approximation, merging). (author)

Historical evidence concerning the Sun: interpretation of sunspot records during the telescopic and pretelescopic eras

International Nuclear Information System (INIS)

Stephenson, F.R.

1990-01-01

The value of sunspot observations in investigating solar activity trends - mainly on the centennial to millennial timescale - is considered in some detail. It is shown that although observations made since the mid-eighteenth century are in general very reliable indicators of solar activity, older data are of dubious quality and utility. The sunspot record in both the pretelescopic and early telescopic periods appears to be confused by serious data artefacts. (author)

Data Mining Smart Energy Time Series

Directory of Open Access Journals (Sweden)

Janina POPEANGA

2015-07-01

Full Text Available With the advent of smart metering technology the amount of energy data will increase significantly and utilities industry will have to face another big challenge - to find relationships within time-series data and even more - to analyze such huge numbers of time series to find useful patterns and trends with fast or even real-time response. This study makes a small review of the literature in the field, trying to demonstrate how essential is the application of data mining techniques in the time series to make the best use of this large quantity of data, despite all the difficulties. Also, the most important Time Series Data Mining techniques are presented, highlighting their applicability in the energy domain.

Predicting chaotic time series

International Nuclear Information System (INIS)

Farmer, J.D.; Sidorowich, J.J.

1987-01-01

We present a forecasting technique for chaotic data. After embedding a time series in a state space using delay coordinates, we ''learn'' the induced nonlinear mapping using local approximation. This allows us to make short-term predictions of the future behavior of a time series, using information based only on past values. We present an error estimate for this technique, and demonstrate its effectiveness by applying it to several examples, including data from the Mackey-Glass delay differential equation, Rayleigh-Benard convection, and Taylor-Couette flow

Successive X-class Flares and Coronal Mass Ejections Driven by Shearing Motion and Sunspot Rotation in Active Region NOAA 12673

Science.gov (United States)

Yan, X. L.; Wang, J. C.; Pan, G. M.; Kong, D. F.; Xue, Z. K.; Yang, L. H.; Li, Q. L.; Feng, X. S.

2018-03-01

We present a clear case study on the occurrence of two successive X-class flares, including a decade-class flare (X9.3) and two coronal mass ejections (CMEs) triggered by shearing motion and sunspot rotation in active region NOAA 12673 on 2017 September 6. A shearing motion between the main sunspots with opposite polarities began on September 5 and lasted even after the second X-class flare on September 6. Moreover, the main sunspot with negative polarity rotated around its umbral center, and another main sunspot with positive polarity also exhibited a slow rotation. The sunspot with negative polarity at the northwest of the active region also began to rotate counterclockwise before the onset of the first X-class flare, which is related to the formation of the second S-shaped structure. The successive formation and eruption of two S-shaped structures were closely related to the counterclockwise rotation of the three sunspots. The existence of a flux rope is found prior to the onset of two flares by using nonlinear force-free field extrapolation based on the vector magnetograms observed by Solar Dynamics Observatory/Helioseismic and Magnetic Image. The first flux rope corresponds to the first S-shaped structures mentioned above. The second S-shaped structure was formed after the eruption of the first flux rope. These results suggest that a shearing motion and sunspot rotation play an important role in the buildup of the free energy and the formation of flux ropes in the corona that produces solar flares and CMEs.

On the correlation of longitudinal and latitudinal motions of sunspots

International Nuclear Information System (INIS)

Gilman, P.A.

1984-01-01

Using new measurements of positions of individual sunspots and sunspot groups obtained from 62 years of the Mt. Wilson white-light plate collection, we have recomputed the correlation between longitude and latitude motion. Our results for groups are similar to those of Ward (1965a) computed from the Greenwich record, but for individual spots the covariance is reduced by a factor of about 3 from the Ward values, though still of the same sign and still statistically significant. We conclude that there is a real correlation between longitude and latitude movement of individual spots, implying angular momentum transport toward the equator as inferred by Ward. The two thirds reduction in the covariance for individual spots as opposed to groups is probably due to certain properties of spot groups, as first pointed out in an unpublished manuscript by Leighton. (orig.)

SOHO sees right through the Sun, and finds sunspots on the far side

Science.gov (United States)

2000-03-01

front side about 6 seconds earlier than equivalent waves from sunspot-free regions, in a total travel time of about 3 hours. The change in speed becomes evident when sound waves shuttling back and forth get out of step with one another. MDI data for 28-29 March 1998 revealed, on the far side, a sunspot group that was not plainly visible on the near side until ten days later. Observations for 24 hours were more than sufficient to detect the sunspots, which means that routine monitoring is a realistic possibility. "The far-side sunspots are a good example of why this spacecraft is so exciting to work with," said Bernhard Fleck, ESA's project scientist for SOHO. "We can make a completely new discovery in fundamental solar physics, and immediately think of applying it to the practical task of monitoring the daily activity of the Sun and predicting its effects on the Earth." The SOHO project is an international cooperation between the European Space Agency (ESA) and NASA. The spacecraft was built in Europe for ESA and equipped with instruments by teams of scientists in Europe and the USA. NASA launched SOHO in December 1995, and in 1998 ESA and NASA decided to extend its highly successful operations until 2003.

Measuring multiscaling in financial time-series

International Nuclear Information System (INIS)

Buonocore, R.J.; Aste, T.; Di Matteo, T.

2016-01-01

We discuss the origin of multiscaling in financial time-series and investigate how to best quantify it. Our methodology consists in separating the different sources of measured multifractality by analyzing the multi/uni-scaling behavior of synthetic time-series with known properties. We use the results from the synthetic time-series to interpret the measure of multifractality of real log-returns time-series. The main finding is that the aggregation horizon of the returns can introduce a strong bias effect on the measure of multifractality. This effect can become especially important when returns distributions have power law tails with exponents in the range (2, 5). We discuss the right aggregation horizon to mitigate this bias.

Time averaging, ageing and delay analysis of financial time series

Science.gov (United States)

Cherstvy, Andrey G.; Vinod, Deepak; Aghion, Erez; Chechkin, Aleksei V.; Metzler, Ralf

2017-06-01

We introduce three strategies for the analysis of financial time series based on time averaged observables. These comprise the time averaged mean squared displacement (MSD) as well as the ageing and delay time methods for varying fractions of the financial time series. We explore these concepts via statistical analysis of historic time series for several Dow Jones Industrial indices for the period from the 1960s to 2015. Remarkably, we discover a simple universal law for the delay time averaged MSD. The observed features of the financial time series dynamics agree well with our analytical results for the time averaged measurables for geometric Brownian motion, underlying the famed Black-Scholes-Merton model. The concepts we promote here are shown to be useful for financial data analysis and enable one to unveil new universal features of stock market dynamics.

Applied time series analysis

CERN Document Server

Woodward, Wayne A; Elliott, Alan C

2011-01-01

""There is scarcely a standard technique that the reader will find left out … this book is highly recommended for those requiring a ready introduction to applicable methods in time series and serves as a useful resource for pedagogical purposes.""-International Statistical Review (2014), 82""Current time series theory for practice is well summarized in this book.""-Emmanuel Parzen, Texas A&M University""What an extraordinary range of topics covered, all very insightfully. I like [the authors'] innovations very much, such as the AR factor table.""-David Findley, U.S. Census Bureau (retired)""…

Essential features of long-term changes of areas and diameters of sunspot groups in solar activity cycles 12-24

Science.gov (United States)

Efimenko, V. M.; Lozitsky, V. G.

2018-06-01

We analyze the Greenwich catalog data on areas of sunspot groups of last thirteen solar cycles. Various parameters of sunspots are considered, namely: average monthly smoothed areas, maximum area for each year and equivalent diameters of groups of sunspots. The first parameter shows an exceptional power of the 19th cycle of solar activity, which appears here more contrastively than in the numbers of spots (that is, in Wolf's numbers). It was found that in the maximum areas of sunspot groups for a year there is a unique phenomenon: a short and high jump in the 18th cycle (in 1946-1947) that has no analogues in other cycles. We also studied the integral distributions for equivalent diameters and found the following: (a) the average value of the index of power-law approximation is 5.4 for the last 13 cycles and (b) there is reliable evidence of Hale's double cycle (about 44 years). Since this indicator reflects the dispersion of sunspot group diameters, the results obtained show that the convective zone of the Sun generates embryos of active regions in different statistical regimes which change with a cycle of about 44 years.

Entropic Analysis of Electromyography Time Series

Science.gov (United States)

Kaufman, Miron; Sung, Paul

2005-03-01

We are in the process of assessing the effectiveness of fractal and entropic measures for the diagnostic of low back pain from surface electromyography (EMG) time series. Surface electromyography (EMG) is used to assess patients with low back pain. In a typical EMG measurement, the voltage is measured every millisecond. We observed back muscle fatiguing during one minute, which results in a time series with 60,000 entries. We characterize the complexity of time series by computing the Shannon entropy time dependence. The analysis of the time series from different relevant muscles from healthy and low back pain (LBP) individuals provides evidence that the level of variability of back muscle activities is much larger for healthy individuals than for individuals with LBP. In general the time dependence of the entropy shows a crossover from a diffusive regime to a regime characterized by long time correlations (self organization) at about 0.01s.

Quantifying memory in complex physiological time-series.

Science.gov (United States)

Shirazi, Amir H; Raoufy, Mohammad R; Ebadi, Haleh; De Rui, Michele; Schiff, Sami; Mazloom, Roham; Hajizadeh, Sohrab; Gharibzadeh, Shahriar; Dehpour, Ahmad R; Amodio, Piero; Jafari, G Reza; Montagnese, Sara; Mani, Ali R

2013-01-01

In a time-series, memory is a statistical feature that lasts for a period of time and distinguishes the time-series from a random, or memory-less, process. In the present study, the concept of "memory length" was used to define the time period, or scale over which rare events within a physiological time-series do not appear randomly. The method is based on inverse statistical analysis and provides empiric evidence that rare fluctuations in cardio-respiratory time-series are 'forgotten' quickly in healthy subjects while the memory for such events is significantly prolonged in pathological conditions such as asthma (respiratory time-series) and liver cirrhosis (heart-beat time-series). The memory length was significantly higher in patients with uncontrolled asthma compared to healthy volunteers. Likewise, it was significantly higher in patients with decompensated cirrhosis compared to those with compensated cirrhosis and healthy volunteers. We also observed that the cardio-respiratory system has simple low order dynamics and short memory around its average, and high order dynamics around rare fluctuations.

Effective Feature Preprocessing for Time Series Forecasting

DEFF Research Database (Denmark)

Zhao, Junhua; Dong, Zhaoyang; Xu, Zhao

2006-01-01

Time series forecasting is an important area in data mining research. Feature preprocessing techniques have significant influence on forecasting accuracy, therefore are essential in a forecasting model. Although several feature preprocessing techniques have been applied in time series forecasting...... performance in time series forecasting. It is demonstrated in our experiment that, effective feature preprocessing can significantly enhance forecasting accuracy. This research can be a useful guidance for researchers on effectively selecting feature preprocessing techniques and integrating them with time...... series forecasting models....

Distribution of electric currents in sunspots from photosphere to corona

Energy Technology Data Exchange (ETDEWEB)

Gosain, Sanjay [National Solar Observatory, 950 North Cherry Avenue, Tucson, AZ 85719 (United States); Démoulin, Pascal [Observatoire de Paris, LESIA, UMR 8109 (CNRS), F-92195 Meudon Principal Cedex (France); López Fuentes, Marcelo [Instituto de Astronomía y Física del Espacio (IAFE), UBA-CONICET, CC. 67, Suc. 28 Buenos Aires 1428 (Argentina)

2014-09-20

We present a study of two regular sunspots that exhibit nearly uniform twist from the photosphere to the corona. We derive the twist parameter in the corona and in the chromosphere by minimizing the difference between the extrapolated linear force-free field model field lines and the observed intensity structures in the extreme-ultraviolet images of the Sun. The chromospheric structures appear more twisted than the coronal structures by a factor of two. Further, we derive the vertical component of electric current density, j{sub z} , using vector magnetograms from the Hinode Solar Optical Telescope (SOT). The spatial distribution of j{sub z} has a zebra pattern of strong positive and negative values owing to the penumbral fibril structure resolved by Hinode/SOT. This zebra pattern is due to the derivative of the horizontal magnetic field across the thin fibrils; therefore, it is strong and masks weaker currents that might be present, for example, as a result of the twist of the sunspot. We decompose j{sub z} into the contribution due to the derivatives along and across the direction of the horizontal field, which follows the fibril orientation closely. The map of the tangential component has more distributed currents that are coherent with the chromospheric and coronal twisted structures. Moreover, it allows us to map and identify the direct and return currents in the sunspots. Finally, this decomposition of j{sub z} is general and can be applied to any vector magnetogram in order to better identify the weaker large-scale currents that are associated with coronal twisted/sheared structures.

High Velocity Horizontal Motions at the Edge of Sunspot Penumbrae

Science.gov (United States)

Hagenaar-Daggett, Hermance J.; Shine, R.

2010-05-01

The outer edges of sunspot penumbrae have long been noted as a region of interesting dynamics including formation of MMFs, extensions and retractions of the penumbral tips, fast moving (2-3 km/s) bright features dubbed"streakers", and localized regions of high speed downflows interpreted as Evershed "sinks". Using 30s cadence movies of high spatial resolution G band and Ca II H images taken by the Hinode SOT/FPP instrument from 5-7 Jan 2007, we have been investigating the penumbra around a sunspot in AR 10933. In addition to the expected phenomena, we also see occasional small dark crescent-shaped features with high horizontal velocities (6.5 km/s) in G band movies. These appear to be emitted from penumbral tips. They travel about 1.5 Mm developing a bright wake that evolves into a slower moving (1-2 km/s) bright feature. In some cases, there may be an earlier outward propagating disturbance within the penumbra. We have also analyzed available Fe 6302 Stokes V images to obtain information on the magnetic field. Although only lower resolution 6302 images made with a slower cadence are available for these particular data sets, we can establish that the features have the opposite magnetic polarity of the sunspot. This observation may be in agreement with simulations showing that a horizontal flux tube develops crests that move outward with a velocity as large as 10 km/s. This work was supported by NASA contract NNM07AA01C.

Deciphering solar magnetic activity. I. On the relationship between the sunspot cycle and the evolution of small magnetic features

Energy Technology Data Exchange (ETDEWEB)

McIntosh, Scott W.; Wang, Xin; Markel, Robert S.; Thompson, Michael J. [High Altitude Observatory, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307 (United States); Leamon, Robert J.; Malanushenko, Anna V. [Department of Physics, Montana State University, Bozeman, MT 59717 (United States); Davey, Alisdair R. [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Howe, Rachel [School of Physics and Astronomy, University of Birmingham, Edgbaston, Birmingham, B15 2TT (United Kingdom); Krista, Larisza D. [Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO 80205 (United States); Cirtain, Jonathan W. [Marshall Space Flight Center, Code ZP13, Huntsville, AL 35812 (United States); Gurman, Joseph B.; Pesnell, William D., E-mail: mscott@ucar.edu [Solar Physics Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771 (United States)

2014-09-01

Sunspots are a canonical marker of the Sun's internal magnetic field which flips polarity every ∼22 yr. The principal variation of sunspots, an ∼11 yr variation, modulates the amount of the magnetic field that pierces the solar surface and drives significant variations in our star's radiative, particulate, and eruptive output over that period. This paper presents observations from the Solar and Heliospheric Observatory and Solar Dynamics Observatory indicating that the 11 yr sunspot variation is intrinsically tied to the spatio-temporal overlap of the activity bands belonging to the 22 yr magnetic activity cycle. Using a systematic analysis of ubiquitous coronal brightpoints and the magnetic scale on which they appear to form, we show that the landmarks of sunspot cycle 23 can be explained by considering the evolution and interaction of the overlapping activity bands of the longer-scale variability.

Statistical criteria for characterizing irradiance time series.

Energy Technology Data Exchange (ETDEWEB)

Stein, Joshua S.; Ellis, Abraham; Hansen, Clifford W.

2010-10-01

We propose and examine several statistical criteria for characterizing time series of solar irradiance. Time series of irradiance are used in analyses that seek to quantify the performance of photovoltaic (PV) power systems over time. Time series of irradiance are either measured or are simulated using models. Simulations of irradiance are often calibrated to or generated from statistics for observed irradiance and simulations are validated by comparing the simulation output to the observed irradiance. Criteria used in this comparison should derive from the context of the analyses in which the simulated irradiance is to be used. We examine three statistics that characterize time series and their use as criteria for comparing time series. We demonstrate these statistics using observed irradiance data recorded in August 2007 in Las Vegas, Nevada, and in June 2009 in Albuquerque, New Mexico.

Homogenising time series: beliefs, dogmas and facts

Science.gov (United States)

Domonkos, P.

2011-06-01

In the recent decades various homogenisation methods have been developed, but the real effects of their application on time series are still not known sufficiently. The ongoing COST action HOME (COST ES0601) is devoted to reveal the real impacts of homogenisation methods more detailed and with higher confidence than earlier. As a part of the COST activity, a benchmark dataset was built whose characteristics approach well the characteristics of real networks of observed time series. This dataset offers much better opportunity than ever before to test the wide variety of homogenisation methods, and analyse the real effects of selected theoretical recommendations. Empirical results show that real observed time series usually include several inhomogeneities of different sizes. Small inhomogeneities often have similar statistical characteristics than natural changes caused by climatic variability, thus the pure application of the classic theory that change-points of observed time series can be found and corrected one-by-one is impossible. However, after homogenisation the linear trends, seasonal changes and long-term fluctuations of time series are usually much closer to the reality than in raw time series. Some problems around detecting multiple structures of inhomogeneities, as well as that of time series comparisons within homogenisation procedures are discussed briefly in the study.

Umbral oscillations as a probe of sunspot

International Nuclear Information System (INIS)

Abdelatif, T.E.H.

1985-01-01

The interaction of the solar five-minute oscillations with a sunspot is thoroughly explored, both on observational and theoretical grounds. Simple theoretical models are developed in order to understand the observations of umbral oscillations. Observations made at the National Solar Observatory detected both the three-minute and five-minute umbral oscillations at photospheric heights. The three-minute oscillations were found to have a kinetic energy density six times higher in the photosphere than in the chromosphere and to be concentrated in the central part of the umbra, supporting the photospheric resonance theory for the three-minute umbral oscillations. The five-minute oscillations are attenuated in the umbra, which appears to act as a filter in selecting some of the peaks in the power spectrum of five-minute oscillations in the surrounding photosphere. The k-omega power spectrum of the umbral oscillations shows a shift of power to longer wavelengths. Theoretical models of the transmission of acoustic waves into a magnetic region explain both observed effects

Ocean time-series near Bermuda: Hydrostation S and the US JGOFS Bermuda Atlantic time-series study

Science.gov (United States)

Michaels, Anthony F.; Knap, Anthony H.

1992-01-01

Bermuda is the site of two ocean time-series programs. At Hydrostation S, the ongoing biweekly profiles of temperature, salinity and oxygen now span 37 years. This is one of the longest open-ocean time-series data sets and provides a view of decadal scale variability in ocean processes. In 1988, the U.S. JGOFS Bermuda Atlantic Time-series Study began a wide range of measurements at a frequency of 14-18 cruises each year to understand temporal variability in ocean biogeochemistry. On each cruise, the data range from chemical analyses of discrete water samples to data from electronic packages of hydrographic and optics sensors. In addition, a range of biological and geochemical rate measurements are conducted that integrate over time-periods of minutes to days. This sampling strategy yields a reasonable resolution of the major seasonal patterns and of decadal scale variability. The Sargasso Sea also has a variety of episodic production events on scales of days to weeks and these are only poorly resolved. In addition, there is a substantial amount of mesoscale variability in this region and some of the perceived temporal patterns are caused by the intersection of the biweekly sampling with the natural spatial variability. In the Bermuda time-series programs, we have added a series of additional cruises to begin to assess these other sources of variation and their impacts on the interpretation of the main time-series record. However, the adequate resolution of higher frequency temporal patterns will probably require the introduction of new sampling strategies and some emerging technologies such as biogeochemical moorings and autonomous underwater vehicles.

The reconciliation of an F-region irregularity model with sunspot-cycle variations in spread-F occurrence

International Nuclear Information System (INIS)

Singleton, D.G.

1974-11-01

A recently proposed means of combining models of ionospheric F-layer peak electron density and irregularity incremental electron density (ΔN) so as to simulate the global occurrence probability of the frequency spreading component of spread-F is discussed. This procedure is then used to model experimental spread-F occurrence results. It is found possible to readily simulate the sunspot-maximum results, independently of season, with only small adjustments to the amplitudes of the empirical expressions used to ΔN in the several latitude regimes. However, at sunspot minimum and for each season, the ΔN model requires modification in the equatorial and mid-latitude regions of high irregularity incidence, before successful simulations of the spread-F data can be obtained. These modifications, which include a broadening of the equatorial region and a polewards shift to the mid-latitude region with decreasing sunspot number, are discussed in detail. It is concluded that the scintillation data base, from which the original ΔN model derives, is not sufficiently representative with regard to sunspot number and magnetic index. The use of the spread-F adaptation of the ΔN model, as well as its original scintillation version, to rectify these failings of the ΔN model are also discussed. (author)

Possibility to explain the temperature distribution in sunspots by an anisotropic heat transfer

Energy Technology Data Exchange (ETDEWEB)

Eschrich, K O; Krause, F [Akademie der Wissenschaften der DDR, Potsdam. Zentralinstitut fuer Astrophysik

1977-01-01

Numerical solutions of a heat conduction problem in an anisotropic medium are used for a discussion of the possibility to explain the temperature distribution in sunspots and their environment. The anisotropy is assumed being due to the strong magnetic field in sunspots and the region below. This magnetic field forces the convection to take an anisotropic structure (two-dimensional turbulence) and thus the region gets anisotropic conduction properties, on the average. The discussion shows that the observed temperature profiles can be explained in the case the depth of the region of anisotropy is about as large as the diameter of the spot or larger.

MEASUREMENTS OF ABSORPTION, EMISSIVITY REDUCTION, AND LOCAL SUPPRESSION OF SOLAR ACOUSTIC WAVES IN SUNSPOTS

International Nuclear Information System (INIS)

Chou, D.-Y.; Liang, Z.-C.; Yang, M.-H.; Zhao Hui; Sun, M.-T.

2009-01-01

The power of solar acoustic waves in magnetic regions is lower relative to the quiet Sun. Absorption, emissivity reduction, and local suppression of acoustic waves contribute to the observed power reduction in magnetic regions. We propose a model for the energy budget of acoustic waves propagating through a sunspot in terms of the coefficients of absorption, emissivity reduction, and local suppression of the sunspot. Using the property that the waves emitted along the wave path between two points have no correlation with the signal at the starting point, we can separate the effects of these three mechanisms. Applying this method to helioseismic data filtered with direction and phase-velocity filters, we measure the fraction of the contribution of each mechanism to the power deficit in the umbra of the leading sunspot of NOAA 9057. The contribution from absorption is 23.3 ± 1.3%, emissivity reduction 8.2 ± 1.4%, and local suppression 68.5 ± 1.5%, for a wave packet corresponding to a phase velocity of 6.98 x 10 -5 rad s -1 .

Forecasting Enrollments with Fuzzy Time Series.

Science.gov (United States)

Song, Qiang; Chissom, Brad S.

The concept of fuzzy time series is introduced and used to forecast the enrollment of a university. Fuzzy time series, an aspect of fuzzy set theory, forecasts enrollment using a first-order time-invariant model. To evaluate the model, the conventional linear regression technique is applied and the predicted values obtained are compared to the…

Observations of the longitudinal magnetic field in the transition region and photosphere of a sunspot

Science.gov (United States)

Henze, W., Jr.; Tandberg-Hanssen, E.; Hagyard, M. J.; West, E. A.; Woodgate, B. E.; Shine, R. A.; Beckers, J. M.; Bruner, M.; Hyder, C. L.; West, E. A.

1982-01-01

The Ultraviolet Spectrometer and Polarimeter on the Solar Maximum Mission spacraft has observed for the first time the longitudinal component of the magnetic field by means of the Zeeman effect in the transition region above a sunspot. The data presented here were obtained on three days in one sunspot, have spatial resolutions of 10 arcsec and 3 arcsec, and yield maximum field strengths greater than 1000 G above the umbrae in the spot. The method of analysis, including a line-width calibration feature used during some of the observations, is described in some detail in an appendix; the line width is required for the determination of the longitudinal magnetic field from the observed circular polarization. The transition region data for one day are compared with photospheric magnetograms from the Marshall Space Flight Center. Vertical gradients of the magnetic field are compared from the two sets of data; the maximum gradients of 0.41 to 0.62 G/km occur above the umbra and agree with or are smaller than values observed previously in the photosphere and low chromosphere.

An econometric investigation of the sunspot number record since the year 1700 and its prediction into the 22nd century

Science.gov (United States)

Travaglini, Guido

2015-09-01

Solar activity, as measured by the yearly revisited time series of sunspot numbers (SSN) for the period 1700-2014 (Clette et al., 2014), undergoes in this paper a triple statistical and econometric checkup. The conclusions are that the SSN sequence: (1) is best modeled as a signal that features nonlinearity in mean and variance, long memory, mean reversion, 'threshold' symmetry, and stationarity; (2) is best described as a discrete damped harmonic oscillator which linearly approximates the flux-transport dynamo model; (3) its prediction well into the 22nd century testifies of a substantial fall of the SSN centered around the year 2030. In addition, the first and last Gleissberg cycles show almost the same peak number and height during the period considered, yet the former slightly prevails when measured by means of the estimated smoother. All of these conclusions are achieved by making use of modern tools developed in the field of Financial Econometrics and of two new proposed procedures for signal smoothing and prediction.

Automated Sunspot Detection and Classification Using SOHO/MDI Imagery

Science.gov (United States)

2015-03-01

to the geocentric North). 3. Focus and size of the solar disk is adjusted to fit an 18 cm diameter circle on the worksheet. 4. Analyst hand draws the...General Nature of the Sunspot,” The Astrophysical Journal 230, 905–913 (1979). 14. Wheatland, M. S., “A Bayesian Approach to Solar Flare Prediction,” The

Forecasting Cryptocurrencies Financial Time Series

DEFF Research Database (Denmark)

Catania, Leopoldo; Grassi, Stefano; Ravazzolo, Francesco

2018-01-01

This paper studies the predictability of cryptocurrencies time series. We compare several alternative univariate and multivariate models in point and density forecasting of four of the most capitalized series: Bitcoin, Litecoin, Ripple and Ethereum. We apply a set of crypto–predictors and rely...

Forecasting Cryptocurrencies Financial Time Series

OpenAIRE

Catania, Leopoldo; Grassi, Stefano; Ravazzolo, Francesco

2018-01-01

This paper studies the predictability of cryptocurrencies time series. We compare several alternative univariate and multivariate models in point and density forecasting of four of the most capitalized series: Bitcoin, Litecoin, Ripple and Ethereum. We apply a set of crypto–predictors and rely on Dynamic Model Averaging to combine a large set of univariate Dynamic Linear Models and several multivariate Vector Autoregressive models with different forms of time variation. We find statistical si...

Time series modeling, computation, and inference

CERN Document Server

Prado, Raquel

2010-01-01

The authors systematically develop a state-of-the-art analysis and modeling of time series. … this book is well organized and well written. The authors present various statistical models for engineers to solve problems in time series analysis. Readers no doubt will learn state-of-the-art techniques from this book.-Hsun-Hsien Chang, Computing Reviews, March 2012My favorite chapters were on dynamic linear models and vector AR and vector ARMA models.-William Seaver, Technometrics, August 2011… a very modern entry to the field of time-series modelling, with a rich reference list of the current lit

Time Series Analysis Forecasting and Control

CERN Document Server

Box, George E P; Reinsel, Gregory C

2011-01-01

A modernized new edition of one of the most trusted books on time series analysis. Since publication of the first edition in 1970, Time Series Analysis has served as one of the most influential and prominent works on the subject. This new edition maintains its balanced presentation of the tools for modeling and analyzing time series and also introduces the latest developments that have occurred n the field over the past decade through applications from areas such as business, finance, and engineering. The Fourth Edition provides a clearly written exploration of the key methods for building, cl

3-color photometry of a sunspot using speckle masking techniques

NARCIS (Netherlands)

Wiehr, E.; Sütterlin, P.

1998-01-01

A three-colour photometry is used to deduce the temperature of sunspot fine-structures. Using the Speckle-Masking method for image restoration, the resulting images (one per colour and burst) have a spatial resolution only limited by the telescope's aperture, i.e. 95km (blue), 145 km (red) and

Costationarity of Locally Stationary Time Series Using costat

OpenAIRE

Cardinali, Alessandro; Nason, Guy P.

2013-01-01

This article describes the R package costat. This package enables a user to (i) perform a test for time series stationarity; (ii) compute and plot time-localized autocovariances, and (iii) to determine and explore any costationary relationship between two locally stationary time series. Two locally stationary time series are said to be costationary if there exists two time-varying combination functions such that the linear combination of the two series with the functions produces another time...

Detecting nonlinear structure in time series

International Nuclear Information System (INIS)

Theiler, J.

1991-01-01

We describe an approach for evaluating the statistical significance of evidence for nonlinearity in a time series. The formal application of our method requires the careful statement of a null hypothesis which characterizes a candidate linear process, the generation of an ensemble of ''surrogate'' data sets which are similar to the original time series but consistent with the null hypothesis, and the computation of a discriminating statistic for the original and for each of the surrogate data sets. The idea is to test the original time series against the null hypothesis by checking whether the discriminating statistic computed for the original time series differs significantly from the statistics computed for each of the surrogate sets. While some data sets very cleanly exhibit low-dimensional chaos, there are many cases where the evidence is sketchy and difficult to evaluate. We hope to provide a framework within which such claims of nonlinearity can be evaluated. 5 refs., 4 figs

Introduction to time series and forecasting

CERN Document Server

Brockwell, Peter J

2016-01-01

This book is aimed at the reader who wishes to gain a working knowledge of time series and forecasting methods as applied to economics, engineering and the natural and social sciences. It assumes knowledge only of basic calculus, matrix algebra and elementary statistics. This third edition contains detailed instructions for the use of the professional version of the Windows-based computer package ITSM2000, now available as a free download from the Springer Extras website. The logic and tools of time series model-building are developed in detail. Numerous exercises are included and the software can be used to analyze and forecast data sets of the user's own choosing. The book can also be used in conjunction with other time series packages such as those included in R. The programs in ITSM2000 however are menu-driven and can be used with minimal investment of time in the computational details. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space mod...

Depressed emission between magnetic arcades near a sunspot

Science.gov (United States)

Ryabov, B. I.; Shibasaki, K.

The locations of the depressed emission in microwaves, EUV and soft X-rays are compared with each other and with the location of the plasma outflow in the active region (AR) 8535 on the Sun. We found that two open-field regions overlap the regions of depressed emission near the AR's sunspot. These two open-field regions are simulated with the potential-field source-surface (PFSS) model under radial distances of RSS = 1.8 R⊙ and RSS = 2.5 R⊙. Each open-field region is located between the arcades of the loops of the same magnetic polarity. The former open-field region covers the region of the plasma outflow, which is thus useful for the tests on connection to the heliosphere. The utmost microwave depression of the intensity in the ordinary mode (the Very Large Array 15 GHz observations) also overlaps the region of the plasma outflow and thus indicates this outflow. The lasting for eight days depression in soft X-rays and the SOHO EIT 2.84× 10-8 m images are attributed to the evacuation of as hot coronal plasma as T≥ 2× 106 K from the extended in height (``open") magnetic structures. We conclude that the AR 8535 presents the sunspot atmosphere affected by the large-scale magnetic fields.

ASYMMETRIC SUNSPOT ACTIVITY AND THE SOUTHWARD DISPLACEMENT OF THE HELIOSPHERIC CURRENT SHEET

International Nuclear Information System (INIS)

Wang, Y.-M.; Robbrecht, E.

2011-01-01

Observations of the interplanetary magnetic field (IMF) have suggested a statistical tendency for the heliospheric current sheet (HCS) to be shifted a few degrees southward of the heliographic equator during the period 1965-2010, particularly in the years near sunspot minimum. Using potential-field source-surface extrapolations and photospheric flux-transport simulations, we demonstrate that this southward displacement follows from Joy's law and the observed hemispheric asymmetry in the sunspot numbers, with activity being stronger in the southern (northern) hemisphere during the declining (rising) phase of cycles 20-23. The hemispheric asymmetry gives rise to an axisymmetric quadrupole field, whose equatorial zone has the sign of the leading-polarity flux in the dominant hemisphere; during the last four cycles, the polarity of the IMF around the equator thus tended to match that of the north polar field both before and after polar field reversal. However, large fluctuations are introduced by the nonaxisymmetric field components, which depend on the longitudinal distribution of sunspot activity in either hemisphere. Consistent with this model, the HCS showed an average northward displacement during cycle 19, when the 'usual' alternation was reversed and the northern hemisphere became far more active than the southern hemisphere during the declining phase of the cycle. We propose a new method for determining the north-south displacement of the HCS from coronal streamer observations.

Initial phase of the development of sunspot groups and their forecast

International Nuclear Information System (INIS)

Berlyand, B.O.; Burov, V.A.; Stepanyan, N.N.

1979-01-01

Some characteristics of the initial phase of sunspot groups and their forecast have been considered. Experimental data on 340 sunspot groups were obtained in 1967-1969. It was found that oscillations of the magnetic flux in the groups indicate the possibility of the existence of typical periods (2 and 4 days) of the magnetic field development. Most of the groups appears in young plages. The probability of the protons injection from the young groups is very small. The typical time of the development of the proton centre is 10-30 days. The characteristics of the group on the first day of its existence are vaguely connected with the lifetime of the group. On the second and third days the magnetic characteristics (the summary magnetic flux and the number of the unipolar regions) have the highest correlation coefficient (approximately 70%) with the lifetime of the group. The problem of the group lifetime forecast was being solved with the pattern recognition technique. On the base of the second day observation of the existence of the group verification of the received forecast 14% exceeds the verification of the climatological forecast. The forecast of the Zurich class with the same technique is effective beginning with the fifth day of the group existence and the forecast of the flare activity of the group since the day of its appearance. The exceeding of the verification as compared with the climatological forecasts in these problems is 10% and 8% accordingly

TIME SERIES ANALYSIS USING A UNIQUE MODEL OF TRANSFORMATION

Directory of Open Access Journals (Sweden)

Goran Klepac

2007-12-01

Full Text Available REFII1 model is an authorial mathematical model for time series data mining. The main purpose of that model is to automate time series analysis, through a unique transformation model of time series. An advantage of this approach of time series analysis is the linkage of different methods for time series analysis, linking traditional data mining tools in time series, and constructing new algorithms for analyzing time series. It is worth mentioning that REFII model is not a closed system, which means that we have a finite set of methods. At first, this is a model for transformation of values of time series, which prepares data used by different sets of methods based on the same model of transformation in a domain of problem space. REFII model gives a new approach in time series analysis based on a unique model of transformation, which is a base for all kind of time series analysis. The advantage of REFII model is its possible application in many different areas such as finance, medicine, voice recognition, face recognition and text mining.

Frontiers in Time Series and Financial Econometrics

OpenAIRE

Ling, S.; McAleer, M.J.; Tong, H.

2015-01-01

__Abstract__ Two of the fastest growing frontiers in econometrics and quantitative finance are time series and financial econometrics. Significant theoretical contributions to financial econometrics have been made by experts in statistics, econometrics, mathematics, and time series analysis. The purpose of this special issue of the journal on “Frontiers in Time Series and Financial Econometrics” is to highlight several areas of research by leading academics in which novel methods have contrib...

Scale-dependent intrinsic entropies of complex time series.

Science.gov (United States)

Yeh, Jia-Rong; Peng, Chung-Kang; Huang, Norden E

2016-04-13

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal's complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease. © 2016 The Author(s).

Elements of nonlinear time series analysis and forecasting

CERN Document Server

De Gooijer, Jan G

2017-01-01

This book provides an overview of the current state-of-the-art of nonlinear time series analysis, richly illustrated with examples, pseudocode algorithms and real-world applications. Avoiding a “theorem-proof” format, it shows concrete applications on a variety of empirical time series. The book can be used in graduate courses in nonlinear time series and at the same time also includes interesting material for more advanced readers. Though it is largely self-contained, readers require an understanding of basic linear time series concepts, Markov chains and Monte Carlo simulation methods. The book covers time-domain and frequency-domain methods for the analysis of both univariate and multivariate (vector) time series. It makes a clear distinction between parametric models on the one hand, and semi- and nonparametric models/methods on the other. This offers the reader the option of concentrating exclusively on one of these nonlinear time series analysis methods. To make the book as user friendly as possible...

An Energy-Based Similarity Measure for Time Series

Directory of Open Access Journals (Sweden)

Pierre Brunagel

2007-11-01

Full Text Available A new similarity measure, called SimilB, for time series analysis, based on the cross-ΨB-energy operator (2004, is introduced. ΨB is a nonlinear measure which quantifies the interaction between two time series. Compared to Euclidean distance (ED or the Pearson correlation coefficient (CC, SimilB includes the temporal information and relative changes of the time series using the first and second derivatives of the time series. SimilB is well suited for both nonstationary and stationary time series and particularly those presenting discontinuities. Some new properties of ΨB are presented. Particularly, we show that ΨB as similarity measure is robust to both scale and time shift. SimilB is illustrated with synthetic time series and an artificial dataset and compared to the CC and the ED measures.

Predicting Maximum Sunspot Number in Solar Cycle 24 Nipa J Bhatt ...

Indian Academy of Sciences (India)

Key words. Sunspot number—precursor prediction technique—geo- magnetic activity index aa. 1. Introduction. Predictions of solar and geomagnetic activities are important for various purposes, including the operation of low-earth orbiting satellites, operation of power grids on. Earth, and satellite communication systems.

Detecting chaos in irregularly sampled time series.

Science.gov (United States)

Kulp, C W

2013-09-01

Recently, Wiebe and Virgin [Chaos 22, 013136 (2012)] developed an algorithm which detects chaos by analyzing a time series' power spectrum which is computed using the Discrete Fourier Transform (DFT). Their algorithm, like other time series characterization algorithms, requires that the time series be regularly sampled. Real-world data, however, are often irregularly sampled, thus, making the detection of chaotic behavior difficult or impossible with those methods. In this paper, a characterization algorithm is presented, which effectively detects chaos in irregularly sampled time series. The work presented here is a modification of Wiebe and Virgin's algorithm and uses the Lomb-Scargle Periodogram (LSP) to compute a series' power spectrum instead of the DFT. The DFT is not appropriate for irregularly sampled time series. However, the LSP is capable of computing the frequency content of irregularly sampled data. Furthermore, a new method of analyzing the power spectrum is developed, which can be useful for differentiating between chaotic and non-chaotic behavior. The new characterization algorithm is successfully applied to irregularly sampled data generated by a model as well as data consisting of observations of variable stars.

Building Chaotic Model From Incomplete Time Series

Science.gov (United States)

Siek, Michael; Solomatine, Dimitri

2010-05-01

This paper presents a number of novel techniques for building a predictive chaotic model from incomplete time series. A predictive chaotic model is built by reconstructing the time-delayed phase space from observed time series and the prediction is made by a global model or adaptive local models based on the dynamical neighbors found in the reconstructed phase space. In general, the building of any data-driven models depends on the completeness and quality of the data itself. However, the completeness of the data availability can not always be guaranteed since the measurement or data transmission is intermittently not working properly due to some reasons. We propose two main solutions dealing with incomplete time series: using imputing and non-imputing methods. For imputing methods, we utilized the interpolation methods (weighted sum of linear interpolations, Bayesian principle component analysis and cubic spline interpolation) and predictive models (neural network, kernel machine, chaotic model) for estimating the missing values. After imputing the missing values, the phase space reconstruction and chaotic model prediction are executed as a standard procedure. For non-imputing methods, we reconstructed the time-delayed phase space from observed time series with missing values. This reconstruction results in non-continuous trajectories. However, the local model prediction can still be made from the other dynamical neighbors reconstructed from non-missing values. We implemented and tested these methods to construct a chaotic model for predicting storm surges at Hoek van Holland as the entrance of Rotterdam Port. The hourly surge time series is available for duration of 1990-1996. For measuring the performance of the proposed methods, a synthetic time series with missing values generated by a particular random variable to the original (complete) time series is utilized. There exist two main performance measures used in this work: (1) error measures between the actual

Multivariate Time Series Search

Data.gov (United States)

National Aeronautics and Space Administration — Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical...

Analysing Stable Time Series

National Research Council Canada - National Science Library

Adler, Robert

1997-01-01

We describe how to take a stable, ARMA, time series through the various stages of model identification, parameter estimation, and diagnostic checking, and accompany the discussion with a goodly number...

THE FORMATION OF AN INVERSE S-SHAPED ACTIVE-REGION FILAMENT DRIVEN BY SUNSPOT MOTION AND MAGNETIC RECONNECTION

Energy Technology Data Exchange (ETDEWEB)

Yan, X. L.; Xue, Z. K.; Wang, J. C.; Yang, L. H. [Yunnan Observatories, Chinese Academy of Sciences, Kunming 650011 (China); Priest, E. R. [Mathematics Institute, University of St Andrews, St Andrews, KY16 9SS (United Kingdom); Guo, Q. L., E-mail: yanxl@ynao.ac.cn [College of Mathematics Physics and Information Engineering, Jiaxing University, Jiaxing 314001 (China)

2016-11-20

We present a detailed study of the formation of an inverse S-shaped filament prior to its eruption in active region NOAA 11884 from 2013 October 31 to November 2. In the initial stage, clockwise rotation of a small positive sunspot around the main negative trailing sunspot formed a curved filament. Then the small sunspot cancelled with the negative magnetic flux to create a longer active-region filament with an inverse S-shape. At the cancellation site a brightening was observed in UV and EUV images and bright material was transferred to the filament. Later the filament erupted after cancellation of two opposite polarities below the upper part of the filament. Nonlinear force-free field extrapolation of vector photospheric fields suggests that the filament may have a twisted structure, but this cannot be confirmed from the current observations.

Neural Network Models for Time Series Forecasts

OpenAIRE

Tim Hill; Marcus O'Connor; William Remus

1996-01-01

Neural networks have been advocated as an alternative to traditional statistical forecasting methods. In the present experiment, time series forecasts produced by neural networks are compared with forecasts from six statistical time series methods generated in a major forecasting competition (Makridakis et al. [Makridakis, S., A. Anderson, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, R. Winkler. 1982. The accuracy of extrapolation (time series) methods: Results of a ...

Time Series Observations in the North Indian Ocean

Digital Repository Service at National Institute of Oceanography (India)

Shenoy, D.M.; Naik, H.; Kurian, S.; Naqvi, S.W.A.; Khare, N.

Ocean and the ongoing time series study (Candolim Time Series; CaTS) off Goa. In addition, this article also focuses on the new time series initiative in the Arabian Sea and the Bay of Bengal under Sustained Indian Ocean Biogeochemistry and Ecosystem...

Critical frequencies of the ionospheric F1 and F2 layers during the last four solar cycles: Sunspot group type dependencies

Science.gov (United States)

Yiǧit, Erdal; Kilcik, Ali; Elias, Ana Georgina; Dönmez, Burçin; Ozguc, Atila; Yurchshyn, Vasyl; Rozelot, Jean-Pierre

2018-06-01

The long term solar activity dependencies of ionospheric F1 and F2 regions' critical frequencies (f0F1 and f0F2) are analyzed for the last four solar cycles (1976-2015). We show that the ionospheric F1 and F2 regions have different solar activity dependencies in terms of the sunspot group (SG) numbers: F1 region critical frequency (f0F1) peaks at the same time with the small SG numbers, while the f0F2 reaches its maximum at the same time with the large SG numbers, especially during the solar cycle 23. The observed differences in the sensitivity of ionospheric critical frequencies to sunspot group (SG) numbers provide a new insight into the solar activity effects on the ionosphere and space weather. While the F1 layer is influenced by the slow solar wind, which is largely associated with small SGs, the ionospheric F2 layer is more sensitive to Coronal Mass Ejections (CMEs) and fast solar winds, which are mainly produced by large SGs and coronal holes. The SG numbers maximize during of peak of the solar cycle and the number of coronal holes peaks during the sunspot declining phase. During solar minimum there are relatively less large SGs, hence reduced CME and flare activity. These results provide a new perspective for assessing how the different regions of the ionosphere respond to space weather effects.

Geometric noise reduction for multivariate time series.

Science.gov (United States)

Mera, M Eugenia; Morán, Manuel

2006-03-01

We propose an algorithm for the reduction of observational noise in chaotic multivariate time series. The algorithm is based on a maximum likelihood criterion, and its goal is to reduce the mean distance of the points of the cleaned time series to the attractor. We give evidence of the convergence of the empirical measure associated with the cleaned time series to the underlying invariant measure, implying the possibility to predict the long run behavior of the true dynamics.

BRITS: Bidirectional Recurrent Imputation for Time Series

OpenAIRE

Cao, Wei; Wang, Dong; Li, Jian; Zhou, Hao; Li, Lei; Li, Yitan

2018-01-01

Time series are widely used as signals in many classification/regression tasks. It is ubiquitous that time series contains many missing values. Given multiple correlated time series data, how to fill in missing values and to predict their class labels? Existing imputation methods often impose strong assumptions of the underlying data generating process, such as linear dynamics in the state space. In this paper, we propose BRITS, a novel method based on recurrent neural networks for missing va...

Efficient Algorithms for Segmentation of Item-Set Time Series

Science.gov (United States)

Chundi, Parvathi; Rosenkrantz, Daniel J.

We propose a special type of time series, which we call an item-set time series, to facilitate the temporal analysis of software version histories, email logs, stock market data, etc. In an item-set time series, each observed data value is a set of discrete items. We formalize the concept of an item-set time series and present efficient algorithms for segmenting a given item-set time series. Segmentation of a time series partitions the time series into a sequence of segments where each segment is constructed by combining consecutive time points of the time series. Each segment is associated with an item set that is computed from the item sets of the time points in that segment, using a function which we call a measure function. We then define a concept called the segment difference, which measures the difference between the item set of a segment and the item sets of the time points in that segment. The segment difference values are required to construct an optimal segmentation of the time series. We describe novel and efficient algorithms to compute segment difference values for each of the measure functions described in the paper. We outline a dynamic programming based scheme to construct an optimal segmentation of the given item-set time series. We use the item-set time series segmentation techniques to analyze the temporal content of three different data sets—Enron email, stock market data, and a synthetic data set. The experimental results show that an optimal segmentation of item-set time series data captures much more temporal content than a segmentation constructed based on the number of time points in each segment, without examining the item set data at the time points, and can be used to analyze different types of temporal data.

Studies on time series applications in environmental sciences

CERN Document Server

Bărbulescu, Alina

2016-01-01

Time series analysis and modelling represent a large study field, implying the approach from the perspective of the time and frequency, with applications in different domains. Modelling hydro-meteorological time series is difficult due to the characteristics of these series, as long range dependence, spatial dependence, the correlation with other series. Continuous spatial data plays an important role in planning, risk assessment and decision making in environmental management. In this context, in this book we present various statistical tests and modelling techniques used for time series analysis, as well as applications to hydro-meteorological series from Dobrogea, a region situated in the south-eastern part of Romania, less studied till now. Part of the results are accompanied by their R code. .

Global Population Density Grid Time Series Estimates

Data.gov (United States)

National Aeronautics and Space Administration — Global Population Density Grid Time Series Estimates provide a back-cast time series of population density grids based on the year 2000 population grid from SEDAC's...

Prediction and Geometry of Chaotic Time Series

National Research Council Canada - National Science Library

Leonardi, Mary

1997-01-01

This thesis examines the topic of chaotic time series. An overview of chaos, dynamical systems, and traditional approaches to time series analysis is provided, followed by an examination of state space reconstruction...

Sensor-Generated Time Series Events: A Definition Language

Science.gov (United States)

Anguera, Aurea; Lara, Juan A.; Lizcano, David; Martínez, Maria Aurora; Pazos, Juan

2012-01-01

There are now a great many domains where information is recorded by sensors over a limited time period or on a permanent basis. This data flow leads to sequences of data known as time series. In many domains, like seismography or medicine, time series analysis focuses on particular regions of interest, known as events, whereas the remainder of the time series contains hardly any useful information. In these domains, there is a need for mechanisms to identify and locate such events. In this paper, we propose an events definition language that is general enough to be used to easily and naturally define events in time series recorded by sensors in any domain. The proposed language has been applied to the definition of time series events generated within the branch of medicine dealing with balance-related functions in human beings. A device, called posturograph, is used to study balance-related functions. The platform has four sensors that record the pressure intensity being exerted on the platform, generating four interrelated time series. As opposed to the existing ad hoc proposals, the results confirm that the proposed language is valid, that is generally applicable and accurate, for identifying the events contained in the time series.

Correlation and multifractality in climatological time series

International Nuclear Information System (INIS)

Pedron, I T

2010-01-01

Climate can be described by statistical analysis of mean values of atmospheric variables over a period. It is possible to detect correlations in climatological time series and to classify its behavior. In this work the Hurst exponent, which can characterize correlation and persistence in time series, is obtained by using the Detrended Fluctuation Analysis (DFA) method. Data series of temperature, precipitation, humidity, solar radiation, wind speed, maximum squall, atmospheric pressure and randomic series are studied. Furthermore, the multifractality of such series is analyzed applying the Multifractal Detrended Fluctuation Analysis (MF-DFA) method. The results indicate presence of correlation (persistent character) in all climatological series and multifractality as well. A larger set of data, and longer, could provide better results indicating the universality of the exponents.

A model of a sunspot chromosphere based on OSO 8 observations

Science.gov (United States)

Lites, B. W.; Skumanich, A.

1982-01-01

OSO 8 spectrometer observations of the H I, Mg II, and Ca II resonance lines of a large quiet sunspot during November 16-17, 1975, along with a C IV line of that event obtained by a ground-based spectrometer, are analyzed together with near-simultaneous ground-based Stokes measurements to yield an umbral chromosphere and transition region model. Features of this model include a chromosphere that is effectively thin in the resonance lines of H I and Mg II, while being saturated in Ca II, and an upper chromospheric structure similar to that of quiet-sun models. The similarity of the upper chromosphere of the sunspot umbra to the quiet-sun chromosphere suggests that the intense magnetic field plays only a passive role in the chromospheric heating mechanism, and the observations cited indicate that solar-type stars with large areas of ordered magnetic flux would not necessarily exhibit extremely active chromosphere.

Time Series Forecasting with Missing Values

Directory of Open Access Journals (Sweden)

Shin-Fu Wu

2015-11-01

Full Text Available Time series prediction has become more popular in various kinds of applications such as weather prediction, control engineering, financial analysis, industrial monitoring, etc. To deal with real-world problems, we are often faced with missing values in the data due to sensor malfunctions or human errors. Traditionally, the missing values are simply omitted or replaced by means of imputation methods. However, omitting those missing values may cause temporal discontinuity. Imputation methods, on the other hand, may alter the original time series. In this study, we propose a novel forecasting method based on least squares support vector machine (LSSVM. We employ the input patterns with the temporal information which is defined as local time index (LTI. Time series data as well as local time indexes are fed to LSSVM for doing forecasting without imputation. We compare the forecasting performance of our method with other imputation methods. Experimental results show that the proposed method is promising and is worth further investigations.

SMALL-SCALE AND GLOBAL DYNAMOS AND THE AREA AND FLUX DISTRIBUTIONS OF ACTIVE REGIONS, SUNSPOT GROUPS, AND SUNSPOTS: A MULTI-DATABASE STUDY

Energy Technology Data Exchange (ETDEWEB)

Muñoz-Jaramillo, Andrés; Windmueller, John C.; Amouzou, Ernest C.; Longcope, Dana W. [Department of Physics, Montana State University, Bozeman, MT 59717 (United States); Senkpeil, Ryan R. [Department of Physics, Purdue University, West Lafayette, IN 47907 (United States); Tlatov, Andrey G. [Kislovodsk Mountain Astronomical Station of the Pulkovo Observatory, Kislovodsk 357700 (Russian Federation); Nagovitsyn, Yury A. [Pulkovo Astronomical Observatory, Russian Academy of Sciences, St. Petersburg 196140 (Russian Federation); Pevtsov, Alexei A. [National Solar Observatory, Sunspot, NM 88349 (United States); Chapman, Gary A.; Cookson, Angela M. [San Fernando Observatory, Department of Physics and Astronomy, California State University Northridge, Northridge, CA 91330 (United States); Yeates, Anthony R. [Department of Mathematical Sciences, Durham University, South Road, Durham DH1 3LE (United Kingdom); Watson, Fraser T. [National Solar Observatory, Tucson, AZ 85719 (United States); Balmaceda, Laura A. [Institute for Astronomical, Terrestrial and Space Sciences (ICATE-CONICET), San Juan (Argentina); DeLuca, Edward E. [Harvard-Smithsonian Center for Astrophysics, Cambridge, MA 02138 (United States); Martens, Petrus C. H., E-mail: munoz@solar.physics.montana.edu [Department of Physics and Astronomy, Georgia State University, Atlanta, GA 30303 (United States)

2015-02-10

In this work, we take advantage of 11 different sunspot group, sunspot, and active region databases to characterize the area and flux distributions of photospheric magnetic structures. We find that, when taken separately, different databases are better fitted by different distributions (as has been reported previously in the literature). However, we find that all our databases can be reconciled by the simple application of a proportionality constant, and that, in reality, different databases are sampling different parts of a composite distribution. This composite distribution is made up by linear combination of Weibull and log-normal distributions—where a pure Weibull (log-normal) characterizes the distribution of structures with fluxes below (above) 10{sup 21}Mx (10{sup 22}Mx). Additionally, we demonstrate that the Weibull distribution shows the expected linear behavior of a power-law distribution (when extended to smaller fluxes), making our results compatible with the results of Parnell et al. We propose that this is evidence of two separate mechanisms giving rise to visible structures on the photosphere: one directly connected to the global component of the dynamo (and the generation of bipolar active regions), and the other with the small-scale component of the dynamo (and the fragmentation of magnetic structures due to their interaction with turbulent convection)

Reconstruction of ensembles of coupled time-delay systems from time series.

Science.gov (United States)

Sysoev, I V; Prokhorov, M D; Ponomarenko, V I; Bezruchko, B P

2014-06-01

We propose a method to recover from time series the parameters of coupled time-delay systems and the architecture of couplings between them. The method is based on a reconstruction of model delay-differential equations and estimation of statistical significance of couplings. It can be applied to networks composed of nonidentical nodes with an arbitrary number of unidirectional and bidirectional couplings. We test our method on chaotic and periodic time series produced by model equations of ensembles of diffusively coupled time-delay systems in the presence of noise, and apply it to experimental time series obtained from electronic oscillators with delayed feedback coupled by resistors.

The analysis of time series: an introduction

National Research Council Canada - National Science Library

Chatfield, Christopher

1989-01-01

.... A variety of practical examples are given to support the theory. The book covers a wide range of time-series topics, including probability models for time series, Box-Jenkins forecasting, spectral analysis, linear systems and system identification...

Surge-like Oscillations above Sunspot Light Bridges Driven by Magnetoacoustic Shocks

Energy Technology Data Exchange (ETDEWEB)

Zhang, Jingwen; Tian, Hui; He, Jiansen; Wang, Linghua, E-mail: huitian@pku.edu.cn [School of Earth and Space Sciences, Peking University, 100871 Beijing (China)

2017-03-20

High-resolution observations of the solar chromosphere and transition region often reveal surge-like oscillatory activities above sunspot light bridges (LBs). These oscillations are often interpreted as intermittent plasma jets produced by quasi-periodic magnetic reconnection. We have analyzed the oscillations above an LB in a sunspot using data taken by the Interface Region Imaging Spectrograph . The chromospheric 2796 Å images show surge-like activities above the entire LB at any time, forming an oscillating wall. Within the wall we often see that the core of the Mg ii k 2796.35 Å line first experiences a large blueshift, and then gradually decreases to zero shift before increasing to a redshift of comparable magnitude. Such a behavior suggests that the oscillations are highly nonlinear and likely related to shocks. In the 1400 Å passband, which samples emission mainly from the Si iv ion, the most prominent feature is a bright oscillatory front ahead of the surges. We find a positive correlation between the acceleration and maximum velocity of the moving front, which is consistent with numerical simulations of upward propagating slow-mode shock waves. The Si iv 1402.77 Å line profile is generally enhanced and broadened in the bright front, which might be caused by turbulence generated through compression or by the shocks. These results, together with the fact that the oscillation period stays almost unchanged over a long duration, lead us to propose that the surge-like oscillations above LBs are caused by shocked p-mode waves leaked from the underlying photosphere.

Time series modeling in traffic safety research.

Science.gov (United States)

Lavrenz, Steven M; Vlahogianni, Eleni I; Gkritza, Konstantina; Ke, Yue

2018-08-01

The use of statistical models for analyzing traffic safety (crash) data has been well-established. However, time series techniques have traditionally been underrepresented in the corresponding literature, due to challenges in data collection, along with a limited knowledge of proper methodology. In recent years, new types of high-resolution traffic safety data, especially in measuring driver behavior, have made time series modeling techniques an increasingly salient topic of study. Yet there remains a dearth of information to guide analysts in their use. This paper provides an overview of the state of the art in using time series models in traffic safety research, and discusses some of the fundamental techniques and considerations in classic time series modeling. It also presents ongoing and future opportunities for expanding the use of time series models, and explores newer modeling techniques, including computational intelligence models, which hold promise in effectively handling ever-larger data sets. The information contained herein is meant to guide safety researchers in understanding this broad area of transportation data analysis, and provide a framework for understanding safety trends that can influence policy-making. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Earth's Interaction With the Sun Over the Millennia From Analyses of Historical Sunspot, Auroral and Climate Records

Science.gov (United States)

Yau, K.

2001-12-01

A prolonged decrease in the Sun's irradiance during the Maunder Minimum has been proposed as a cause of the Little Ice Age ({ca} 1600-1800). Eddy [{Science} {192}, 1976, 1189] made this suggestion after noting that very few sunspots were observed from 1645 to 1715, indicative of a weakened Sun. Pre-telescopic Oriental sunspot records go back over 2200 years. Periods when no sunspots were seen have been documented by, {eg}, Clark [{Astron} {7}, 2/1979, 50]. Abundances of C 14 in tree rings and Be10 in ice cores are also good indicators of past solar activity. These isotopes are produced by cosmic rays high in the atmosphere. When the Sun is less active more of them are made and deposited at ground level. There is thus a strong {negative} correlation between their abundances and sunspot counts. Minima of solar activity in tree rings and a south polar ice core have been collated by, {eg}, Bard [{Earth Planet Sci Lett} {150} 1997, 453]; and show striking correspondence with periods when no sunspots were seen, centered at {ca} 900, 1050, 1500, 1700. Pang and Yau [{Eos} {79}, #45, 1998, F149] investigated the Medieval Minimum at 700, using in addition the frequency of auroral sighting7s, a good indicator of solar activity too [Yau, PhD thesis, 1988]; and found that the progression of minima in solar activity goes back to 700. Auroral frequency, C 14 and Be 10 concentrations are also affected by variations in the geomagnetic field. Deposition changes can also influence C 14 and Be 10 abundances. Sunspot counts are thus the only true indicator of solar activity. The Sun's bolometric variations (-0.3% for the Maunder Minimum) can contribute to climatic changes (\\0.5° C for the Little Ice Age)[{eg}, Lean, {GRL} {22}, 1995, 3195]. For times with no thermometer data, temperature can be estimated from, {eg}, Oxygen 18 isotopic abundance in ice cores, which in turn depends on the temperature of the ocean water it evaporated from. We have linked the Medieval Minimum to the cold

Time series prediction: statistical and neural techniques

Science.gov (United States)

Zahirniak, Daniel R.; DeSimio, Martin P.

1996-03-01

In this paper we compare the performance of nonlinear neural network techniques to those of linear filtering techniques in the prediction of time series. Specifically, we compare the results of using the nonlinear systems, known as multilayer perceptron and radial basis function neural networks, with the results obtained using the conventional linear Wiener filter, Kalman filter and Widrow-Hoff adaptive filter in predicting future values of stationary and non- stationary time series. Our results indicate the performance of each type of system is heavily dependent upon the form of the time series being predicted and the size of the system used. In particular, the linear filters perform adequately for linear or near linear processes while the nonlinear systems perform better for nonlinear processes. Since the linear systems take much less time to be developed, they should be tried prior to using the nonlinear systems when the linearity properties of the time series process are unknown.

Effectiveness of Multivariate Time Series Classification Using Shapelets

Directory of Open Access Journals (Sweden)

A. P. Karpenko

2015-01-01

Full Text Available Typically, time series classifiers require signal pre-processing (filtering signals from noise and artifact removal, etc., enhancement of signal features (amplitude, frequency, spectrum, etc., classification of signal features in space using the classical techniques and classification algorithms of multivariate data. We consider a method of classifying time series, which does not require enhancement of the signal features. The method uses the shapelets of time series (time series shapelets i.e. small fragments of this series, which reflect properties of one of its classes most of all.Despite the significant number of publications on the theory and shapelet applications for classification of time series, the task to evaluate the effectiveness of this technique remains relevant. An objective of this publication is to study the effectiveness of a number of modifications of the original shapelet method as applied to the multivariate series classification that is a littlestudied problem. The paper presents the problem statement of multivariate time series classification using the shapelets and describes the shapelet–based basic method of binary classification, as well as various generalizations and proposed modification of the method. It also offers the software that implements a modified method and results of computational experiments confirming the effectiveness of the algorithmic and software solutions.The paper shows that the modified method and the software to use it allow us to reach the classification accuracy of about 85%, at best. The shapelet search time increases in proportion to input data dimension.

Sunspot Light Walls Suppressed by Nearby Brightenings

Energy Technology Data Exchange (ETDEWEB)

Yang, Shuhong; Zhang, Jun; Hou, Yijun; Li, Xiaohong [CAS Key Laboratory of Solar Activity, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012 (China); Erdélyi, Robertus [Solar Physics and Space Plasma Research Centre, School of Mathematics and Statistics, University of Sheffield, Hicks Building, Hounsfield Road, Sheffield S3 7RH (United Kingdom); Yan, Limei, E-mail: shuhongyang@nao.cas.cn [Key Laboratory of Earth and Planetary Physics, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029 (China)

2017-07-01

Light walls, as ensembles of oscillating bright structures rooted in sunspot light bridges, have not been well studied, although they are important for understanding sunspot properties. Using the Interface Region Imaging Spectrograph and Solar Dynamics Observatory observations, here we study the evolution of two oscillating light walls each within its own active region (AR). The emission of each light wall decays greatly after the appearance of adjacent brightenings. For the first light wall, rooted within AR 12565, the average height, amplitude, and oscillation period significantly decrease from 3.5 Mm, 1.7 Mm, and 8.5 minutes to 1.6 Mm, 0.4 Mm, and 3.0 minutes, respectively. For the second light wall, rooted within AR 12597, the mean height, amplitude, and oscillation period of the light wall decrease from 2.1 Mm, 0.5 Mm, and 3.0 minutes to 1.5 Mm, 0.2 Mm, and 2.1 minutes, respectively. Particularly, a part of the second light wall even becomes invisible after the influence of a nearby brightening. These results reveal that the light walls are suppressed by nearby brightenings. Considering the complex magnetic topology in light bridges, we conjecture that the fading of light walls may be caused by a drop in the magnetic pressure, where the flux is canceled by magnetic reconnection at the site of the nearby brightening. Another hypothesis is that the wall fading is due to the suppression of driver source ( p -mode oscillation), resulting from the nearby avalanche of downward particles along reconnected brightening loops.

Time-series-analysis techniques applied to nuclear-material accounting

International Nuclear Information System (INIS)

Pike, D.H.; Morrison, G.W.; Downing, D.J.

1982-05-01

This document is designed to introduce the reader to the applications of Time Series Analysis techniques to Nuclear Material Accountability data. Time series analysis techniques are designed to extract information from a collection of random variables ordered by time by seeking to identify any trends, patterns, or other structure in the series. Since nuclear material accountability data is a time series, one can extract more information using time series analysis techniques than by using other statistical techniques. Specifically, the objective of this document is to examine the applicability of time series analysis techniques to enhance loss detection of special nuclear materials. An introductory section examines the current industry approach which utilizes inventory differences. The error structure of inventory differences is presented. Time series analysis techniques discussed include the Shewhart Control Chart, the Cumulative Summation of Inventory Differences Statistics (CUSUM) and the Kalman Filter and Linear Smoother

Clinical and epidemiological rounds. Time series

Directory of Open Access Journals (Sweden)

León-Álvarez, Alba Luz

2016-07-01

Full Text Available Analysis of time series is a technique that implicates the study of individuals or groups observed in successive moments in time. This type of analysis allows the study of potential causal relationships between different variables that change over time and relate to each other. It is the most important technique to make inferences about the future, predicting, on the basis or what has happened in the past and it is applied in different disciplines of knowledge. Here we discuss different components of time series, the analysis technique and specific examples in health research.

Integer-valued time series

NARCIS (Netherlands)

van den Akker, R.

2007-01-01

This thesis adresses statistical problems in econometrics. The first part contributes statistical methodology for nonnegative integer-valued time series. The second part of this thesis discusses semiparametric estimation in copula models and develops semiparametric lower bounds for a large class of

Robust Forecasting of Non-Stationary Time Series

NARCIS (Netherlands)

Croux, C.; Fried, R.; Gijbels, I.; Mahieu, K.

2010-01-01

This paper proposes a robust forecasting method for non-stationary time series. The time series is modelled using non-parametric heteroscedastic regression, and fitted by a localized MM-estimator, combining high robustness and large efficiency. The proposed method is shown to produce reliable

Fine structure in sunspots. IV. Penumbral grains in speckle reconstucted images

Czech Academy of Sciences Publication Activity Database

Sobotka, Michal; Suetterlin, P.

2001-01-01

Roč. 380, č. 2 (2001), s. 714-718 ISSN 0004-6361 R&D Projects: GA AV ČR KSK2043105; GA AV ČR IAA3003903 Institutional research plan: CEZ:AV0Z1003909 Keywords : sun * sunspots Subject RIV: BN - Astronomy, Celestial Mechanics, Astrophysics Impact factor: 2.790, year: 2000

Are climatological correlations with the Hale double sunspot cycle meaningful

International Nuclear Information System (INIS)

Goldberg, R.A.; Herman, J.R.

1975-09-01

A sunspot cycle which may have been subject to a predicted phase reversal between 1800 and 1880 A.D. is discussed. Several climatological parameters normally correlated with this cycle are examined and do not exhibit a corresponding phase reversal during this period. It is proposed that this apparent discrepency can be resolved by suitable observations during the upcoming half decade

Complex network approach to fractional time series

Energy Technology Data Exchange (ETDEWEB)

Manshour, Pouya [Physics Department, Persian Gulf University, Bushehr 75169 (Iran, Islamic Republic of)

2015-10-15

In order to extract correlation information inherited in stochastic time series, the visibility graph algorithm has been recently proposed, by which a time series can be mapped onto a complex network. We demonstrate that the visibility algorithm is not an appropriate one to study the correlation aspects of a time series. We then employ the horizontal visibility algorithm, as a much simpler one, to map fractional processes onto complex networks. The degree distributions are shown to have parabolic exponential forms with Hurst dependent fitting parameter. Further, we take into account other topological properties such as maximum eigenvalue of the adjacency matrix and the degree assortativity, and show that such topological quantities can also be used to predict the Hurst exponent, with an exception for anti-persistent fractional Gaussian noises. To solve this problem, we take into account the Spearman correlation coefficient between nodes' degrees and their corresponding data values in the original time series.

Temperature mapping of sunspots and pores from speckle reconstructed three colour photometry

NARCIS (Netherlands)

Sütterlin, P.; Wiehr, E.

1998-01-01

The two-dimensional temperature distribution in a highly structured sunspot and in two small umbrae is determined from a three-colour photometry in narrow spectral continua. Disturbing influences from the earth’s atmosphere are removed by speckle masking techniques, yielding a spatial resolution

Introduction to time series analysis and forecasting

CERN Document Server

Montgomery, Douglas C; Kulahci, Murat

2008-01-01

An accessible introduction to the most current thinking in and practicality of forecasting techniques in the context of time-oriented data. Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences. As a result, there is a widespread need for large groups of people in a variety of fields to understand the basic concepts of time series analysis and forecasting. Introduction to Time Series Analysis and Forecasting presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts, and it also bridges the gap between theory and practice by equipping readers with the tools needed to analyze time-oriented data and construct useful, short- to medium-term, statistically based forecasts.

Relation of flare activity to the approach and separation of sunspots in an active region and to its magnetic properties

International Nuclear Information System (INIS)

Markova, E.

1978-01-01

The relation between the flare activity of active regions within the scope of a large complex and the magnetic gradients of these active regions and their daily variations is investigated in the interval of the exceptionally high flare activity occurring in June 1970. New indices, characterizing the active region, were defined, e.g., the instantaneous sunspot-area density and the instantaneous sunspot-number density. These indices were determined on the basis of measurements of the surface containing all sunspots of the complex of active regions enclosed by an envelope. An attempt was made to substitute the surface in the relation for the individual indices by distance. The daily variations of these indices were again compared with the flare activity and some mutual relations were derived. (author)

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance.

Science.gov (United States)

Liu, Yongli; Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

2018-01-01

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy.

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance

Science.gov (United States)

Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

2018-01-01

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy. PMID:29795600

The foundations of modern time series analysis

CERN Document Server

Mills, Terence C

2011-01-01

This book develops the analysis of Time Series from its formal beginnings in the 1890s through to the publication of Box and Jenkins' watershed publication in 1970, showing how these methods laid the foundations for the modern techniques of Time Series analysis that are in use today.

Time series clustering in large data sets

Directory of Open Access Journals (Sweden)

Jiří Fejfar

2011-01-01

Full Text Available The clustering of time series is a widely researched area. There are many methods for dealing with this task. We are actually using the Self-organizing map (SOM with the unsupervised learning algorithm for clustering of time series. After the first experiment (Fejfar, Weinlichová, Šťastný, 2009 it seems that the whole concept of the clustering algorithm is correct but that we have to perform time series clustering on much larger dataset to obtain more accurate results and to find the correlation between configured parameters and results more precisely. The second requirement arose in a need for a well-defined evaluation of results. It seems useful to use sound recordings as instances of time series again. There are many recordings to use in digital libraries, many interesting features and patterns can be found in this area. We are searching for recordings with the similar development of information density in this experiment. It can be used for musical form investigation, cover songs detection and many others applications.The objective of the presented paper is to compare clustering results made with different parameters of feature vectors and the SOM itself. We are describing time series in a simplistic way evaluating standard deviations for separated parts of recordings. The resulting feature vectors are clustered with the SOM in batch training mode with different topologies varying from few neurons to large maps.There are other algorithms discussed, usable for finding similarities between time series and finally conclusions for further research are presented. We also present an overview of the related actual literature and projects.

Transmission of linear regression patterns between time series: from relationship in time series to complex networks.

Science.gov (United States)

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

Lag space estimation in time series modelling

DEFF Research Database (Denmark)

Goutte, Cyril

1997-01-01

The purpose of this article is to investigate some techniques for finding the relevant lag-space, i.e. input information, for time series modelling. This is an important aspect of time series modelling, as it conditions the design of the model through the regressor vector a.k.a. the input layer...

Time-series prediction and applications a machine intelligence approach

CERN Document Server

Konar, Amit

2017-01-01

This book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications. It also proposes new uncertainty management techniques in an economic time-series using type-2 fuzzy sets for prediction of the time-series at a given time point from its preceding value in fluctuating business environments. It employs machine learning to determine repetitively occurring similar structural patterns in the time-series and uses stochastic automaton to predict the most probabilistic structure at a given partition of the time-series. Such predictions help in determining probabilistic moves in a stock index time-series Primarily written for graduate students and researchers in computer science, the book is equally useful for researchers/professionals in business intelligence and stock index prediction. A background of undergraduate level mathematics is presumed, although not mandatory, for most of the sections. Exercises with tips are provided at...

The Temperature - Magnetic Field Relation in Observed and Simulated Sunspots

Czech Academy of Sciences Publication Activity Database

Sobotka, Michal; Rezaei, R.

2017-01-01

Roč. 292, č. 12 (2017), 188/1-188/12 ISSN 0038-0938 R&D Projects: GA ČR(CZ) GA14-04338S; GA MŠk(CZ) 7E13003 EU Projects: European Commission(XE) 312495 - SOLARNET Institutional support: RVO:67985815 Keywords : sunspots * magnetic fields * comparison Subject RIV: BN - Astronomy, Celestial Mechanics, Astrophysics OBOR OECD: Astronomy (including astrophysics,space science) Impact factor: 2.682, year: 2016

A Time Series Forecasting Method

Directory of Open Access Journals (Sweden)

Wang Zhao-Yu

2017-01-01

Full Text Available This paper proposes a novel time series forecasting method based on a weighted self-constructing clustering technique. The weighted self-constructing clustering processes all the data patterns incrementally. If a data pattern is not similar enough to an existing cluster, it forms a new cluster of its own. However, if a data pattern is similar enough to an existing cluster, it is removed from the cluster it currently belongs to and added to the most similar cluster. During the clustering process, weights are learned for each cluster. Given a series of time-stamped data up to time t, we divide it into a set of training patterns. By using the weighted self-constructing clustering, the training patterns are grouped into a set of clusters. To estimate the value at time t + 1, we find the k nearest neighbors of the input pattern and use these k neighbors to decide the estimation. Experimental results are shown to demonstrate the effectiveness of the proposed approach.

Variations and Regularities in the Hemispheric Distributions in Sunspot Groups of Various Classes

Science.gov (United States)

Gao, Peng-Xin

2018-05-01

The present study investigates the variations and regularities in the distributions in sunspot groups (SGs) of various classes in the northern and southern hemispheres from Solar Cycles (SCs) 12 to 23. Here, we use the separation scheme that was introduced by Gao, Li, and Li ( Solar Phys. 292, 124, 2017), which is based on A/U ( A is the corrected area of the SG, and U is the corrected umbral area of the SG), in order to separate SGs into simple SGs (A/U ≤ 4.5) and complex SGs (A/U > 6.2). The time series of Greenwich photoheliographic results from 1875 to 1976 (corresponding to complete SCs 12 - 20) and Debrecen photoheliographic data during the period 1974 - 2015 (corresponding to complete SCs 21 - 23) are used to show the distributions of simple and complex SGs in the northern and southern hemispheres. The main results we obtain are reported as follows: i) the larger of the maximum annual simple SG numbers in the two hemispheres and the larger of the maximum annual complex SG numbers in the two hemispheres occur in different hemispheres during SCs 12, 14, 18, and 19; ii) the relative changing trends of two curves - cumulative SG numbers in the northern and southern hemispheres - for simple SGs are different from those for complex SGs during SCs 12, 14, 18, and 21; and iii) there are discrepancies between the dominant hemispheres of simple and complex SGs for SCs 12, 14, 18, and 21.

Stochastic nature of series of waiting times

Science.gov (United States)

Anvari, Mehrnaz; Aghamohammadi, Cina; Dashti-Naserabadi, H.; Salehi, E.; Behjat, E.; Qorbani, M.; Khazaei Nezhad, M.; Zirak, M.; Hadjihosseini, Ali; Peinke, Joachim; Tabar, M. Reza Rahimi

2013-06-01

Although fluctuations in the waiting time series have been studied for a long time, some important issues such as its long-range memory and its stochastic features in the presence of nonstationarity have so far remained unstudied. Here we find that the “waiting times” series for a given increment level have long-range correlations with Hurst exponents belonging to the interval 1/2time distribution. We find that the logarithmic difference of waiting times series has a short-range correlation, and then we study its stochastic nature using the Markovian method and determine the corresponding Kramers-Moyal coefficients. As an example, we analyze the velocity fluctuations in high Reynolds number turbulence and determine the level dependence of Markov time scales, as well as the drift and diffusion coefficients. We show that the waiting time distributions exhibit power law tails, and we were able to model the distribution with a continuous time random walk.

Efficient Approximate OLAP Querying Over Time Series

DEFF Research Database (Denmark)

Perera, Kasun Baruhupolage Don Kasun Sanjeewa; Hahmann, Martin; Lehner, Wolfgang

2016-01-01

The ongoing trend for data gathering not only produces larger volumes of data, but also increases the variety of recorded data types. Out of these, especially time series, e.g. various sensor readings, have attracted attention in the domains of business intelligence and decision making. As OLAP...... queries play a major role in these domains, it is desirable to also execute them on time series data. While this is not a problem on the conceptual level, it can become a bottleneck with regards to query run-time. In general, processing OLAP queries gets more computationally intensive as the volume...... of data grows. This is a particular problem when querying time series data, which generally contains multiple measures recorded at fine time granularities. Usually, this issue is addressed either by scaling up hardware or by employing workload based query optimization techniques. However, these solutions...

A Dynamic Fuzzy Cluster Algorithm for Time Series

Directory of Open Access Journals (Sweden)

Min Ji

2013-01-01

clustering time series by introducing the definition of key point and improving FCM algorithm. The proposed algorithm works by determining those time series whose class labels are vague and further partitions them into different clusters over time. The main advantage of this approach compared with other existing algorithms is that the property of some time series belonging to different clusters over time can be partially revealed. Results from simulation-based experiments on geographical data demonstrate the excellent performance and the desired results have been obtained. The proposed algorithm can be applied to solve other clustering problems in data mining.

A novel weight determination method for time series data aggregation

Science.gov (United States)

Xu, Paiheng; Zhang, Rong; Deng, Yong

2017-09-01

Aggregation in time series is of great importance in time series smoothing, predicting and other time series analysis process, which makes it crucial to address the weights in times series correctly and reasonably. In this paper, a novel method to obtain the weights in time series is proposed, in which we adopt induced ordered weighted aggregation (IOWA) operator and visibility graph averaging (VGA) operator and linearly combine the weights separately generated by the two operator. The IOWA operator is introduced to the weight determination of time series, through which the time decay factor is taken into consideration. The VGA operator is able to generate weights with respect to the degree distribution in the visibility graph constructed from the corresponding time series, which reflects the relative importance of vertices in time series. The proposed method is applied to two practical datasets to illustrate its merits. The aggregation of Construction Cost Index (CCI) demonstrates the ability of proposed method to smooth time series, while the aggregation of The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) illustrate how proposed method maintain the variation tendency of original data.

Foundations of Sequence-to-Sequence Modeling for Time Series

OpenAIRE

Kuznetsov, Vitaly; Mariet, Zelda

2018-01-01

The availability of large amounts of time series data, paired with the performance of deep-learning algorithms on a broad class of problems, has recently led to significant interest in the use of sequence-to-sequence models for time series forecasting. We provide the first theoretical analysis of this time series forecasting framework. We include a comparison of sequence-to-sequence modeling to classical time series models, and as such our theory can serve as a quantitative guide for practiti...

Climate Prediction Center (CPC) Global Precipitation Time Series

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The global precipitation time series provides time series charts showing observations of daily precipitation as well as accumulated precipitation compared to normal...

Climate Prediction Center (CPC) Global Temperature Time Series

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The global temperature time series provides time series charts using station based observations of daily temperature. These charts provide information about the...

THE MYSTERIOUS CASE OF THE SOLAR ARGON ABUNDANCE NEAR SUNSPOTS IN FLARES

International Nuclear Information System (INIS)

Doschek, G. A.; Warren, H. P.

2016-01-01

Recently we discussed an enhancement of the abundance of Ar xiv relative to Ca xiv near a sunspot during a flare, observed in spectra recorded by the Extreme-ultraviolet Imaging Spectrometer (EIS) on the Hinode spacecraft. The observed Ar xiv/Ca xiv ratio yields an argon/calcium abundance ratio seven times greater than expected from the photospheric abundance. Such a large abundance anomaly is unprecedented in the solar atmosphere. We interpreted this result as being due to an inverse first ionization potential (FIP) effect. In the published work, two lines of Ar xiv were observed, and one line was tentatively identified as an Ar xi line. In this paper, we report observing a similar enhancement in a full-CCD EIS flare spectrum in 13 argon lines that lie within the EIS wavelength ranges. The observed lines include two Ar xi lines, four Ar xiii lines, six Ar xiv lines, and one Ar xv line. The enhancement is far less than reported in Doschek et al. but exhibits similar morphology. The argon abundance is close to a photospheric abundance in the enhanced area, and the abundance could be photospheric. This enhancement occurs in association with a sunspot in a small area only a few arcseconds (1″ = about 700 km) in size. There is no enhancement effect observed in the normally high-FIP sulfur and oxygen line ratios relative to lines of low-FIP elements available to EIS. Calculations of path lengths in the strongest enhanced area in Doschek et al. indicate a depletion of low-FIP elements.

Recurrent Neural Network Applications for Astronomical Time Series

Science.gov (United States)

Protopapas, Pavlos

2017-06-01

The benefits of good predictive models in astronomy lie in early event prediction systems and effective resource allocation. Current time series methods applicable to regular time series have not evolved to generalize for irregular time series. In this talk, I will describe two Recurrent Neural Network methods, Long Short-Term Memory (LSTM) and Echo State Networks (ESNs) for predicting irregular time series. Feature engineering along with a non-linear modeling proved to be an effective predictor. For noisy time series, the prediction is improved by training the network on error realizations using the error estimates from astronomical light curves. In addition to this, we propose a new neural network architecture to remove correlation from the residuals in order to improve prediction and compensate for the noisy data. Finally, I show how to set hyperparameters for a stable and performant solution correctly. In this work, we circumvent this obstacle by optimizing ESN hyperparameters using Bayesian optimization with Gaussian Process priors. This automates the tuning procedure, enabling users to employ the power of RNN without needing an in-depth understanding of the tuning procedure.

Transition Icons for Time-Series Visualization and Exploratory Analysis.

Science.gov (United States)

Nickerson, Paul V; Baharloo, Raheleh; Wanigatunga, Amal A; Manini, Todd M; Tighe, Patrick J; Rashidi, Parisa

2018-03-01

The modern healthcare landscape has seen the rapid emergence of techniques and devices that temporally monitor and record physiological signals. The prevalence of time-series data within the healthcare field necessitates the development of methods that can analyze the data in order to draw meaningful conclusions. Time-series behavior is notoriously difficult to intuitively understand due to its intrinsic high-dimensionality, which is compounded in the case of analyzing groups of time series collected from different patients. Our framework, which we call transition icons, renders common patterns in a visual format useful for understanding the shared behavior within groups of time series. Transition icons are adept at detecting and displaying subtle differences and similarities, e.g., between measurements taken from patients receiving different treatment strategies or stratified by demographics. We introduce various methods that collectively allow for exploratory analysis of groups of time series, while being free of distribution assumptions and including simple heuristics for parameter determination. Our technique extracts discrete transition patterns from symbolic aggregate approXimation representations, and compiles transition frequencies into a bag of patterns constructed for each group. These transition frequencies are normalized and aligned in icon form to intuitively display the underlying patterns. We demonstrate the transition icon technique for two time-series datasets-postoperative pain scores, and hip-worn accelerometer activity counts. We believe transition icons can be an important tool for researchers approaching time-series data, as they give rich and intuitive information about collective time-series behaviors.

Multifractal analysis of visibility graph-based Ito-related connectivity time series.

Science.gov (United States)

Czechowski, Zbigniew; Lovallo, Michele; Telesca, Luciano

2016-02-01

In this study, we investigate multifractal properties of connectivity time series resulting from the visibility graph applied to normally distributed time series generated by the Ito equations with multiplicative power-law noise. We show that multifractality of the connectivity time series (i.e., the series of numbers of links outgoing any node) increases with the exponent of the power-law noise. The multifractality of the connectivity time series could be due to the width of connectivity degree distribution that can be related to the exit time of the associated Ito time series. Furthermore, the connectivity time series are characterized by persistence, although the original Ito time series are random; this is due to the procedure of visibility graph that, connecting the values of the time series, generates persistence but destroys most of the nonlinear correlations. Moreover, the visibility graph is sensitive for detecting wide "depressions" in input time series.

Mathematical foundations of time series analysis a concise introduction

CERN Document Server

Beran, Jan

2017-01-01

This book provides a concise introduction to the mathematical foundations of time series analysis, with an emphasis on mathematical clarity. The text is reduced to the essential logical core, mostly using the symbolic language of mathematics, thus enabling readers to very quickly grasp the essential reasoning behind time series analysis. It appeals to anybody wanting to understand time series in a precise, mathematical manner. It is suitable for graduate courses in time series analysis but is equally useful as a reference work for students and researchers alike.

Time series analysis in the social sciences the fundamentals

CERN Document Server

Shin, Youseop

2017-01-01

Times Series Analysis in the Social Sciences is a practical and highly readable introduction written exclusively for students and researchers whose mathematical background is limited to basic algebra. The book focuses on fundamental elements of time series analysis that social scientists need to understand so they can employ time series analysis for their research and practice. Through step-by-step explanations and using monthly violent crime rates as case studies, this book explains univariate time series from the preliminary visual analysis through the modeling of seasonality, trends, and re

Data imputation analysis for Cosmic Rays time series

Science.gov (United States)

Fernandes, R. C.; Lucio, P. S.; Fernandez, J. H.

2017-05-01

The occurrence of missing data concerning Galactic Cosmic Rays time series (GCR) is inevitable since loss of data is due to mechanical and human failure or technical problems and different periods of operation of GCR stations. The aim of this study was to perform multiple dataset imputation in order to depict the observational dataset. The study has used the monthly time series of GCR Climax (CLMX) and Roma (ROME) from 1960 to 2004 to simulate scenarios of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% and 90% of missing data compared to observed ROME series, with 50 replicates. Then, the CLMX station as a proxy for allocation of these scenarios was used. Three different methods for monthly dataset imputation were selected: AMÉLIA II - runs the bootstrap Expectation Maximization algorithm, MICE - runs an algorithm via Multivariate Imputation by Chained Equations and MTSDI - an Expectation Maximization algorithm-based method for imputation of missing values in multivariate normal time series. The synthetic time series compared with the observed ROME series has also been evaluated using several skill measures as such as RMSE, NRMSE, Agreement Index, R, R2, F-test and t-test. The results showed that for CLMX and ROME, the R2 and R statistics were equal to 0.98 and 0.96, respectively. It was observed that increases in the number of gaps generate loss of quality of the time series. Data imputation was more efficient with MTSDI method, with negligible errors and best skill coefficients. The results suggest a limit of about 60% of missing data for imputation, for monthly averages, no more than this. It is noteworthy that CLMX, ROME and KIEL stations present no missing data in the target period. This methodology allowed reconstructing 43 time series.

Algorithm for Compressing Time-Series Data

Science.gov (United States)

Hawkins, S. Edward, III; Darlington, Edward Hugo

2012-01-01

An algorithm based on Chebyshev polynomials effects lossy compression of time-series data or other one-dimensional data streams (e.g., spectral data) that are arranged in blocks for sequential transmission. The algorithm was developed for use in transmitting data from spacecraft scientific instruments to Earth stations. In spite of its lossy nature, the algorithm preserves the information needed for scientific analysis. The algorithm is computationally simple, yet compresses data streams by factors much greater than two. The algorithm is not restricted to spacecraft or scientific uses: it is applicable to time-series data in general. The algorithm can also be applied to general multidimensional data that have been converted to time-series data, a typical example being image data acquired by raster scanning. However, unlike most prior image-data-compression algorithms, this algorithm neither depends on nor exploits the two-dimensional spatial correlations that are generally present in images. In order to understand the essence of this compression algorithm, it is necessary to understand that the net effect of this algorithm and the associated decompression algorithm is to approximate the original stream of data as a sequence of finite series of Chebyshev polynomials. For the purpose of this algorithm, a block of data or interval of time for which a Chebyshev polynomial series is fitted to the original data is denoted a fitting interval. Chebyshev approximation has two properties that make it particularly effective for compressing serial data streams with minimal loss of scientific information: The errors associated with a Chebyshev approximation are nearly uniformly distributed over the fitting interval (this is known in the art as the "equal error property"); and the maximum deviations of the fitted Chebyshev polynomial from the original data have the smallest possible values (this is known in the art as the "min-max property").

Modeling of Volatility with Non-linear Time Series Model

OpenAIRE

Kim Song Yon; Kim Mun Chol

2013-01-01

In this paper, non-linear time series models are used to describe volatility in financial time series data. To describe volatility, two of the non-linear time series are combined into form TAR (Threshold Auto-Regressive Model) with AARCH (Asymmetric Auto-Regressive Conditional Heteroskedasticity) error term and its parameter estimation is studied.

Layered Ensemble Architecture for Time Series Forecasting.

Science.gov (United States)

Rahman, Md Mustafizur; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin

2016-01-01

Time series forecasting (TSF) has been widely used in many application areas such as science, engineering, and finance. The phenomena generating time series are usually unknown and information available for forecasting is only limited to the past values of the series. It is, therefore, necessary to use an appropriate number of past values, termed lag, for forecasting. This paper proposes a layered ensemble architecture (LEA) for TSF problems. Our LEA consists of two layers, each of which uses an ensemble of multilayer perceptron (MLP) networks. While the first ensemble layer tries to find an appropriate lag, the second ensemble layer employs the obtained lag for forecasting. Unlike most previous work on TSF, the proposed architecture considers both accuracy and diversity of the individual networks in constructing an ensemble. LEA trains different networks in the ensemble by using different training sets with an aim of maintaining diversity among the networks. However, it uses the appropriate lag and combines the best trained networks to construct the ensemble. This indicates LEAs emphasis on accuracy of the networks. The proposed architecture has been tested extensively on time series data of neural network (NN)3 and NN5 competitions. It has also been tested on several standard benchmark time series data. In terms of forecasting accuracy, our experimental results have revealed clearly that LEA is better than other ensemble and nonensemble methods.

Information retrieval for nonstationary data records

Science.gov (United States)

Su, M. Y.

1971-01-01

A review and a critical discussion are made on the existing methods for analysis of nonstationary time series, and a new algorithm for splitting nonstationary time series, is applied to the analysis of sunspot data.

Emergence of Magnetic Flux Generated in a Solar Convective Dynamo. I. The Formation of Sunspots and Active Regions, and The Origin of Their Asymmetries

Energy Technology Data Exchange (ETDEWEB)

Chen, Feng; Rempel, Matthias; Fan, Yuhong, E-mail: chenfeng@ucar.edu [High Altitude Observatory, NCAR, P.O. Box 3000, Boulder, CO, 80307 (United States)

2017-09-10

We present a realistic numerical model of sunspot and active region formation based on the emergence of flux bundles generated in a solar convective dynamo. To this end, we use the magnetic and velocity fields in a horizontal layer near the top boundary of the solar convective dynamo simulation to drive realistic radiative-magnetohydrodynamic simulations of the uppermost layers of the convection zone. The main results are as follows. (1) The emerging flux bundles rise with the mean speed of convective upflows and fragment into small-scale magnetic elements that further rise to the photosphere, where bipolar sunspot pairs are formed through the coalescence of the small-scale magnetic elements. (2) Filamentary penumbral structures form when the sunspot is still growing through ongoing flux emergence. In contrast to the classical Evershed effect, the inflow seems to prevail over the outflow in a large part of the penumbra. (3) A well-formed sunspot is a mostly monolithic magnetic structure that is anchored in a persistent deep-seated downdraft lane. The flow field outside the spot shows a giant vortex ring that comprises an inflow below 15 Mm depth and an outflow above 15 Mm depth. (4) The sunspots successfully reproduce the fundamental properties of the observed solar active regions, including the more coherent leading spots with a stronger field strength, and the correct tilts of bipolar sunspot pairs. These asymmetries can be linked to the intrinsic asymmetries in the magnetic and flow fields adapted from the convective dynamo simulation.

A window-based time series feature extraction method.

Science.gov (United States)

Katircioglu-Öztürk, Deniz; Güvenir, H Altay; Ravens, Ursula; Baykal, Nazife

2017-10-01

This study proposes a robust similarity score-based time series feature extraction method that is termed as Window-based Time series Feature ExtraCtion (WTC). Specifically, WTC generates domain-interpretable results and involves significantly low computational complexity thereby rendering itself useful for densely sampled and populated time series datasets. In this study, WTC is applied to a proprietary action potential (AP) time series dataset on human cardiomyocytes and three precordial leads from a publicly available electrocardiogram (ECG) dataset. This is followed by comparing WTC in terms of predictive accuracy and computational complexity with shapelet transform and fast shapelet transform (which constitutes an accelerated variant of the shapelet transform). The results indicate that WTC achieves a slightly higher classification performance with significantly lower execution time when compared to its shapelet-based alternatives. With respect to its interpretable features, WTC has a potential to enable medical experts to explore definitive common trends in novel datasets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prewhitening of hydroclimatic time series? Implications for inferred change and variability across time scales

Science.gov (United States)

Razavi, Saman; Vogel, Richard

2018-02-01

Prewhitening, the process of eliminating or reducing short-term stochastic persistence to enable detection of deterministic change, has been extensively applied to time series analysis of a range of geophysical variables. Despite the controversy around its utility, methodologies for prewhitening time series continue to be a critical feature of a variety of analyses including: trend detection of hydroclimatic variables and reconstruction of climate and/or hydrology through proxy records such as tree rings. With a focus on the latter, this paper presents a generalized approach to exploring the impact of a wide range of stochastic structures of short- and long-term persistence on the variability of hydroclimatic time series. Through this approach, we examine the impact of prewhitening on the inferred variability of time series across time scales. We document how a focus on prewhitened, residual time series can be misleading, as it can drastically distort (or remove) the structure of variability across time scales. Through examples with actual data, we show how such loss of information in prewhitened time series of tree rings (so-called "residual chronologies") can lead to the underestimation of extreme conditions in climate and hydrology, particularly droughts, reconstructed for centuries preceding the historical period.

DTW-APPROACH FOR UNCORRELATED MULTIVARIATE TIME SERIES IMPUTATION

OpenAIRE

Phan , Thi-Thu-Hong; Poisson Caillault , Emilie; Bigand , André; Lefebvre , Alain

2017-01-01

International audience; Missing data are inevitable in almost domains of applied sciences. Data analysis with missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Some well-known methods for multivariate time series imputation require high correlations between series or their features. In this paper , we propose an approach based on the shape-behaviour relation in low/un-correlated multivariate time series under an assumption of...

Variable Selection in Time Series Forecasting Using Random Forests

Directory of Open Access Journals (Sweden)

Hristos Tyralis

2017-10-01

Full Text Available Time series forecasting using machine learning algorithms has gained popularity recently. Random forest is a machine learning algorithm implemented in time series forecasting; however, most of its forecasting properties have remained unexplored. Here we focus on assessing the performance of random forests in one-step forecasting using two large datasets of short time series with the aim to suggest an optimal set of predictor variables. Furthermore, we compare its performance to benchmarking methods. The first dataset is composed by 16,000 simulated time series from a variety of Autoregressive Fractionally Integrated Moving Average (ARFIMA models. The second dataset consists of 135 mean annual temperature time series. The highest predictive performance of RF is observed when using a low number of recent lagged predictor variables. This outcome could be useful in relevant future applications, with the prospect to achieve higher predictive accuracy.

Trend time-series modeling and forecasting with neural networks.

Science.gov (United States)

Qi, Min; Zhang, G Peter

2008-05-01

Despite its great importance, there has been no general consensus on how to model the trends in time-series data. Compared to traditional approaches, neural networks (NNs) have shown some promise in time-series forecasting. This paper investigates how to best model trend time series using NNs. Four different strategies (raw data, raw data with time index, detrending, and differencing) are used to model various trend patterns (linear, nonlinear, deterministic, stochastic, and breaking trend). We find that with NNs differencing often gives meritorious results regardless of the underlying data generating processes (DGPs). This finding is also confirmed by the real gross national product (GNP) series.

Segmentation of Nonstationary Time Series with Geometric Clustering

DEFF Research Database (Denmark)

Bocharov, Alexei; Thiesson, Bo

2013-01-01

We introduce a non-parametric method for segmentation in regimeswitching time-series models. The approach is based on spectral clustering of target-regressor tuples and derives a switching regression tree, where regime switches are modeled by oblique splits. Such models can be learned efficiently...... from data, where clustering is used to propose one single split candidate at each split level. We use the class of ART time series models to serve as illustration, but because of the non-parametric nature of our segmentation approach, it readily generalizes to a wide range of time-series models that go...

Non-parametric characterization of long-term rainfall time series

Science.gov (United States)

Tiwari, Harinarayan; Pandey, Brij Kishor

2018-03-01

The statistical study of rainfall time series is one of the approaches for efficient hydrological system design. Identifying, and characterizing long-term rainfall time series could aid in improving hydrological systems forecasting. In the present study, eventual statistics was applied for the long-term (1851-2006) rainfall time series under seven meteorological regions of India. Linear trend analysis was carried out using Mann-Kendall test for the observed rainfall series. The observed trend using the above-mentioned approach has been ascertained using the innovative trend analysis method. Innovative trend analysis has been found to be a strong tool to detect the general trend of rainfall time series. Sequential Mann-Kendall test has also been carried out to examine nonlinear trends of the series. The partial sum of cumulative deviation test is also found to be suitable to detect the nonlinear trend. Innovative trend analysis, sequential Mann-Kendall test and partial cumulative deviation test have potential to detect the general as well as nonlinear trend for the rainfall time series. Annual rainfall analysis suggests that the maximum changes in mean rainfall is 11.53% for West Peninsular India, whereas the maximum fall in mean rainfall is 7.8% for the North Mountainous Indian region. The innovative trend analysis method is also capable of finding the number of change point available in the time series. Additionally, we have performed von Neumann ratio test and cumulative deviation test to estimate the departure from homogeneity. Singular spectrum analysis has been applied in this study to evaluate the order of departure from homogeneity in the rainfall time series. Monsoon season (JS) of North Mountainous India and West Peninsular India zones has higher departure from homogeneity and singular spectrum analysis shows the results to be in coherence with the same.

The mutual attraction of magnetic knots. [solar hydromagnetic instability in sunspot regions

Science.gov (United States)

Parker, E. N.

1978-01-01

It is observed that the magnetic knots associated with active regions on the sun have an attraction for each other during the formative period of the active regions, when new magnetic flux is coming to the surface. The attraction disappears when new flux ceases to rise through the surface. Then the magnetic spots and knots tend to come apart, leading to disintegration of the sunspots previously formed. The dissolution of the fields is to be expected, as a consequence of the magnetic repulsion of knots of like polarity and as a consequence of the hydromagnetic exchange instability. The purpose of this paper is to show that the mutual attraction of knots during the formative stages of a sunspot region may be understood as the mutual hydrodynamic attraction of the rising flux tubes. Two rising tubes attract each other, as a consequence of the wake of the leading tube when one is moving behind the other, and as a consequence of the Bernoulli effect when rising side by side.

A Relationship Between the Solar Rotation and Activity Analysed by Tracing Sunspot Groups

Science.gov (United States)

Ruždjak, Domagoj; Brajša, Roman; Sudar, Davor; Skokić, Ivica; Poljančić Beljan, Ivana

2017-12-01

The sunspot position published in the data bases of the Greenwich Photoheliographic Results (GPR), the US Air Force Solar Optical Observing Network and National Oceanic and Atmospheric Administration (USAF/NOAA), and of the Debrecen Photoheliographic Data (DPD) in the period 1874 to 2016 were used to calculate yearly values of the solar differential-rotation parameters A and B. These differential-rotation parameters were compared with the solar-activity level. We found that the Sun rotates more differentially at the minimum than at the maximum of activity during the epoch 1977 - 2016. An inverse correlation between equatorial rotation and solar activity was found using the recently revised sunspot number. The secular decrease of the equatorial rotation rate that accompanies the increase in activity stopped in the last part of the twentieth century. It was noted that when a significant peak in equatorial rotation velocity is observed during activity minimum, the next maximum is weaker than the previous one.

Time Series Decomposition into Oscillation Components and Phase Estimation.

Science.gov (United States)

Matsuda, Takeru; Komaki, Fumiyasu

2017-02-01

Many time series are naturally considered as a superposition of several oscillation components. For example, electroencephalogram (EEG) time series include oscillation components such as alpha, beta, and gamma. We propose a method for decomposing time series into such oscillation components using state-space models. Based on the concept of random frequency modulation, gaussian linear state-space models for oscillation components are developed. In this model, the frequency of an oscillator fluctuates by noise. Time series decomposition is accomplished by this model like the Bayesian seasonal adjustment method. Since the model parameters are estimated from data by the empirical Bayes' method, the amplitudes and the frequencies of oscillation components are determined in a data-driven manner. Also, the appropriate number of oscillation components is determined with the Akaike information criterion (AIC). In this way, the proposed method provides a natural decomposition of the given time series into oscillation components. In neuroscience, the phase of neural time series plays an important role in neural information processing. The proposed method can be used to estimate the phase of each oscillation component and has several advantages over a conventional method based on the Hilbert transform. Thus, the proposed method enables an investigation of the phase dynamics of time series. Numerical results show that the proposed method succeeds in extracting intermittent oscillations like ripples and detecting the phase reset phenomena. We apply the proposed method to real data from various fields such as astronomy, ecology, tidology, and neuroscience.

Introduction to time series analysis and forecasting

CERN Document Server

Montgomery, Douglas C; Kulahci, Murat

2015-01-01

Praise for the First Edition ""…[t]he book is great for readers who need to apply the methods and models presented but have little background in mathematics and statistics."" -MAA Reviews Thoroughly updated throughout, Introduction to Time Series Analysis and Forecasting, Second Edition presents the underlying theories of time series analysis that are needed to analyze time-oriented data and construct real-world short- to medium-term statistical forecasts.    Authored by highly-experienced academics and professionals in engineering statistics, the Second Edition features discussions on both

Multi-Scale Dissemination of Time Series Data

DEFF Research Database (Denmark)

Guo, Qingsong; Zhou, Yongluan; Su, Li

2013-01-01

In this paper, we consider the problem of continuous dissemination of time series data, such as sensor measurements, to a large number of subscribers. These subscribers fall into multiple subscription levels, where each subscription level is specified by the bandwidth constraint of a subscriber......, which is an abstract indicator for both the physical limits and the amount of data that the subscriber would like to handle. To handle this problem, we propose a system framework for multi-scale time series data dissemination that employs a typical tree-based dissemination network and existing time...

RADON CONCENTRATION TIME SERIES MODELING AND APPLICATION DISCUSSION.

Science.gov (United States)

Stránský, V; Thinová, L

2017-11-01

In the year 2010 a continual radon measurement was established at Mladeč Caves in the Czech Republic using a continual radon monitor RADIM3A. In order to model radon time series in the years 2010-15, the Box-Jenkins Methodology, often used in econometrics, was applied. Because of the behavior of radon concentrations (RCs), a seasonal integrated, autoregressive moving averages model with exogenous variables (SARIMAX) has been chosen to model the measured time series. This model uses the time series seasonality, previously acquired values and delayed atmospheric parameters, to forecast RC. The developed model for RC time series is called regARIMA(5,1,3). Model residuals could be retrospectively compared with seismic evidence of local or global earthquakes, which occurred during the RCs measurement. This technique enables us to asses if continuously measured RC could serve an earthquake precursor. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Similarity estimators for irregular and age uncertain time series

Science.gov (United States)

Rehfeld, K.; Kurths, J.

2013-09-01

Paleoclimate time series are often irregularly sampled and age uncertain, which is an important technical challenge to overcome for successful reconstruction of past climate variability and dynamics. Visual comparison and interpolation-based linear correlation approaches have been used to infer dependencies from such proxy time series. While the first is subjective, not measurable and not suitable for the comparison of many datasets at a time, the latter introduces interpolation bias, and both face difficulties if the underlying dependencies are nonlinear. In this paper we investigate similarity estimators that could be suitable for the quantitative investigation of dependencies in irregular and age uncertain time series. We compare the Gaussian-kernel based cross correlation (gXCF, Rehfeld et al., 2011) and mutual information (gMI, Rehfeld et al., 2013) against their interpolation-based counterparts and the new event synchronization function (ESF). We test the efficiency of the methods in estimating coupling strength and coupling lag numerically, using ensembles of synthetic stalagmites with short, autocorrelated, linear and nonlinearly coupled proxy time series, and in the application to real stalagmite time series. In the linear test case coupling strength increases are identified consistently for all estimators, while in the nonlinear test case the correlation-based approaches fail. The lag at which the time series are coupled is identified correctly as the maximum of the similarity functions in around 60-55% (in the linear case) to 53-42% (for the nonlinear processes) of the cases when the dating of the synthetic stalagmite is perfectly precise. If the age uncertainty increases beyond 5% of the time series length, however, the true coupling lag is not identified more often than the others for which the similarity function was estimated. Age uncertainty contributes up to half of the uncertainty in the similarity estimation process. Time series irregularity

Similarity estimators for irregular and age-uncertain time series

Science.gov (United States)

Rehfeld, K.; Kurths, J.

2014-01-01

Paleoclimate time series are often irregularly sampled and age uncertain, which is an important technical challenge to overcome for successful reconstruction of past climate variability and dynamics. Visual comparison and interpolation-based linear correlation approaches have been used to infer dependencies from such proxy time series. While the first is subjective, not measurable and not suitable for the comparison of many data sets at a time, the latter introduces interpolation bias, and both face difficulties if the underlying dependencies are nonlinear. In this paper we investigate similarity estimators that could be suitable for the quantitative investigation of dependencies in irregular and age-uncertain time series. We compare the Gaussian-kernel-based cross-correlation (gXCF, Rehfeld et al., 2011) and mutual information (gMI, Rehfeld et al., 2013) against their interpolation-based counterparts and the new event synchronization function (ESF). We test the efficiency of the methods in estimating coupling strength and coupling lag numerically, using ensembles of synthetic stalagmites with short, autocorrelated, linear and nonlinearly coupled proxy time series, and in the application to real stalagmite time series. In the linear test case, coupling strength increases are identified consistently for all estimators, while in the nonlinear test case the correlation-based approaches fail. The lag at which the time series are coupled is identified correctly as the maximum of the similarity functions in around 60-55% (in the linear case) to 53-42% (for the nonlinear processes) of the cases when the dating of the synthetic stalagmite is perfectly precise. If the age uncertainty increases beyond 5% of the time series length, however, the true coupling lag is not identified more often than the others for which the similarity function was estimated. Age uncertainty contributes up to half of the uncertainty in the similarity estimation process. Time series irregularity

Robust Forecasting of Non-Stationary Time Series

OpenAIRE

Croux, C.; Fried, R.; Gijbels, I.; Mahieu, K.

2010-01-01

This paper proposes a robust forecasting method for non-stationary time series. The time series is modelled using non-parametric heteroscedastic regression, and fitted by a localized MM-estimator, combining high robustness and large efficiency. The proposed method is shown to produce reliable forecasts in the presence of outliers, non-linearity, and heteroscedasticity. In the absence of outliers, the forecasts are only slightly less precise than those based on a localized Least Squares estima...

Time Series Econometrics for the 21st Century

Science.gov (United States)

Hansen, Bruce E.

2017-01-01

The field of econometrics largely started with time series analysis because many early datasets were time-series macroeconomic data. As the field developed, more cross-sectional and longitudinal datasets were collected, which today dominate the majority of academic empirical research. In nonacademic (private sector, central bank, and governmental)…

Effectiveness of firefly algorithm based neural network in time series ...

African Journals Online (AJOL)

Effectiveness of firefly algorithm based neural network in time series forecasting. ... In the experiments, three well known time series were used to evaluate the performance. Results obtained were compared with ... Keywords: Time series, Artificial Neural Network, Firefly Algorithm, Particle Swarm Optimization, Overfitting ...

Time Series Analysis of Insar Data: Methods and Trends

Science.gov (United States)

Osmanoglu, Batuhan; Sunar, Filiz; Wdowinski, Shimon; Cano-Cabral, Enrique

2015-01-01

Time series analysis of InSAR data has emerged as an important tool for monitoring and measuring the displacement of the Earth's surface. Changes in the Earth's surface can result from a wide range of phenomena such as earthquakes, volcanoes, landslides, variations in ground water levels, and changes in wetland water levels. Time series analysis is applied to interferometric phase measurements, which wrap around when the observed motion is larger than one-half of the radar wavelength. Thus, the spatio-temporal ''unwrapping" of phase observations is necessary to obtain physically meaningful results. Several different algorithms have been developed for time series analysis of InSAR data to solve for this ambiguity. These algorithms may employ different models for time series analysis, but they all generate a first-order deformation rate, which can be compared to each other. However, there is no single algorithm that can provide optimal results in all cases. Since time series analyses of InSAR data are used in a variety of applications with different characteristics, each algorithm possesses inherently unique strengths and weaknesses. In this review article, following a brief overview of InSAR technology, we discuss several algorithms developed for time series analysis of InSAR data using an example set of results for measuring subsidence rates in Mexico City.

Interpretation of a compositional time series

Science.gov (United States)

Tolosana-Delgado, R.; van den Boogaart, K. G.

2012-04-01

Common methods for multivariate time series analysis use linear operations, from the definition of a time-lagged covariance/correlation to the prediction of new outcomes. However, when the time series response is a composition (a vector of positive components showing the relative importance of a set of parts in a total, like percentages and proportions), then linear operations are afflicted of several problems. For instance, it has been long recognised that (auto/cross-)correlations between raw percentages are spurious, more dependent on which other components are being considered than on any natural link between the components of interest. Also, a long-term forecast of a composition in models with a linear trend will ultimately predict negative components. In general terms, compositional data should not be treated in a raw scale, but after a log-ratio transformation (Aitchison, 1986: The statistical analysis of compositional data. Chapman and Hill). This is so because the information conveyed by a compositional data is relative, as stated in their definition. The principle of working in coordinates allows to apply any sort of multivariate analysis to a log-ratio transformed composition, as long as this transformation is invertible. This principle is of full application to time series analysis. We will discuss how results (both auto/cross-correlation functions and predictions) can be back-transformed, viewed and interpreted in a meaningful way. One view is to use the exhaustive set of all possible pairwise log-ratios, which allows to express the results into D(D - 1)/2 separate, interpretable sets of one-dimensional models showing the behaviour of each possible pairwise log-ratios. Another view is the interpretation of estimated coefficients or correlations back-transformed in terms of compositions. These two views are compatible and complementary. These issues are illustrated with time series of seasonal precipitation patterns at different rain gauges of the USA

Capturing Structure Implicitly from Time-Series having Limited Data

OpenAIRE

Emaasit, Daniel; Johnson, Matthew

2018-01-01

Scientific fields such as insider-threat detection and highway-safety planning often lack sufficient amounts of time-series data to estimate statistical models for the purpose of scientific discovery. Moreover, the available limited data are quite noisy. This presents a major challenge when estimating time-series models that are robust to overfitting and have well-calibrated uncertainty estimates. Most of the current literature in these fields involve visualizing the time-series for noticeabl...

Solar magnetic field studies using the 12 micron emission lines. II - Stokes profiles and vector field samples in sunspots

Science.gov (United States)

Hewagama, Tilak; Deming, Drake; Jennings, Donald E.; Osherovich, Vladimir; Wiedemann, Gunter; Zipoy, David; Mickey, Donald L.; Garcia, Howard

1993-01-01

Polarimetric observations at 12 microns of two sunpots are reported. The horizontal distribution of parameters such as magnetic field strength, inclination, azimuth, and magnetic field filling factors are presented along with information about the height dependence of the magnetic field strength. Comparisons with contemporary magnetostatic sunspot models are made. The magnetic data are used to estimate the height of 12 micron line formation. From the data, it is concluded that within a stable sunspot there are no regions that are magnetically filamentary, in the sense of containing both strong-field and field-free regions.

Self-affinity in the dengue fever time series

Science.gov (United States)

Azevedo, S. M.; Saba, H.; Miranda, J. G. V.; Filho, A. S. Nascimento; Moret, M. A.

2016-06-01

Dengue is a complex public health problem that is common in tropical and subtropical regions. This disease has risen substantially in the last three decades, and the physical symptoms depict the self-affine behavior of the occurrences of reported dengue cases in Bahia, Brazil. This study uses detrended fluctuation analysis (DFA) to verify the scale behavior in a time series of dengue cases and to evaluate the long-range correlations that are characterized by the power law α exponent for different cities in Bahia, Brazil. The scaling exponent (α) presents different long-range correlations, i.e. uncorrelated, anti-persistent, persistent and diffusive behaviors. The long-range correlations highlight the complex behavior of the time series of this disease. The findings show that there are two distinct types of scale behavior. In the first behavior, the time series presents a persistent α exponent for a one-month period. For large periods, the time series signal approaches subdiffusive behavior. The hypothesis of the long-range correlations in the time series of the occurrences of reported dengue cases was validated. The observed self-affinity is useful as a forecasting tool for future periods through extrapolation of the α exponent behavior. This complex system has a higher predictability in a relatively short time (approximately one month), and it suggests a new tool in epidemiological control strategies. However, predictions for large periods using DFA are hidden by the subdiffusive behavior.

Preliminary results from the orbiting solar observatory 8: Transition-zone dynamics over a sunspot

International Nuclear Information System (INIS)

Bruner, E.C. Jr.; Chipman, E.G.; Lites, B.W.; Rottman, G.J.; Shine, R.A.; Athay, R.G.; White, O.R.

1976-01-01

The University of Colorado experiment aboard OSO-8 observed the C IV 1548 A line in the bright plume over a sunspot. Transient redshifts at 5 minute intervals were studied, but the expected phenomena associated with simple Alfven wave effects were not observed

On the plurality of times: disunified time and the A-series | Nefdt ...

African Journals Online (AJOL)

Then, I attempt to show that disunified time is a problem for a semantics based on the A-series since A-truthmakers are hard to come by in a universe of temporally disconnected time-series. Finally, I provide a novel argument showing that presentists should be particularly fearful of such a universe. South African Journal of ...

Time-series modeling of long-term weight self-monitoring data.

Science.gov (United States)

Helander, Elina; Pavel, Misha; Jimison, Holly; Korhonen, Ilkka

2015-08-01

Long-term self-monitoring of weight is beneficial for weight maintenance, especially after weight loss. Connected weight scales accumulate time series information over long term and hence enable time series analysis of the data. The analysis can reveal individual patterns, provide more sensitive detection of significant weight trends, and enable more accurate and timely prediction of weight outcomes. However, long term self-weighing data has several challenges which complicate the analysis. Especially, irregular sampling, missing data, and existence of periodic (e.g. diurnal and weekly) patterns are common. In this study, we apply time series modeling approach on daily weight time series from two individuals and describe information that can be extracted from this kind of data. We study the properties of weight time series data, missing data and its link to individuals behavior, periodic patterns and weight series segmentation. Being able to understand behavior through weight data and give relevant feedback is desired to lead to positive intervention on health behaviors.

Time series prediction of apple scab using meteorological ...

African Journals Online (AJOL)

A new prediction model for the early warning of apple scab is proposed in this study. The method is based on artificial intelligence and time series prediction. The infection period of apple scab was evaluated as the time series prediction model instead of summation of wetness duration. Also, the relations of different ...

Characterization of time series via Rényi complexity-entropy curves

Science.gov (United States)

Jauregui, M.; Zunino, L.; Lenzi, E. K.; Mendes, R. S.; Ribeiro, H. V.

2018-05-01

One of the most useful tools for distinguishing between chaotic and stochastic time series is the so-called complexity-entropy causality plane. This diagram involves two complexity measures: the Shannon entropy and the statistical complexity. Recently, this idea has been generalized by considering the Tsallis monoparametric generalization of the Shannon entropy, yielding complexity-entropy curves. These curves have proven to enhance the discrimination among different time series related to stochastic and chaotic processes of numerical and experimental nature. Here we further explore these complexity-entropy curves in the context of the Rényi entropy, which is another monoparametric generalization of the Shannon entropy. By combining the Rényi entropy with the proper generalization of the statistical complexity, we associate a parametric curve (the Rényi complexity-entropy curve) with a given time series. We explore this approach in a series of numerical and experimental applications, demonstrating the usefulness of this new technique for time series analysis. We show that the Rényi complexity-entropy curves enable the differentiation among time series of chaotic, stochastic, and periodic nature. In particular, time series of stochastic nature are associated with curves displaying positive curvature in a neighborhood of their initial points, whereas curves related to chaotic phenomena have a negative curvature; finally, periodic time series are represented by vertical straight lines.

Quantifying Selection with Pool-Seq Time Series Data.

Science.gov (United States)

Taus, Thomas; Futschik, Andreas; Schlötterer, Christian

2017-11-01

Allele frequency time series data constitute a powerful resource for unraveling mechanisms of adaptation, because the temporal dimension captures important information about evolutionary forces. In particular, Evolve and Resequence (E&R), the whole-genome sequencing of replicated experimentally evolving populations, is becoming increasingly popular. Based on computer simulations several studies proposed experimental parameters to optimize the identification of the selection targets. No such recommendations are available for the underlying parameters selection strength and dominance. Here, we introduce a highly accurate method to estimate selection parameters from replicated time series data, which is fast enough to be applied on a genome scale. Using this new method, we evaluate how experimental parameters can be optimized to obtain the most reliable estimates for selection parameters. We show that the effective population size (Ne) and the number of replicates have the largest impact. Because the number of time points and sequencing coverage had only a minor effect, we suggest that time series analysis is feasible without major increase in sequencing costs. We anticipate that time series analysis will become routine in E&R studies. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Transformation-cost time-series method for analyzing irregularly sampled data.

Science.gov (United States)

Ozken, Ibrahim; Eroglu, Deniz; Stemler, Thomas; Marwan, Norbert; Bagci, G Baris; Kurths, Jürgen

2015-06-01

Irregular sampling of data sets is one of the challenges often encountered in time-series analysis, since traditional methods cannot be applied and the frequently used interpolation approach can corrupt the data and bias the subsequence analysis. Here we present the TrAnsformation-Cost Time-Series (TACTS) method, which allows us to analyze irregularly sampled data sets without degenerating the quality of the data set. Instead of using interpolation we consider time-series segments and determine how close they are to each other by determining the cost needed to transform one segment into the following one. Using a limited set of operations-with associated costs-to transform the time series segments, we determine a new time series, that is our transformation-cost time series. This cost time series is regularly sampled and can be analyzed using standard methods. While our main interest is the analysis of paleoclimate data, we develop our method using numerical examples like the logistic map and the Rössler oscillator. The numerical data allows us to test the stability of our method against noise and for different irregular samplings. In addition we provide guidance on how to choose the associated costs based on the time series at hand. The usefulness of the TACTS method is demonstrated using speleothem data from the Secret Cave in Borneo that is a good proxy for paleoclimatic variability in the monsoon activity around the maritime continent.

Transformation-cost time-series method for analyzing irregularly sampled data

Science.gov (United States)

Ozken, Ibrahim; Eroglu, Deniz; Stemler, Thomas; Marwan, Norbert; Bagci, G. Baris; Kurths, Jürgen

2015-06-01

Irregular sampling of data sets is one of the challenges often encountered in time-series analysis, since traditional methods cannot be applied and the frequently used interpolation approach can corrupt the data and bias the subsequence analysis. Here we present the TrAnsformation-Cost Time-Series (TACTS) method, which allows us to analyze irregularly sampled data sets without degenerating the quality of the data set. Instead of using interpolation we consider time-series segments and determine how close they are to each other by determining the cost needed to transform one segment into the following one. Using a limited set of operations—with associated costs—to transform the time series segments, we determine a new time series, that is our transformation-cost time series. This cost time series is regularly sampled and can be analyzed using standard methods. While our main interest is the analysis of paleoclimate data, we develop our method using numerical examples like the logistic map and the Rössler oscillator. The numerical data allows us to test the stability of our method against noise and for different irregular samplings. In addition we provide guidance on how to choose the associated costs based on the time series at hand. The usefulness of the TACTS method is demonstrated using speleothem data from the Secret Cave in Borneo that is a good proxy for paleoclimatic variability in the monsoon activity around the maritime continent.

A multidisciplinary database for geophysical time series management

Science.gov (United States)

Montalto, P.; Aliotta, M.; Cassisi, C.; Prestifilippo, M.; Cannata, A.

2013-12-01

The variables collected by a sensor network constitute a heterogeneous data source that needs to be properly organized in order to be used in research and geophysical monitoring. With the time series term we refer to a set of observations of a given phenomenon acquired sequentially in time. When the time intervals are equally spaced one speaks of period or sampling frequency. Our work describes in detail a possible methodology for storage and management of time series using a specific data structure. We designed a framework, hereinafter called TSDSystem (Time Series Database System), in order to acquire time series from different data sources and standardize them within a relational database. The operation of standardization provides the ability to perform operations, such as query and visualization, of many measures synchronizing them using a common time scale. The proposed architecture follows a multiple layer paradigm (Loaders layer, Database layer and Business Logic layer). Each layer is specialized in performing particular operations for the reorganization and archiving of data from different sources such as ASCII, Excel, ODBC (Open DataBase Connectivity), file accessible from the Internet (web pages, XML). In particular, the loader layer performs a security check of the working status of each running software through an heartbeat system, in order to automate the discovery of acquisition issues and other warning conditions. Although our system has to manage huge amounts of data, performance is guaranteed by using a smart partitioning table strategy, that keeps balanced the percentage of data stored in each database table. TSDSystem also contains modules for the visualization of acquired data, that provide the possibility to query different time series on a specified time range, or follow the realtime signal acquisition, according to a data access policy from the users.

Modeling financial time series with S-plus

CERN Document Server

Zivot, Eric

2003-01-01

The field of financial econometrics has exploded over the last decade This book represents an integration of theory, methods, and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics This is the first book to show the power of S-PLUS for the analysis of time series data It is written for researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts Eric Zivot is an associate professor and Gary Waterman Distinguished Scholar in the Economics Department at the University of Washington, and is co-director of the nascent Professional Master's Program in Computational Finance He regularly teaches courses on econometric theory, financial econometrics and time series econometrics, and is the recipient of the He...

Application of Time Series Analysis in Determination of Lag Time in Jahanbin Basin

Directory of Open Access Journals (Sweden)

Seied Yahya Mirzaee

2005-11-01

    One of the important issues that have significant role in study of hydrology of basin is determination of lag time. Lag time has significant role in hydrological studies. Quantity of rainfall related lag time depends on several factors, such as permeability, vegetation cover, catchments slope, rainfall intensity, storm duration and type of rain. Determination of lag time is important parameter in many projects such as dam design and also water resource studies. Lag time of basin could be calculated using various methods. One of these methods is time series analysis of spectral density. The analysis is based on fouries series. The time series is approximated with Sinuous and Cosines functions. In this method harmonically significant quantities with individual frequencies are presented. Spectral density under multiple time series could be used to obtain basin lag time for annual runoff and short-term rainfall fluctuation. A long lag time could be due to snowmelt as well as melting ice due to rainfalls in freezing days. In this research the lag time of Jahanbin basin has been determined using spectral density method. The catchments is subjected to both rainfall and snowfall. For short term rainfall fluctuation with a return period  2, 3, 4 months, the lag times were found 0.18, 0.5 and 0.083 month, respectively.

Modeling Time Series Data for Supervised Learning

Science.gov (United States)

Baydogan, Mustafa Gokce

2012-01-01

Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning…

Empirical method to measure stochasticity and multifractality in nonlinear time series

Science.gov (United States)

Lin, Chih-Hao; Chang, Chia-Seng; Li, Sai-Ping

2013-12-01

An empirical algorithm is used here to study the stochastic and multifractal nature of nonlinear time series. A parameter can be defined to quantitatively measure the deviation of the time series from a Wiener process so that the stochasticity of different time series can be compared. The local volatility of the time series under study can be constructed using this algorithm, and the multifractal structure of the time series can be analyzed by using this local volatility. As an example, we employ this method to analyze financial time series from different stock markets. The result shows that while developed markets evolve very much like an Ito process, the emergent markets are far from efficient. Differences about the multifractal structures and leverage effects between developed and emergent markets are discussed. The algorithm used here can be applied in a similar fashion to study time series of other complex systems.

Clinical time series prediction: Toward a hierarchical dynamical system framework.

Science.gov (United States)

Liu, Zitao; Hauskrecht, Milos

2015-09-01

Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. We tested our framework by first learning the time series model from data for the patients in the training set, and then using it to predict future time series values for the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. Copyright © 2014 Elsevier B.V. All rights reserved.

Clinical time series prediction: towards a hierarchical dynamical system framework

Science.gov (United States)

Liu, Zitao; Hauskrecht, Milos

2014-01-01

Objective Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Materials and methods Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. Results We tested our framework by first learning the time series model from data for the patient in the training set, and then applying the model in order to predict future time series values on the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. Conclusion A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive

Turbulencelike Behavior of Seismic Time Series

International Nuclear Information System (INIS)

Manshour, P.; Saberi, S.; Sahimi, Muhammad; Peinke, J.; Pacheco, Amalio F.; Rahimi Tabar, M. Reza

2009-01-01

We report on a stochastic analysis of Earth's vertical velocity time series by using methods originally developed for complex hierarchical systems and, in particular, for turbulent flows. Analysis of the fluctuations of the detrended increments of the series reveals a pronounced transition in their probability density function from Gaussian to non-Gaussian. The transition occurs 5-10 hours prior to a moderate or large earthquake, hence representing a new and reliable precursor for detecting such earthquakes

Characterizing time series: when Granger causality triggers complex networks

Science.gov (United States)

Ge, Tian; Cui, Yindong; Lin, Wei; Kurths, Jürgen; Liu, Chong

2012-08-01

In this paper, we propose a new approach to characterize time series with noise perturbations in both the time and frequency domains by combining Granger causality and complex networks. We construct directed and weighted complex networks from time series and use representative network measures to describe their physical and topological properties. Through analyzing the typical dynamical behaviors of some physical models and the MIT-BIHMassachusetts Institute of Technology-Beth Israel Hospital. human electrocardiogram data sets, we show that the proposed approach is able to capture and characterize various dynamics and has much potential for analyzing real-world time series of rather short length.

Characterizing time series: when Granger causality triggers complex networks

International Nuclear Information System (INIS)

Ge Tian; Cui Yindong; Lin Wei; Liu Chong; Kurths, Jürgen

2012-01-01

In this paper, we propose a new approach to characterize time series with noise perturbations in both the time and frequency domains by combining Granger causality and complex networks. We construct directed and weighted complex networks from time series and use representative network measures to describe their physical and topological properties. Through analyzing the typical dynamical behaviors of some physical models and the MIT-BIH human electrocardiogram data sets, we show that the proposed approach is able to capture and characterize various dynamics and has much potential for analyzing real-world time series of rather short length. (paper)

Multivariate time series analysis with R and financial applications

CERN Document Server

Tsay, Ruey S

2013-01-01

Since the publication of his first book, Analysis of Financial Time Series, Ruey Tsay has become one of the most influential and prominent experts on the topic of time series. Different from the traditional and oftentimes complex approach to multivariate (MV) time series, this sequel book emphasizes structural specification, which results in simplified parsimonious VARMA modeling and, hence, eases comprehension. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-worl

Influence of the sunspot cycle on the Northern Hemisphere wintertime circulation from long upper-air data sets

Directory of Open Access Journals (Sweden)

Y. Brugnara

2013-07-01

Full Text Available Here we present a study of the 11 yr sunspot cycle's imprint on the Northern Hemisphere atmospheric circulation, using three recently developed gridded upper-air data sets that extend back to the early twentieth century. We find a robust response of the tropospheric late-wintertime circulation to the sunspot cycle, independent from the data set. This response is particularly significant over Europe, although results show that it is not directly related to a North Atlantic Oscillation (NAO modulation; instead, it reveals a significant connection to the more meridional Eurasian pattern (EU. The magnitude of mean seasonal temperature changes over the European land areas locally exceeds 1 K in the lower troposphere over a sunspot cycle. We also analyse surface data to address the question whether the solar signal over Europe is temporally stable for a longer 250 yr period. The results increase our confidence in the existence of an influence of the 11 yr cycle on the European climate, but the signal is much weaker in the first half of the period compared to the second half. The last solar minimum (2005 to 2010, which was not included in our analysis, shows anomalies that are consistent with our statistical results for earlier solar minima.

Measurements of spatial population synchrony: influence of time series transformations.

Science.gov (United States)

Chevalier, Mathieu; Laffaille, Pascal; Ferdy, Jean-Baptiste; Grenouillet, Gaël

2015-09-01

Two mechanisms have been proposed to explain spatial population synchrony: dispersal among populations, and the spatial correlation of density-independent factors (the "Moran effect"). To identify which of these two mechanisms is driving spatial population synchrony, time series transformations (TSTs) of abundance data have been used to remove the signature of one mechanism, and highlight the effect of the other. However, several issues with TSTs remain, and to date no consensus has emerged about how population time series should be handled in synchrony studies. Here, by using 3131 time series involving 34 fish species found in French rivers, we computed several metrics commonly used in synchrony studies to determine whether a large-scale climatic factor (temperature) influenced fish population dynamics at the regional scale, and to test the effect of three commonly used TSTs (detrending, prewhitening and a combination of both) on these metrics. We also tested whether the influence of TSTs on time series and population synchrony levels was related to the features of the time series using both empirical and simulated time series. For several species, and regardless of the TST used, we evidenced a Moran effect on freshwater fish populations. However, these results were globally biased downward by TSTs which reduced our ability to detect significant signals. Depending on the species and the features of the time series, we found that TSTs could lead to contradictory results, regardless of the metric considered. Finally, we suggest guidelines on how population time series should be processed in synchrony studies.

CHROMOSPHERIC MASS MOTIONS AND INTRINSIC SUNSPOT ROTATIONS FOR NOAA ACTIVE REGIONS 10484, 10486, AND 10488 USING ISOON DATA

International Nuclear Information System (INIS)

Hardersen, Paul S.; Balasubramaniam, K. S.; Shkolyar, Svetlana

2013-01-01

This work utilizes Improved Solar Observing Optical Network continuum (630.2 nm) and Hα (656.2 nm) data to: (1) detect and measure intrinsic sunspot rotations occurring in the photosphere and chromosphere, (2) identify and measure chromospheric filament mass motions, and (3) assess any large-scale photospheric and chromospheric mass couplings. Significant results from 2003 October 27-29, using the techniques of Brown et al., indicate significant counter-rotation between the two large sunspots in NOAA AR 10486 on October 29, as well as discrete filament mass motions in NOAA AR 10484 on October 27 that appear to be associated with at least one C-class solar flare

Stochastic time series analysis of hydrology data for water resources

Science.gov (United States)

Sathish, S.; Khadar Babu, S. K.

2017-11-01

The prediction to current publication of stochastic time series analysis in hydrology and seasonal stage. The different statistical tests for predicting the hydrology time series on Thomas-Fiering model. The hydrology time series of flood flow have accept a great deal of consideration worldwide. The concentration of stochastic process areas of time series analysis method are expanding with develop concerns about seasonal periods and global warming. The recent trend by the researchers for testing seasonal periods in the hydrologic flowseries using stochastic process on Thomas-Fiering model. The present article proposed to predict the seasonal periods in hydrology using Thomas-Fiering model.

Neural network versus classical time series forecasting models

Science.gov (United States)

Nor, Maria Elena; Safuan, Hamizah Mohd; Shab, Noorzehan Fazahiyah Md; Asrul, Mohd; Abdullah, Affendi; Mohamad, Nurul Asmaa Izzati; Lee, Muhammad Hisyam

2017-05-01

Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network and classical time series forecasting method namely seasonal autoregressive integrated moving average models was being compared by utilizing gold price data. Moreover, the effect of different data preprocessing on the forecast performance of neural network being examined. The forecast accuracy was evaluated using mean absolute deviation, root mean square error and mean absolute percentage error. It was found that ANN produced the most accurate forecast when Box-Cox transformation was used as data preprocessing.

Nonlinear time series analysis of the human electrocardiogram

International Nuclear Information System (INIS)

Perc, Matjaz

2005-01-01

We analyse the human electrocardiogram with simple nonlinear time series analysis methods that are appropriate for graduate as well as undergraduate courses. In particular, attention is devoted to the notions of determinism and stationarity in physiological data. We emphasize that methods of nonlinear time series analysis can be successfully applied only if the studied data set originates from a deterministic stationary system. After positively establishing the presence of determinism and stationarity in the studied electrocardiogram, we calculate the maximal Lyapunov exponent, thus providing interesting insights into the dynamics of the human heart. Moreover, to facilitate interest and enable the integration of nonlinear time series analysis methods into the curriculum at an early stage of the educational process, we also provide user-friendly programs for each implemented method

Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis.

Science.gov (United States)

Wang, Jin; Sun, Xiangping; Nahavandi, Saeid; Kouzani, Abbas; Wu, Yuchuan; She, Mary

2014-11-01

Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Hidden Markov Models for Time Series An Introduction Using R

CERN Document Server

Zucchini, Walter

2009-01-01

Illustrates the flexibility of HMMs as general-purpose models for time series data. This work presents an overview of HMMs for analyzing time series data, from continuous-valued, circular, and multivariate series to binary data, bounded and unbounded counts and categorical observations.

Constructing ordinal partition transition networks from multivariate time series.

Science.gov (United States)

Zhang, Jiayang; Zhou, Jie; Tang, Ming; Guo, Heng; Small, Michael; Zou, Yong

2017-08-10

A growing number of algorithms have been proposed to map a scalar time series into ordinal partition transition networks. However, most observable phenomena in the empirical sciences are of a multivariate nature. We construct ordinal partition transition networks for multivariate time series. This approach yields weighted directed networks representing the pattern transition properties of time series in velocity space, which hence provides dynamic insights of the underling system. Furthermore, we propose a measure of entropy to characterize ordinal partition transition dynamics, which is sensitive to capturing the possible local geometric changes of phase space trajectories. We demonstrate the applicability of pattern transition networks to capture phase coherence to non-coherence transitions, and to characterize paths to phase synchronizations. Therefore, we conclude that the ordinal partition transition network approach provides complementary insight to the traditional symbolic analysis of nonlinear multivariate time series.

Permutation entropy of finite-length white-noise time series.

Science.gov (United States)

Little, Douglas J; Kane, Deb M

2016-08-01

Permutation entropy (PE) is commonly used to discriminate complex structure from white noise in a time series. While the PE of white noise is well understood in the long time-series limit, analysis in the general case is currently lacking. Here the expectation value and variance of white-noise PE are derived as functions of the number of ordinal pattern trials, N, and the embedding dimension, D. It is demonstrated that the probability distribution of the white-noise PE converges to a χ^{2} distribution with D!-1 degrees of freedom as N becomes large. It is further demonstrated that the PE variance for an arbitrary time series can be estimated as the variance of a related metric, the Kullback-Leibler entropy (KLE), allowing the qualitative N≫D! condition to be recast as a quantitative estimate of the N required to achieve a desired PE calculation precision. Application of this theory to statistical inference is demonstrated in the case of an experimentally obtained noise series, where the probability of obtaining the observed PE value was calculated assuming a white-noise time series. Standard statistical inference can be used to draw conclusions whether the white-noise null hypothesis can be accepted or rejected. This methodology can be applied to other null hypotheses, such as discriminating whether two time series are generated from different complex system states.

Multiresolution analysis of Bursa Malaysia KLCI time series

Science.gov (United States)

Ismail, Mohd Tahir; Dghais, Amel Abdoullah Ahmed

2017-05-01

In general, a time series is simply a sequence of numbers collected at regular intervals over a period. Financial time series data processing is concerned with the theory and practice of processing asset price over time, such as currency, commodity data, and stock market data. The primary aim of this study is to understand the fundamental characteristics of selected financial time series by using the time as well as the frequency domain analysis. After that prediction can be executed for the desired system for in sample forecasting. In this study, multiresolution analysis which the assist of discrete wavelet transforms (DWT) and maximal overlap discrete wavelet transform (MODWT) will be used to pinpoint special characteristics of Bursa Malaysia KLCI (Kuala Lumpur Composite Index) daily closing prices and return values. In addition, further case study discussions include the modeling of Bursa Malaysia KLCI using linear ARIMA with wavelets to address how multiresolution approach improves fitting and forecasting results.

Modelling bursty time series

International Nuclear Information System (INIS)

Vajna, Szabolcs; Kertész, János; Tóth, Bálint

2013-01-01

Many human-related activities show power-law decaying interevent time distribution with exponents usually varying between 1 and 2. We study a simple task-queuing model, which produces bursty time series due to the non-trivial dynamics of the task list. The model is characterized by a priority distribution as an input parameter, which describes the choice procedure from the list. We give exact results on the asymptotic behaviour of the model and we show that the interevent time distribution is power-law decaying for any kind of input distributions that remain normalizable in the infinite list limit, with exponents tunable between 1 and 2. The model satisfies a scaling law between the exponents of interevent time distribution (β) and autocorrelation function (α): α + β = 2. This law is general for renewal processes with power-law decaying interevent time distribution. We conclude that slowly decaying autocorrelation function indicates long-range dependence only if the scaling law is violated. (paper)

Timing calibration and spectral cleaning of LOFAR time series data

NARCIS (Netherlands)

Corstanje, A.; Buitink, S.; Enriquez, J. E.; Falcke, H.; Horandel, J. R.; Krause, M.; Nelles, A.; Rachen, J. P.; Schellart, P.; Scholten, O.; ter Veen, S.; Thoudam, S.; Trinh, T. N. G.

We describe a method for spectral cleaning and timing calibration of short time series data of the voltage in individual radio interferometer receivers. It makes use of phase differences in fast Fourier transform (FFT) spectra across antenna pairs. For strong, localized terrestrial sources these are

Time series momentum and contrarian effects in the Chinese stock market

Science.gov (United States)

Shi, Huai-Long; Zhou, Wei-Xing

2017-10-01

This paper concentrates on the time series momentum or contrarian effects in the Chinese stock market. We evaluate the performance of the time series momentum strategy applied to major stock indices in mainland China and explore the relation between the performance of time series momentum strategies and some firm-specific characteristics. Our findings indicate that there is a time series momentum effect in the short run and a contrarian effect in the long run in the Chinese stock market. The performances of the time series momentum and contrarian strategies are highly dependent on the look-back and holding periods and firm-specific characteristics.

Time-Series Analysis: A Cautionary Tale

Science.gov (United States)

Damadeo, Robert

2015-01-01

Time-series analysis has often been a useful tool in atmospheric science for deriving long-term trends in various atmospherically important parameters (e.g., temperature or the concentration of trace gas species). In particular, time-series analysis has been repeatedly applied to satellite datasets in order to derive the long-term trends in stratospheric ozone, which is a critical atmospheric constituent. However, many of the potential pitfalls relating to the non-uniform sampling of the datasets were often ignored and the results presented by the scientific community have been unknowingly biased. A newly developed and more robust application of this technique is applied to the Stratospheric Aerosol and Gas Experiment (SAGE) II version 7.0 ozone dataset and the previous biases and newly derived trends are presented.

Characterizing interdependencies of multiple time series theory and applications

CERN Document Server

Hosoya, Yuzo; Takimoto, Taro; Kinoshita, Ryo

2017-01-01

This book introduces academic researchers and professionals to the basic concepts and methods for characterizing interdependencies of multiple time series in the frequency domain. Detecting causal directions between a pair of time series and the extent of their effects, as well as testing the non existence of a feedback relation between them, have constituted major focal points in multiple time series analysis since Granger introduced the celebrated definition of causality in view of prediction improvement. Causality analysis has since been widely applied in many disciplines. Although most analyses are conducted from the perspective of the time domain, a frequency domain method introduced in this book sheds new light on another aspect that disentangles the interdependencies between multiple time series in terms of long-term or short-term effects, quantitatively characterizing them. The frequency domain method includes the Granger noncausality test as a special case. Chapters 2 and 3 of the book introduce an i...

A perturbative approach for enhancing the performance of time series forecasting.

Science.gov (United States)

de Mattos Neto, Paulo S G; Ferreira, Tiago A E; Lima, Aranildo R; Vasconcelos, Germano C; Cavalcanti, George D C

2017-04-01

This paper proposes a method to perform time series prediction based on perturbation theory. The approach is based on continuously adjusting an initial forecasting model to asymptotically approximate a desired time series model. First, a predictive model generates an initial forecasting for a time series. Second, a residual time series is calculated as the difference between the original time series and the initial forecasting. If that residual series is not white noise, then it can be used to improve the accuracy of the initial model and a new predictive model is adjusted using residual series. The whole process is repeated until convergence or the residual series becomes white noise. The output of the method is then given by summing up the outputs of all trained predictive models in a perturbative sense. To test the method, an experimental investigation was conducted on six real world time series. A comparison was made with six other methods experimented and ten other results found in the literature. Results show that not only the performance of the initial model is significantly improved but also the proposed method outperforms the other results previously published. Copyright © 2017 Elsevier Ltd. All rights reserved.

Drunk driving detection based on classification of multivariate time series.

Science.gov (United States)

Li, Zhenlong; Jin, Xue; Zhao, Xiaohua

2015-09-01

This paper addresses the problem of detecting drunk driving based on classification of multivariate time series. First, driving performance measures were collected from a test in a driving simulator located in the Traffic Research Center, Beijing University of Technology. Lateral position and steering angle were used to detect drunk driving. Second, multivariate time series analysis was performed to extract the features. A piecewise linear representation was used to represent multivariate time series. A bottom-up algorithm was then employed to separate multivariate time series. The slope and time interval of each segment were extracted as the features for classification. Third, a support vector machine classifier was used to classify driver's state into two classes (normal or drunk) according to the extracted features. The proposed approach achieved an accuracy of 80.0%. Drunk driving detection based on the analysis of multivariate time series is feasible and effective. The approach has implications for drunk driving detection. Copyright © 2015 Elsevier Ltd and National Safety Council. All rights reserved.

Evaluation of scaling invariance embedded in short time series.

Directory of Open Access Journals (Sweden)

Xue Pan

Full Text Available Scaling invariance of time series has been making great contributions in diverse research fields. But how to evaluate scaling exponent from a real-world series is still an open problem. Finite length of time series may induce unacceptable fluctuation and bias to statistical quantities and consequent invalidation of currently used standard methods. In this paper a new concept called correlation-dependent balanced estimation of diffusion entropy is developed to evaluate scale-invariance in very short time series with length ~10(2. Calculations with specified Hurst exponent values of 0.2,0.3,...,0.9 show that by using the standard central moving average de-trending procedure this method can evaluate the scaling exponents for short time series with ignorable bias (≤0.03 and sharp confidential interval (standard deviation ≤0.05. Considering the stride series from ten volunteers along an approximate oval path of a specified length, we observe that though the averages and deviations of scaling exponents are close, their evolutionary behaviors display rich patterns. It has potential use in analyzing physiological signals, detecting early warning signals, and so on. As an emphasis, the our core contribution is that by means of the proposed method one can estimate precisely shannon entropy from limited records.

Evaluation of scaling invariance embedded in short time series.

Science.gov (United States)

Pan, Xue; Hou, Lei; Stephen, Mutua; Yang, Huijie; Zhu, Chenping

2014-01-01

Scaling invariance of time series has been making great contributions in diverse research fields. But how to evaluate scaling exponent from a real-world series is still an open problem. Finite length of time series may induce unacceptable fluctuation and bias to statistical quantities and consequent invalidation of currently used standard methods. In this paper a new concept called correlation-dependent balanced estimation of diffusion entropy is developed to evaluate scale-invariance in very short time series with length ~10(2). Calculations with specified Hurst exponent values of 0.2,0.3,...,0.9 show that by using the standard central moving average de-trending procedure this method can evaluate the scaling exponents for short time series with ignorable bias (≤0.03) and sharp confidential interval (standard deviation ≤0.05). Considering the stride series from ten volunteers along an approximate oval path of a specified length, we observe that though the averages and deviations of scaling exponents are close, their evolutionary behaviors display rich patterns. It has potential use in analyzing physiological signals, detecting early warning signals, and so on. As an emphasis, the our core contribution is that by means of the proposed method one can estimate precisely shannon entropy from limited records.

Modeling Non-Gaussian Time Series with Nonparametric Bayesian Model.

Science.gov (United States)

Xu, Zhiguang; MacEachern, Steven; Xu, Xinyi

2015-02-01

We present a class of Bayesian copula models whose major components are the marginal (limiting) distribution of a stationary time series and the internal dynamics of the series. We argue that these are the two features with which an analyst is typically most familiar, and hence that these are natural components with which to work. For the marginal distribution, we use a nonparametric Bayesian prior distribution along with a cdf-inverse cdf transformation to obtain large support. For the internal dynamics, we rely on the traditionally successful techniques of normal-theory time series. Coupling the two components gives us a family of (Gaussian) copula transformed autoregressive models. The models provide coherent adjustments of time scales and are compatible with many extensions, including changes in volatility of the series. We describe basic properties of the models, show their ability to recover non-Gaussian marginal distributions, and use a GARCH modification of the basic model to analyze stock index return series. The models are found to provide better fit and improved short-range and long-range predictions than Gaussian competitors. The models are extensible to a large variety of fields, including continuous time models, spatial models, models for multiple series, models driven by external covariate streams, and non-stationary models.

Geomechanical time series and its singularity spectrum analysis

Czech Academy of Sciences Publication Activity Database

Lyubushin, Alexei A.; Kaláb, Zdeněk; Lednická, Markéta

2012-01-01

Roč. 47, č. 1 (2012), s. 69-77 ISSN 1217-8977 R&D Projects: GA ČR GA105/09/0089 Institutional research plan: CEZ:AV0Z30860518 Keywords : geomechanical time series * singularity spectrum * time series segmentation * laser distance meter Subject RIV: DC - Siesmology, Volcanology, Earth Structure Impact factor: 0.347, year: 2012 http://www.akademiai.com/content/88v4027758382225/fulltext.pdf

Estudios de clima espacial basados en observaciones solares históricas: recientes progresos y perspectivas

Science.gov (United States)

Vaquero, J. M.

During the last decades, an effort has been made to improve the sunspot number time-series, one of the more useful data set for space climate stud- ies, using historical solar observations. Moreover, not only the sunspot number can be studied using these early solar records. During the last years, historical sources (i.e., sunspot drawings and solar radius measurements) have been also used to study the space climate. Here, I review some recent progress on these issues. In a hand, there are some periods with very few sunspot records and sunspot numbers are not so reliable in these intervals. I discuss the quality of sunspot records during these interesting periods: (a) 1610-1645, (b) 1721-1761, and (c) 1779-1795. On the other hand, I dis- cuss the reliability of early sunspot drawings, sunspot position data, and solar diameter determinations to study long-term variations in our Sun. Fi- nally, some information on historical documents from Argentina and Chile related with space climate are summarised. FULL TEXT IN SPANISH

Pseudo-random bit generator based on lag time series

Science.gov (United States)

García-Martínez, M.; Campos-Cantón, E.

2014-12-01

In this paper, we present a pseudo-random bit generator (PRBG) based on two lag time series of the logistic map using positive and negative values in the bifurcation parameter. In order to hidden the map used to build the pseudo-random series we have used a delay in the generation of time series. These new series when they are mapped xn against xn+1 present a cloud of points unrelated to the logistic map. Finally, the pseudo-random sequences have been tested with the suite of NIST giving satisfactory results for use in stream ciphers.

Non-linear forecasting in high-frequency financial time series

Science.gov (United States)

Strozzi, F.; Zaldívar, J. M.

2005-08-01

A new methodology based on state space reconstruction techniques has been developed for trading in financial markets. The methodology has been tested using 18 high-frequency foreign exchange time series. The results are in apparent contradiction with the efficient market hypothesis which states that no profitable information about future movements can be obtained by studying the past prices series. In our (off-line) analysis positive gain may be obtained in all those series. The trading methodology is quite general and may be adapted to other financial time series. Finally, the steps for its on-line application are discussed.

Analysis of JET ELMy time series

International Nuclear Information System (INIS)

Zvejnieks, G.; Kuzovkov, V.N.

2005-01-01

Full text: Achievement of the planned operational regime in the next generation tokamaks (such as ITER) still faces principal problems. One of the main challenges is obtaining the control of edge localized modes (ELMs), which should lead to both long plasma pulse times and reasonable divertor life time. In order to control ELMs the hypothesis was proposed by Degeling [1] that ELMs exhibit features of chaotic dynamics and thus a standard chaos control methods might be applicable. However, our findings which are based on the nonlinear autoregressive (NAR) model contradict this hypothesis for JET ELMy time-series. In turn, it means that ELM behavior is of a relaxation or random type. These conclusions coincide with our previous results obtained for ASDEX Upgrade time series [2]. [1] A.W. Degeling, Y.R. Martin, P.E. Bak, J. B.Lister, and X. Llobet, Plasma Phys. Control. Fusion 43, 1671 (2001). [2] G. Zvejnieks, V.N. Kuzovkov, O. Dumbrajs, A.W. Degeling, W. Suttrop, H. Urano, and H. Zohm, Physics of Plasmas 11, 5658 (2004)

The Statistical Analysis of Time Series

CERN Document Server

Anderson, T W

2011-01-01

The Wiley Classics Library consists of selected books that have become recognized classics in their respective fields. With these new unabridged and inexpensive editions, Wiley hopes to extend the life of these important works by making them available to future generations of mathematicians and scientists. Currently available in the Series: T. W. Anderson Statistical Analysis of Time Series T. S. Arthanari & Yadolah Dodge Mathematical Programming in Statistics Emil Artin Geometric Algebra Norman T. J. Bailey The Elements of Stochastic Processes with Applications to the Natural Sciences George

Analysis of time series and size of equivalent sample

International Nuclear Information System (INIS)

Bernal, Nestor; Molina, Alicia; Pabon, Daniel; Martinez, Jorge

2004-01-01

In a meteorological context, a first approach to the modeling of time series is to use models of autoregressive type. This allows one to take into account the meteorological persistence or temporal behavior, thereby identifying the memory of the analyzed process. This article seeks to pre-sent the concept of the size of an equivalent sample, which helps to identify in the data series sub periods with a similar structure. Moreover, in this article we examine the alternative of adjusting the variance of the series, keeping in mind its temporal structure, as well as an adjustment to the covariance of two time series. This article presents two examples, the first one corresponding to seven simulated series with autoregressive structure of first order, and the second corresponding to seven meteorological series of anomalies of the air temperature at the surface in two Colombian regions

Scalable Prediction of Energy Consumption using Incremental Time Series Clustering

Energy Technology Data Exchange (ETDEWEB)

Simmhan, Yogesh; Noor, Muhammad Usman

2013-10-09

Time series datasets are a canonical form of high velocity Big Data, and often generated by pervasive sensors, such as found in smart infrastructure. Performing predictive analytics on time series data can be computationally complex, and requires approximation techniques. In this paper, we motivate this problem using a real application from the smart grid domain. We propose an incremental clustering technique, along with a novel affinity score for determining cluster similarity, which help reduce the prediction error for cumulative time series within a cluster. We evaluate this technique, along with optimizations, using real datasets from smart meters, totaling ~700,000 data points, and show the efficacy of our techniques in improving the prediction error of time series data within polynomial time.

Forecasting with nonlinear time series models

DEFF Research Database (Denmark)

Kock, Anders Bredahl; Teräsvirta, Timo

In this paper, nonlinear models are restricted to mean nonlinear parametric models. Several such models popular in time series econo- metrics are presented and some of their properties discussed. This in- cludes two models based on universal approximators: the Kolmogorov- Gabor polynomial model...... applied to economic fore- casting problems, is briefly highlighted. A number of large published studies comparing macroeconomic forecasts obtained using different time series models are discussed, and the paper also contains a small simulation study comparing recursive and direct forecasts in a partic...... and two versions of a simple artificial neural network model. Techniques for generating multi-period forecasts from nonlinear models recursively are considered, and the direct (non-recursive) method for this purpose is mentioned as well. Forecasting with com- plex dynamic systems, albeit less frequently...

Flow and magnetic field properties in the trailing sunspots of active region NOAA 12396

Czech Academy of Sciences Publication Activity Database

Verma, M.; Denker, C.; Boehm, F.; Balthasar, H.; Fischer, C.E.; Kuckein, C.; Gonzalez, N.B.; Berkefeld, T.; Collados Vera, M.; Diercke, A.; Feller, A.; Gonzalez Manrique, S. J.; Hofmann, A.; Lagg, A.; Nicklas, H.; Orozco Suárez, D.; Pator Yabar, A.; Rezaei, R.; Schlichenmaier, R.; Schmidt, D.; Schmidt, W.; Sigwarth, M.; Sobotka, Michal; Solanki, S.K.; Soltau, D.; Staude, J.; Strassmeier, K.G.; Volkmer, R.; von der Lühe, O.; Waldmann, T.A.

2016-01-01

Roč. 337, č. 10 (2016), s. 1090-1098 ISSN 0004-6337. [Dynamic Sun - Exploring the Many Facets of Solar Eruptive Events. Potsdam, 26.10. 2015 -29.10. 2015 ] Institutional support: RVO:67985815 Keywords : Sun * magnetic fields * sunspots Subject RIV: BN - Astronomy, Celestial Mechanics, Astrophysics Impact factor: 0.916, year: 2016

Nonparametric factor analysis of time series

OpenAIRE

Rodríguez-Poo, Juan M.; Linton, Oliver Bruce

1998-01-01

We introduce a nonparametric smoothing procedure for nonparametric factor analaysis of multivariate time series. The asymptotic properties of the proposed procedures are derived. We present an application based on the residuals from the Fair macromodel.

Time Series Outlier Detection Based on Sliding Window Prediction

Directory of Open Access Journals (Sweden)

Yufeng Yu

2014-01-01

Full Text Available In order to detect outliers in hydrological time series data for improving data quality and decision-making quality related to design, operation, and management of water resources, this research develops a time series outlier detection method for hydrologic data that can be used to identify data that deviate from historical patterns. The method first built a forecasting model on the history data and then used it to predict future values. Anomalies are assumed to take place if the observed values fall outside a given prediction confidence interval (PCI, which can be calculated by the predicted value and confidence coefficient. The use of PCI as threshold is mainly on the fact that it considers the uncertainty in the data series parameters in the forecasting model to address the suitable threshold selection problem. The method performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no preclassification of anomalies. Experiments with different hydrologic real-world time series showed that the proposed methods are fast and correctly identify abnormal data and can be used for hydrologic time series analysis.

Metagenomics meets time series analysis: unraveling microbial community dynamics

NARCIS (Netherlands)

Faust, K.; Lahti, L.M.; Gonze, D.; Vos, de W.M.; Raes, J.

2015-01-01

The recent increase in the number of microbial time series studies offers new insights into the stability and dynamics of microbial communities, from the world's oceans to human microbiota. Dedicated time series analysis tools allow taking full advantage of these data. Such tools can reveal periodic

Time series forecasting based on deep extreme learning machine

NARCIS (Netherlands)

Guo, Xuqi; Pang, Y.; Yan, Gaowei; Qiao, Tiezhu; Yang, Guang-Hong; Yang, Dan

2017-01-01

Multi-layer Artificial Neural Networks (ANN) has caught widespread attention as a new method for time series forecasting due to the ability of approximating any nonlinear function. In this paper, a new local time series prediction model is established with the nearest neighbor domain theory, in

False-nearest-neighbors algorithm and noise-corrupted time series

International Nuclear Information System (INIS)

Rhodes, C.; Morari, M.

1997-01-01

The false-nearest-neighbors (FNN) algorithm was originally developed to determine the embedding dimension for autonomous time series. For noise-free computer-generated time series, the algorithm does a good job in predicting the embedding dimension. However, the problem of predicting the embedding dimension when the time-series data are corrupted by noise was not fully examined in the original studies of the FNN algorithm. Here it is shown that with large data sets, even small amounts of noise can lead to incorrect prediction of the embedding dimension. Surprisingly, as the length of the time series analyzed by FNN grows larger, the cause of incorrect prediction becomes more pronounced. An analysis of the effect of noise on the FNN algorithm and a solution for dealing with the effects of noise are given here. Some results on the theoretically correct choice of the FNN threshold are also presented. copyright 1997 The American Physical Society

CauseMap: fast inference of causality from complex time series.

Science.gov (United States)

Maher, M Cyrus; Hernandez, Ryan D

2015-01-01

Background. Establishing health-related causal relationships is a central pursuit in biomedical research. Yet, the interdependent non-linearity of biological systems renders causal dynamics laborious and at times impractical to disentangle. This pursuit is further impeded by the dearth of time series that are sufficiently long to observe and understand recurrent patterns of flux. However, as data generation costs plummet and technologies like wearable devices democratize data collection, we anticipate a coming surge in the availability of biomedically-relevant time series data. Given the life-saving potential of these burgeoning resources, it is critical to invest in the development of open source software tools that are capable of drawing meaningful insight from vast amounts of time series data. Results. Here we present CauseMap, the first open source implementation of convergent cross mapping (CCM), a method for establishing causality from long time series data (≳25 observations). Compared to existing time series methods, CCM has the advantage of being model-free and robust to unmeasured confounding that could otherwise induce spurious associations. CCM builds on Takens' Theorem, a well-established result from dynamical systems theory that requires only mild assumptions. This theorem allows us to reconstruct high dimensional system dynamics using a time series of only a single variable. These reconstructions can be thought of as shadows of the true causal system. If reconstructed shadows can predict points from opposing time series, we can infer that the corresponding variables are providing views of the same causal system, and so are causally related. Unlike traditional metrics, this test can establish the directionality of causation, even in the presence of feedback loops. Furthermore, since CCM can extract causal relationships from times series of, e.g., a single individual, it may be a valuable tool to personalized medicine. We implement CCM in Julia, a

CauseMap: fast inference of causality from complex time series

Directory of Open Access Journals (Sweden)

M. Cyrus Maher

2015-03-01

Full Text Available Background. Establishing health-related causal relationships is a central pursuit in biomedical research. Yet, the interdependent non-linearity of biological systems renders causal dynamics laborious and at times impractical to disentangle. This pursuit is further impeded by the dearth of time series that are sufficiently long to observe and understand recurrent patterns of flux. However, as data generation costs plummet and technologies like wearable devices democratize data collection, we anticipate a coming surge in the availability of biomedically-relevant time series data. Given the life-saving potential of these burgeoning resources, it is critical to invest in the development of open source software tools that are capable of drawing meaningful insight from vast amounts of time series data.Results. Here we present CauseMap, the first open source implementation of convergent cross mapping (CCM, a method for establishing causality from long time series data (≳25 observations. Compared to existing time series methods, CCM has the advantage of being model-free and robust to unmeasured confounding that could otherwise induce spurious associations. CCM builds on Takens’ Theorem, a well-established result from dynamical systems theory that requires only mild assumptions. This theorem allows us to reconstruct high dimensional system dynamics using a time series of only a single variable. These reconstructions can be thought of as shadows of the true causal system. If reconstructed shadows can predict points from opposing time series, we can infer that the corresponding variables are providing views of the same causal system, and so are causally related. Unlike traditional metrics, this test can establish the directionality of causation, even in the presence of feedback loops. Furthermore, since CCM can extract causal relationships from times series of, e.g., a single individual, it may be a valuable tool to personalized medicine. We implement

Time domain series system definition and gear set reliability modeling

International Nuclear Information System (INIS)

Xie, Liyang; Wu, Ningxiang; Qian, Wenxue

2016-01-01

Time-dependent multi-configuration is a typical feature for mechanical systems such as gear trains and chain drives. As a series system, a gear train is distinct from a traditional series system, such as a chain, in load transmission path, system-component relationship, system functioning manner, as well as time-dependent system configuration. Firstly, the present paper defines time-domain series system to which the traditional series system reliability model is not adequate. Then, system specific reliability modeling technique is proposed for gear sets, including component (tooth) and subsystem (tooth-pair) load history description, material priori/posterior strength expression, time-dependent and system specific load-strength interference analysis, as well as statistically dependent failure events treatment. Consequently, several system reliability models are developed for gear sets with different tooth numbers in the scenario of tooth root material ultimate tensile strength failure. The application of the models is discussed in the last part, and the differences between the system specific reliability model and the traditional series system reliability model are illustrated by virtue of several numerical examples. - Highlights: • A new type of series system, i.e. time-domain multi-configuration series system is defined, that is of great significance to reliability modeling. • Multi-level statistical analysis based reliability modeling method is presented for gear transmission system. • Several system specific reliability models are established for gear set reliability estimation. • The differences between the traditional series system reliability model and the new model are illustrated.

Track Irregularity Time Series Analysis and Trend Forecasting

Directory of Open Access Journals (Sweden)

Jia Chaolong

2012-01-01

Full Text Available The combination of linear and nonlinear methods is widely used in the prediction of time series data. This paper analyzes track irregularity time series data by using gray incidence degree models and methods of data transformation, trying to find the connotative relationship between the time series data. In this paper, GM (1,1 is based on first-order, single variable linear differential equations; after an adaptive improvement and error correction, it is used to predict the long-term changing trend of track irregularity at a fixed measuring point; the stochastic linear AR, Kalman filtering model, and artificial neural network model are applied to predict the short-term changing trend of track irregularity at unit section. Both long-term and short-term changes prove that the model is effective and can achieve the expected accuracy.

PRESEE: an MDL/MML algorithm to time-series stream segmenting.

Science.gov (United States)

Xu, Kaikuo; Jiang, Yexi; Tang, Mingjie; Yuan, Changan; Tang, Changjie

2013-01-01

Time-series stream is one of the most common data types in data mining field. It is prevalent in fields such as stock market, ecology, and medical care. Segmentation is a key step to accelerate the processing speed of time-series stream mining. Previous algorithms for segmenting mainly focused on the issue of ameliorating precision instead of paying much attention to the efficiency. Moreover, the performance of these algorithms depends heavily on parameters, which are hard for the users to set. In this paper, we propose PRESEE (parameter-free, real-time, and scalable time-series stream segmenting algorithm), which greatly improves the efficiency of time-series stream segmenting. PRESEE is based on both MDL (minimum description length) and MML (minimum message length) methods, which could segment the data automatically. To evaluate the performance of PRESEE, we conduct several experiments on time-series streams of different types and compare it with the state-of-art algorithm. The empirical results show that PRESEE is very efficient for real-time stream datasets by improving segmenting speed nearly ten times. The novelty of this algorithm is further demonstrated by the application of PRESEE in segmenting real-time stream datasets from ChinaFLUX sensor networks data stream.

Time-varying surrogate data to assess nonlinearity in nonstationary time series: application to heart rate variability.

Science.gov (United States)

Faes, Luca; Zhao, He; Chon, Ki H; Nollo, Giandomenico

2009-03-01

We propose a method to extend to time-varying (TV) systems the procedure for generating typical surrogate time series, in order to test the presence of nonlinear dynamics in potentially nonstationary signals. The method is based on fitting a TV autoregressive (AR) model to the original series and then regressing the model coefficients with random replacements of the model residuals to generate TV AR surrogate series. The proposed surrogate series were used in combination with a TV sample entropy (SE) discriminating statistic to assess nonlinearity in both simulated and experimental time series, in comparison with traditional time-invariant (TIV) surrogates combined with the TIV SE discriminating statistic. Analysis of simulated time series showed that using TIV surrogates, linear nonstationary time series may be erroneously regarded as nonlinear and weak TV nonlinearities may remain unrevealed, while the use of TV AR surrogates markedly increases the probability of a correct interpretation. Application to short (500 beats) heart rate variability (HRV) time series recorded at rest (R), after head-up tilt (T), and during paced breathing (PB) showed: 1) modifications of the SE statistic that were well interpretable with the known cardiovascular physiology; 2) significant contribution of nonlinear dynamics to HRV in all conditions, with significant increase during PB at 0.2 Hz respiration rate; and 3) a disagreement between TV AR surrogates and TIV surrogates in about a quarter of the series, suggesting that nonstationarity may affect HRV recordings and bias the outcome of the traditional surrogate-based nonlinearity test.

Local normalization: Uncovering correlations in non-stationary financial time series

Science.gov (United States)

Schäfer, Rudi; Guhr, Thomas

2010-09-01

The measurement of correlations between financial time series is of vital importance for risk management. In this paper we address an estimation error that stems from the non-stationarity of the time series. We put forward a method to rid the time series of local trends and variable volatility, while preserving cross-correlations. We test this method in a Monte Carlo simulation, and apply it to empirical data for the S&P 500 stocks.

Fuzzy time-series based on Fibonacci sequence for stock price forecasting

Science.gov (United States)

Chen, Tai-Liang; Cheng, Ching-Hsue; Jong Teoh, Hia

2007-07-01

Time-series models have been utilized to make reasonably accurate predictions in the areas of stock price movements, academic enrollments, weather, etc. For promoting the forecasting performance of fuzzy time-series models, this paper proposes a new model, which incorporates the concept of the Fibonacci sequence, the framework of Song and Chissom's model and the weighted method of Yu's model. This paper employs a 5-year period TSMC (Taiwan Semiconductor Manufacturing Company) stock price data and a 13-year period of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) stock index data as experimental datasets. By comparing our forecasting performances with Chen's (Forecasting enrollments based on fuzzy time-series. Fuzzy Sets Syst. 81 (1996) 311-319), Yu's (Weighted fuzzy time-series models for TAIEX forecasting. Physica A 349 (2004) 609-624) and Huarng's (The application of neural networks to forecast fuzzy time series. Physica A 336 (2006) 481-491) models, we conclude that the proposed model surpasses in accuracy these conventional fuzzy time-series models.

Parameterizing unconditional skewness in models for financial time series

DEFF Research Database (Denmark)

He, Changli; Silvennoinen, Annastiina; Teräsvirta, Timo

In this paper we consider the third-moment structure of a class of time series models. It is often argued that the marginal distribution of financial time series such as returns is skewed. Therefore it is of importance to know what properties a model should possess if it is to accommodate...

The Prediction of Teacher Turnover Employing Time Series Analysis.

Science.gov (United States)

Costa, Crist H.

The purpose of this study was to combine knowledge of teacher demographic data with time-series forecasting methods to predict teacher turnover. Moving averages and exponential smoothing were used to forecast discrete time series. The study used data collected from the 22 largest school districts in Iowa, designated as FACT schools. Predictions…

Stacked Heterogeneous Neural Networks for Time Series Forecasting

Directory of Open Access Journals (Sweden)

Florin Leon

2010-01-01

Full Text Available A hybrid model for time series forecasting is proposed. It is a stacked neural network, containing one normal multilayer perceptron with bipolar sigmoid activation functions, and the other with an exponential activation function in the output layer. As shown by the case studies, the proposed stacked hybrid neural model performs well on a variety of benchmark time series. The combination of weights of the two stack components that leads to optimal performance is also studied.

Chaotic time series prediction: From one to another

International Nuclear Information System (INIS)

Zhao Pengfei; Xing Lei; Yu Jun

2009-01-01

In this Letter, a new local linear prediction model is proposed to predict a chaotic time series of a component x(t) by using the chaotic time series of another component y(t) in the same system with x(t). Our approach is based on the phase space reconstruction coming from the Takens embedding theorem. To illustrate our results, we present an example of Lorenz system and compare with the performance of the original local linear prediction model.

Grammar-based feature generation for time-series prediction

CERN Document Server

De Silva, Anthony Mihirana

2015-01-01

This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. Application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series. The performance of machine learning techniques, among other things, depends on suitable engineering of features. This book proposes a systematic way for generating suitable features using context-free grammar. A number of feature selection criteria are investigated and a hybrid feature generation and selection algorithm using grammatical evolution is proposed. The book contains graphical illustrations to explain the feature generation process. The proposed approaches are demonstrated by predicting the closing price of major stock market indices, peak electricity load and net hourly foreign exchange client trade volume. The proposed method ...

Forecasting autoregressive time series under changing persistence

DEFF Research Database (Denmark)

Kruse, Robinson

Changing persistence in time series models means that a structural change from nonstationarity to stationarity or vice versa occurs over time. Such a change has important implications for forecasting, as negligence may lead to inaccurate model predictions. This paper derives generally applicable...

Recurrent Neural Networks for Multivariate Time Series with Missing Values.

Science.gov (United States)

Che, Zhengping; Purushotham, Sanjay; Cho, Kyunghyun; Sontag, David; Liu, Yan

2018-04-17

Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a., informative missingness. There is very limited work on exploiting the missing patterns for effective imputation and improving prediction performance. In this paper, we develop novel deep learning models, namely GRU-D, as one of the early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a state-of-the-art recurrent neural network. It takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results. Experiments of time series classification tasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic datasets demonstrate that our models achieve state-of-the-art performance and provide useful insights for better understanding and utilization of missing values in time series analysis.

Conditional time series forecasting with convolutional neural networks

NARCIS (Netherlands)

A. Borovykh (Anastasia); S.M. Bohte (Sander); C.W. Oosterlee (Cornelis)

2017-01-01

textabstractForecasting financial time series using past observations has been a significant topic of interest. While temporal relationships in the data exist, they are difficult to analyze and predict accurately due to the non-linear trends and noise present in the series. We propose to learn these

Time Series Analysis of Wheat Futures Reward in China

Institute of Scientific and Technical Information of China (English)

无

2005-01-01

Different from the fact that the main researches are focused on single futures contract and lack of the comparison of different periods, this paper described the statistical characteristics of wheat futures reward time series of Zhengzhou Commodity Exchange in recent three years. Besides the basic statistic analysis, the paper used the GARCH and EGARCH model to describe the time series which had the ARCH effect and analyzed the persistence of volatility shocks and the leverage effect. The results showed that compared with that of normal one,wheat futures reward series were abnormality, leptokurtic and thick tail distribution. The study also found that two-part of the reward series had no autocorrelation. Among the six correlative series, three ones presented the ARCH effect. By using of the Auto-regressive Distributed Lag Model, GARCH model and EGARCH model, the paper demonstrates the persistence of volatility shocks and the leverage effect on the wheat futures reward time series. The results reveal that on the one hand, the statistical characteristics of the wheat futures reward are similar to the aboard mature futures market as a whole. But on the other hand, the results reflect some shortages such as the immatureness and the over-control by the government in the Chinese future market.

forecasting with nonlinear time series model: a monte-carlo

African Journals Online (AJOL)

PUBLICATIONS1

erated recursively up to any step greater than one. For nonlinear time series model, point forecast for step one can be done easily like in the linear case but forecast for a step greater than or equal to ..... London. Franses, P. H. (1998). Time series models for business and Economic forecasting, Cam- bridge University press.

Time series analysis of temporal networks

Science.gov (United States)

Sikdar, Sandipan; Ganguly, Niloy; Mukherjee, Animesh

2016-01-01

A common but an important feature of all real-world networks is that they are temporal in nature, i.e., the network structure changes over time. Due to this dynamic nature, it becomes difficult to propose suitable growth models that can explain the various important characteristic properties of these networks. In fact, in many application oriented studies only knowing these properties is sufficient. For instance, if one wishes to launch a targeted attack on a network, this can be done even without the knowledge of the full network structure; rather an estimate of some of the properties is sufficient enough to launch the attack. We, in this paper show that even if the network structure at a future time point is not available one can still manage to estimate its properties. We propose a novel method to map a temporal network to a set of time series instances, analyze them and using a standard forecast model of time series, try to predict the properties of a temporal network at a later time instance. To our aim, we consider eight properties such as number of active nodes, average degree, clustering coefficient etc. and apply our prediction framework on them. We mainly focus on the temporal network of human face-to-face contacts and observe that it represents a stochastic process with memory that can be modeled as Auto-Regressive-Integrated-Moving-Average (ARIMA). We use cross validation techniques to find the percentage accuracy of our predictions. An important observation is that the frequency domain properties of the time series obtained from spectrogram analysis could be used to refine the prediction framework by identifying beforehand the cases where the error in prediction is likely to be high. This leads to an improvement of 7.96% (for error level ≤20%) in prediction accuracy on an average across all datasets. As an application we show how such prediction scheme can be used to launch targeted attacks on temporal networks. Contribution to the Topical Issue

The Hierarchical Spectral Merger Algorithm: A New Time Series Clustering Procedure

KAUST Repository

Euá n, Carolina; Ombao, Hernando; Ortega, Joaquí n

2018-01-01

We present a new method for time series clustering which we call the Hierarchical Spectral Merger (HSM) method. This procedure is based on the spectral theory of time series and identifies series that share similar oscillations or waveforms

Notes on economic time series analysis system theoretic perspectives

CERN Document Server

Aoki, Masanao

1983-01-01

In seminars and graduate level courses I have had several opportunities to discuss modeling and analysis of time series with economists and economic graduate students during the past several years. These experiences made me aware of a gap between what economic graduate students are taught about vector-valued time series and what is available in recent system literature. Wishing to fill or narrow the gap that I suspect is more widely spread than my personal experiences indicate, I have written these notes to augment and reor­ ganize materials I have given in these courses and seminars. I have endeavored to present, in as much a self-contained way as practicable, a body of results and techniques in system theory that I judge to be relevant and useful to economists interested in using time series in their research. I have essentially acted as an intermediary and interpreter of system theoretic results and perspectives in time series by filtering out non-essential details, and presenting coherent accounts of wha...

Dynamical analysis and visualization of tornadoes time series.

Directory of Open Access Journals (Sweden)

António M Lopes

Full Text Available In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns.

Dynamical analysis and visualization of tornadoes time series.

Science.gov (United States)

Lopes, António M; Tenreiro Machado, J A

2015-01-01

In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns.

"Observation Obscurer" - Time Series Viewer, Editor and Processor

Science.gov (United States)

Andronov, I. L.

The program is described, which contains a set of subroutines suitable for East viewing and interactive filtering and processing of regularly and irregularly spaced time series. Being a 32-bit DOS application, it may be used as a default fast viewer/editor of time series in any compute shell ("commander") or in Windows. It allows to view the data in the "time" or "phase" mode, to remove ("obscure") or filter outstanding bad points; to make scale transformations and smoothing using few methods (e.g. mean with phase binning, determination of the statistically opti- mal number of phase bins; "running parabola" (Andronov, 1997, As. Ap. Suppl, 125, 207) fit and to make time series analysis using some methods, e.g. correlation, autocorrelation and histogram analysis: determination of extrema etc. Some features have been developed specially for variable star observers, e.g. the barycentric correction, the creation and fast analysis of "OC" diagrams etc. The manual for "hot keys" is presented. The computer code was compiled with a 32-bit Free Pascal (www.freepascal.org).

Modelling road accidents: An approach using structural time series

Science.gov (United States)

Junus, Noor Wahida Md; Ismail, Mohd Tahir

2014-09-01

In this paper, the trend of road accidents in Malaysia for the years 2001 until 2012 was modelled using a structural time series approach. The structural time series model was identified using a stepwise method, and the residuals for each model were tested. The best-fitted model was chosen based on the smallest Akaike Information Criterion (AIC) and prediction error variance. In order to check the quality of the model, a data validation procedure was performed by predicting the monthly number of road accidents for the year 2012. Results indicate that the best specification of the structural time series model to represent road accidents is the local level with a seasonal model.

Multiscale Poincaré plots for visualizing the structure of heartbeat time series.

Science.gov (United States)

Henriques, Teresa S; Mariani, Sara; Burykin, Anton; Rodrigues, Filipa; Silva, Tiago F; Goldberger, Ary L

2016-02-09

Poincaré delay maps are widely used in the analysis of cardiac interbeat interval (RR) dynamics. To facilitate visualization of the structure of these time series, we introduce multiscale Poincaré (MSP) plots. Starting with the original RR time series, the method employs a coarse-graining procedure to create a family of time series, each of which represents the system's dynamics in a different time scale. Next, the Poincaré plots are constructed for the original and the coarse-grained time series. Finally, as an optional adjunct, color can be added to each point to represent its normalized frequency. We illustrate the MSP method on simulated Gaussian white and 1/f noise time series. The MSP plots of 1/f noise time series reveal relative conservation of the phase space area over multiple time scales, while those of white noise show a marked reduction in area. We also show how MSP plots can be used to illustrate the loss of complexity when heartbeat time series from healthy subjects are compared with those from patients with chronic (congestive) heart failure syndrome or with atrial fibrillation. This generalized multiscale approach to Poincaré plots may be useful in visualizing other types of time series.

Fully Automated Sunspot Detection and Classification Using SDO HMI Imagery in MATLAB

Science.gov (United States)

2014-03-27

initiating the java program scripted to communicate with the SOON telescope used for continual observation of the sun. The SOON telescope is used at...proximity of spots refers to the angular separation between different spots that could make up a group. The area of each sunspot means the total area...degrees and the different magnetic polarities of each spot being considered. For a spot pair that has the same polarity and small angular separation

Time series patterns and language support in DBMS

Science.gov (United States)

Telnarova, Zdenka

2017-07-01

This contribution is focused on pattern type Time Series as a rich in semantics representation of data. Some example of implementation of this pattern type in traditional Data Base Management Systems is briefly presented. There are many approaches how to manipulate with patterns and query patterns. Crucial issue can be seen in systematic approach to pattern management and specific pattern query language which takes into consideration semantics of patterns. Query language SQL-TS for manipulating with patterns is shown on Time Series data.

Two-fractal overlap time series: Earthquakes and market crashes

Indian Academy of Sciences (India)

velocity over the other and time series of stock prices. An anticipation method for some of the crashes have been proposed here, based on these observations. Keywords. Cantor set; time series; earthquake; market crash. PACS Nos 05.00; 02.50.-r; 64.60; 89.65.Gh; 95.75.Wx. 1. Introduction. Capturing dynamical patterns of ...

Nonlinear time series analysis with R

CERN Document Server

Huffaker, Ray; Rosa, Rodolfo

2017-01-01

In the process of data analysis, the investigator is often facing highly-volatile and random-appearing observed data. A vast body of literature shows that the assumption of underlying stochastic processes was not necessarily representing the nature of the processes under investigation and, when other tools were used, deterministic features emerged. Non Linear Time Series Analysis (NLTS) allows researchers to test whether observed volatility conceals systematic non linear behavior, and to rigorously characterize governing dynamics. Behavioral patterns detected by non linear time series analysis, along with scientific principles and other expert information, guide the specification of mechanistic models that serve to explain real-world behavior rather than merely reproducing it. Often there is a misconception regarding the complexity of the level of mathematics needed to understand and utilize the tools of NLTS (for instance Chaos theory). However, mathematics used in NLTS is much simpler than many other subjec...

InSAR Deformation Time Series Processed On-Demand in the Cloud

Science.gov (United States)

Horn, W. B.; Weeden, R.; Dimarchi, H.; Arko, S. A.; Hogenson, K.

2017-12-01

During this past year, ASF has developed a cloud-based on-demand processing system known as HyP3 (http://hyp3.asf.alaska.edu/), the Hybrid Pluggable Processing Pipeline, for Synthetic Aperture Radar (SAR) data. The system makes it easy for a user who doesn't have the time or inclination to install and use complex SAR processing software to leverage SAR data in their research or operations. One such processing algorithm is generation of a deformation time series product, which is a series of images representing ground displacements over time, which can be computed using a time series of interferometric SAR (InSAR) products. The set of software tools necessary to generate this useful product are difficult to install, configure, and use. Moreover, for a long time series with many images, the processing of just the interferograms can take days. Principally built by three undergraduate students at the ASF DAAC, the deformation time series processing relies the new Amazon Batch service, which enables processing of jobs with complex interconnected dependencies in a straightforward and efficient manner. In the case of generating a deformation time series product from a stack of single-look complex SAR images, the system uses Batch to serialize the up-front processing, interferogram generation, optional tropospheric correction, and deformation time series generation. The most time consuming portion is the interferogram generation, because even for a fairly small stack of images many interferograms need to be processed. By using AWS Batch, the interferograms are all generated in parallel; the entire process completes in hours rather than days. Additionally, the individual interferograms are saved in Amazon's cloud storage, so that when new data is acquired in the stack, an updated time series product can be generated with minimal addiitonal processing. This presentation will focus on the development techniques and enabling technologies that were used in developing the time

TRANSITION-REGION/CORONAL SIGNATURES AND MAGNETIC SETTING OF SUNSPOT PENUMBRAL JETS: HINODE (SOT/FG), Hi-C, AND SDO/AIA OBSERVATIONS

International Nuclear Information System (INIS)

Tiwari, Sanjiv K.; Moore, Ronald L.; Winebarger, Amy R.; Alpert, Shane E.

2016-01-01

Penumbral microjets (PJs) are transient narrow bright features in the chromosphere of sunspot penumbrae, first characterized by Katsukawa et al. using the Ca ii H-line filter on Hinode's Solar Optical Telescope (SOT). It was proposed that the PJs form as a result of reconnection between two magnetic components of penumbrae (spines and interspines), and that they could contribute to the transition region (TR) and coronal heating above sunspot penumbrae. We propose a modified picture of formation of PJs based on recent results on the internal structure of sunspot penumbral filaments. Using data of a sunspot from Hinode/SOT, High Resolution Coronal Imager, and different passbands of the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory, we examine whether PJs have signatures in the TR and corona. We find hardly any discernible signature of normal PJs in any AIA passbands, except for a few of them showing up in the 1600 Å images. However, we discovered exceptionally stronger jets with similar lifetimes but bigger sizes (up to 600 km wide) occurring repeatedly in a few locations in the penumbra, where evidence of patches of opposite-polarity fields in the tails of some penumbral filaments is seen in Stokes-V images. These tail PJs do display signatures in the TR. Whether they have any coronal-temperature plasma is unclear. We infer that none of the PJs, including the tail PJs, directly heat the corona in active regions significantly, but any penumbral jet might drive some coronal heating indirectly via the generation of Alfvén waves and/or braiding of the coronal field

Vector bilinear autoregressive time series model and its superiority ...

African Journals Online (AJOL)

In this research, a vector bilinear autoregressive time series model was proposed and used to model three revenue series (X1, X2, X3) . The “orders” of the three series were identified on the basis of the distribution of autocorrelation and partial autocorrelation functions and were used to construct the vector bilinear models.

25 years of time series forecasting

NARCIS (Netherlands)

de Gooijer, J.G.; Hyndman, R.J.

2006-01-01

We review the past 25 years of research into time series forecasting. In this silver jubilee issue, we naturally highlight results published in journals managed by the International Institute of Forecasters (Journal of Forecasting 1982-1985 and International Journal of Forecasting 1985-2005). During

Markov Trends in Macroeconomic Time Series

NARCIS (Netherlands)

R. Paap (Richard)

1997-01-01

textabstractMany macroeconomic time series are characterised by long periods of positive growth, expansion periods, and short periods of negative growth, recessions. A popular model to describe this phenomenon is the Markov trend, which is a stochastic segmented trend where the slope depends on the

Modeling seasonality in bimonthly time series

NARCIS (Netherlands)

Ph.H.B.F. Franses (Philip Hans)

1992-01-01

textabstractA recurring issue in modeling seasonal time series variables is the choice of the most adequate model for the seasonal movements. One selection method for quarterly data is proposed in Hylleberg et al. (1990). Market response models are often constructed for bimonthly variables, and

On clustering fMRI time series

DEFF Research Database (Denmark)

Goutte, Cyril; Toft, Peter Aundal; Rostrup, E.

1999-01-01

Analysis of fMRI time series is often performed by extracting one or more parameters for the individual voxels. Methods based, e.g., on various statistical tests are then used to yield parameters corresponding to probability of activation or activation strength. However, these methods do...

FALSE DETERMINATIONS OF CHAOS IN SHORT NOISY TIME SERIES. (R828745)

Science.gov (United States)

A method (NEMG) proposed in 1992 for diagnosing chaos in noisy time series with 50 or fewer observations entails fitting the time series with an empirical function which predicts an observation in the series from previous observations, and then estimating the rate of divergenc...

Multiscale multifractal multiproperty analysis of financial time series based on Rényi entropy

Science.gov (United States)

Yujun, Yang; Jianping, Li; Yimei, Yang

This paper introduces a multiscale multifractal multiproperty analysis based on Rényi entropy (3MPAR) method to analyze short-range and long-range characteristics of financial time series, and then applies this method to the five time series of five properties in four stock indices. Combining the two analysis techniques of Rényi entropy and multifractal detrended fluctuation analysis (MFDFA), the 3MPAR method focuses on the curves of Rényi entropy and generalized Hurst exponent of five properties of four stock time series, which allows us to study more universal and subtle fluctuation characteristics of financial time series. By analyzing the curves of the Rényi entropy and the profiles of the logarithm distribution of MFDFA of five properties of four stock indices, the 3MPAR method shows some fluctuation characteristics of the financial time series and the stock markets. Then, it also shows a richer information of the financial time series by comparing the profile of five properties of four stock indices. In this paper, we not only focus on the multifractality of time series but also the fluctuation characteristics of the financial time series and subtle differences in the time series of different properties. We find that financial time series is far more complex than reported in some research works using one property of time series.

A Literature Survey of Early Time Series Classification and Deep Learning

OpenAIRE

Santos, Tiago; Kern, Roman

2017-01-01

This paper provides an overview of current literature on time series classification approaches, in particular of early time series classification. A very common and effective time series classification approach is the 1-Nearest Neighbor classier, with different distance measures such as the Euclidean or dynamic time warping distances. This paper starts by reviewing these baseline methods. More recently, with the gain in popularity in the application of deep neural networks to the eld of...

Signal Processing for Time-Series Functions on a Graph