WorldWideScience

Sample records for hydrographic time series

  1. Series distance – an intuitive metric to quantify hydrograph similarity in terms of occurrence, amplitude and timing of hydrological events

    Directory of Open Access Journals (Sweden)

    U. Ehret

    2011-03-01

    Full Text Available Applying metrics to quantify the similarity or dissimilarity of hydrographs is a central task in hydrological modelling, used both in model calibration and the evaluation of simulations or forecasts. Motivated by the shortcomings of standard objective metrics such as the Root Mean Square Error (RMSE or the Mean Absolute Peak Time Error (MAPTE and the advantages of visual inspection as a powerful tool for simultaneous, case-specific and multi-criteria (yet subjective evaluation, we propose a new objective metric termed Series Distance, which is in close accordance with visual evaluation. The Series Distance quantifies the similarity of two hydrographs neither in a time-aggregated nor in a point-by-point manner, but on the scale of hydrological events. It consists of three parts, namely a Threat Score which evaluates overall agreement of event occurrence, and the overall distance of matching observed and simulated events with respect to amplitude and timing. The novelty of the latter two is the way in which matching point pairs on the observed and simulated hydrographs are identified: not by equality in time (as is the case with the RMSE, but by the same relative position in matching segments (rise or recession of the event, indicating the same underlying hydrological process. Thus, amplitude and timing errors are calculated simultaneously but separately, from point pairs that also match visually, considering complete events rather than only individual points (as is the case with MAPTE. Relative weights can freely be assigned to each component of the Series Distance, which allows (subjective customization of the metric to various fields of application, but in a traceable way. Each of the three components of the Series Distance can be used in an aggregated or non-aggregated way, which makes the Series Distance a suitable tool for differentiated, process-based model diagnostics.

    After discussing the applicability of established time series

  2. The Santander Atlantic Time-Series Station (SATS): A Time Series combination of a monthly hydrographic Station and The Biscay AGL Oceanic Observatory.

    Science.gov (United States)

    Lavin, Alicia; Somavilla, Raquel; Cano, Daniel; Rodriguez, Carmen; Gonzalez-Pola, Cesar; Viloria, Amaia; Tel, Elena; Ruiz-Villareal, Manuel

    2017-04-01

    Long-Term Time Series Stations have been developed in order to document seasonal to decadal scale variations in key physical and biogeochemical parameters. Long-term time series measurements are crucial for determining the physical and biological mechanisms controlling the system. The Science and Technology Ministers of the G7 in their Tsukuba Communiqué have stated that 'many parts of the ocean interior are not sufficiently observed' and that 'it is crucial to develop far stronger scientific knowledge necessary to assess the ongoing changes in the ocean and their impact on economies.' Time series has been classically obtained by oceanographic ships that regularly cover standard sections and stations. From 1991, shelf and slope waters of the Southern Bay of Biscay are regularly sampled in a monthly hydrographic line north of Santander to a depth of 1000 m in early stages and for the whole water column down to 2580 m in recent times. Nearby, in June 2007, the IEO deployed an oceanic-meteorological buoy (AGL Buoy, 43° 50.67'N; 3° 46.20'W, and 40 km offshore, www.boya-agl.st.ieo.es). The Santander Atlantic Time Series Station is integrated in the Spanish Institute of Oceanography Observing Sistem (IEOOS). The long-term hydrographic monitoring has allowed to define the seasonality of the main oceanographic facts as the upwelling, the Iberian Poleward Current, low salinity incursions, trends and interannual variability at mixing layer, and at the main water masses North Atlantic Central Water and Mediterranean Water. The relation of these changes with the high frequency surface conditions recorded by the Biscay AGL has been examined using also satellite and reanalysis data. During the FIXO3 Project (Fixed-point Open Ocean Observatories), and using this combined sources, some products and quality controled series of high interest and utility for scientific purposes has been developed. Hourly products as Sea Surface Temperature and Salinity anomalies, wave significant

  3. On the Relationship Between Transfer Function-derived Response Times and Hydrograph Analysis Timing Parameters: Are there Similarities?

    Science.gov (United States)

    Bansah, S.; Ali, G.; Haque, M. A.; Tang, V.

    2017-12-01

    The proportion of precipitation that becomes streamflow is a function of internal catchment characteristics - which include geology, landscape characteristics and vegetation - and influence overall storage dynamics. The timing and quantity of water discharged by a catchment are indeed embedded in event hydrographs. Event hydrograph timing parameters, such as the response lag and time of concentration, are important descriptors of how long it takes the catchment to respond to input precipitation and how long it takes the latter to filter through the catchment. However, the extent to which hydrograph timing parameters relate to average response times derived from fitting transfer functions to annual hydrographs is unknown. In this study, we used a gamma transfer function to determine catchment average response times as well as event-specific hydrograph parameters across a network of eight nested watersheds ranging from 0.19 km2 to 74.6 km2 prairie catchments located in south central Manitoba (Canada). Various statistical analyses were then performed to correlate average response times - estimated using the parameters of the fitted gamma transfer function - to event-specific hydrograph parameters. Preliminary results show significant interannual variations in response times and hydrograph timing parameters: the former were in the order of a few hours to days, while the latter ranged from a few days to weeks. Some statistically significant relationships were detected between response times and event-specific hydrograph parameters. Future analyses will involve the comparison of statistical distributions of event-specific hydrograph parameters with that of runoff response times and baseflow transit times in order to quantity catchment storage dynamics across a range of temporal scales.

  4. 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.

  5. Approaches in highly parameterized inversion: TSPROC, a general time-series processor to assist in model calibration and result summarization

    Science.gov (United States)

    Westenbroek, Stephen M.; Doherty, John; Walker, John F.; Kelson, Victor A.; Hunt, Randall J.; Cera, Timothy B.

    2012-01-01

    The TSPROC (Time Series PROCessor) computer software uses a simple scripting language to process and analyze time series. It was developed primarily to assist in the calibration of environmental models. The software is designed to perform calculations on time-series data commonly associated with surface-water models, including calculation of flow volumes, transformation by means of basic arithmetic operations, and generation of seasonal and annual statistics and hydrologic indices. TSPROC can also be used to generate some of the key input files required to perform parameter optimization by means of the PEST (Parameter ESTimation) computer software. Through the use of TSPROC, the objective function for use in the model-calibration process can be focused on specific components of a hydrograph.

  6. Integrated response and transit time distributions of watersheds by combining hydrograph separation and long-term transit time modeling

    Directory of Open Access Journals (Sweden)

    M. C. Roa-García

    2010-08-01

    Full Text Available We present a new modeling approach analyzing and predicting the Transit Time Distribution (TTD and the Response Time Distribution (RTD from hourly to annual time scales as two distinct hydrological processes. The model integrates Isotope Hydrograph Separation (IHS and the Instantaneous Unit Hydrograph (IUH approach as a tool to provide a more realistic description of transit and response time of water in catchments. Individual event simulations and parameterizations were combined with long-term baseflow simulation and parameterizations; this provides a comprehensive picture of the catchment response for a long time span for the hydraulic and isotopic processes. The proposed method was tested in three Andean headwater catchments to compare the effects of land use on hydrological response and solute transport. Results show that the characteristics of events and antecedent conditions have a significant influence on TTD and RTD, but in general the RTD of the grassland dominated catchment is concentrated in the shorter time spans and has a higher cumulative TTD, while the forest dominated catchment has a relatively higher response distribution and lower cumulative TTD. The catchment where wetlands concentrate shows a flashier response, but wetlands also appear to prolong transit time.

  7. A new time-space accounting scheme to predict stream water residence time and hydrograph source components at the watershed scale

    Science.gov (United States)

    Takahiro Sayama; Jeffrey J. McDonnell

    2009-01-01

    Hydrograph source components and stream water residence time are fundamental behavioral descriptors of watersheds but, as yet, are poorly represented in most rainfall-runoff models. We present a new time-space accounting scheme (T-SAS) to simulate the pre-event and event water fractions, mean residence time, and spatial source of streamflow at the watershed scale. We...

  8. Hydrograph variances over different timescales in hydropower production networks

    Science.gov (United States)

    Zmijewski, Nicholas; Wörman, Anders

    2016-08-01

    The operation of water reservoirs involves a spectrum of timescales based on the distribution of stream flow travel times between reservoirs, as well as the technical, environmental, and social constraints imposed on the operation. In this research, a hydrodynamically based description of the flow between hydropower stations was implemented to study the relative importance of wave diffusion on the spectrum of hydrograph variance in a regulated watershed. Using spectral decomposition of the effluence hydrograph of a watershed, an exact expression of the variance in the outflow response was derived, as a function of the trends of hydraulic and geomorphologic dispersion and management of production and reservoirs. We show that the power spectra of involved time-series follow nearly fractal patterns, which facilitates examination of the relative importance of wave diffusion and possible changes in production demand on the outflow spectrum. The exact spectral solution can also identify statistical bounds of future demand patterns due to limitations in storage capacity. The impact of the hydraulic description of the stream flow on the reservoir discharge was examined for a given power demand in River Dalälven, Sweden, as function of a stream flow Peclet number. The regulation of hydropower production on the River Dalälven generally increased the short-term variance in the effluence hydrograph, whereas wave diffusion decreased the short-term variance over periods of white noise) as a result of current production objectives.

  9. Design flood hydrographs from the relationship between flood peak and volume

    Directory of Open Access Journals (Sweden)

    L. Mediero

    2010-12-01

    Full Text Available Hydrological frequency analyses are usually focused on flood peaks. Flood volumes and durations have not been studied as extensively, although there are many practical situations, such as when designing a dam, in which the full hydrograph is of interest. A flood hydrograph may be described by a multivariate function of the peak, volume and duration. Most standard bivariate and trivariate functions do not produce univariate three-parameter functions as marginal distributions, however, three-parameter functions are required to fit highly skewed data, such as flood peak and flood volume series. In this paper, the relationship between flood peak and hydrograph volume is analysed to overcome this problem. A Monte Carlo experiment was conducted to generate an ensemble of hydrographs that maintain the statistical properties of marginal distributions of the peaks, volumes and durations. This ensemble can be applied to determine the Design Flood Hydrograph (DFH for a reservoir, which is not a unique hydrograph, but rather a curve in the peak-volume space. All hydrographs on that curve have the same return period, which can be understood as the inverse of the probability to exceed a certain water level in the reservoir in any given year. The procedure can also be applied to design the length of the spillway crest in terms of the risk of exceeding a given water level in the reservoir.

  10. Mathematical modeling of synthetic unit hydrograph case study: Citarum watershed

    Science.gov (United States)

    Islahuddin, Muhammad; Sukrainingtyas, Adiska L. A.; Kusuma, M. Syahril B.; Soewono, Edy

    2015-09-01

    Deriving unit hydrograph is very important in analyzing watershed's hydrologic response of a rainfall event. In most cases, hourly measures of stream flow data needed in deriving unit hydrograph are not always available. Hence, one needs to develop methods for deriving unit hydrograph for ungagged watershed. Methods that have evolved are based on theoretical or empirical formulas relating hydrograph peak discharge and timing to watershed characteristics. These are usually referred to Synthetic Unit Hydrograph. In this paper, a gamma probability density function and its variant are used as mathematical approximations of a unit hydrograph for Citarum Watershed. The model is adjusted with real field condition by translation and scaling. Optimal parameters are determined by using Particle Swarm Optimization method with weighted objective function. With these models, a synthetic unit hydrograph can be developed and hydrologic parameters can be well predicted.

  11. Estimating basin lagtime and hydrograph-timing indexes used to characterize stormflows for runoff-quality analysis

    Science.gov (United States)

    Granato, Gregory E.

    2012-01-01

    A nationwide study to better define triangular-hydrograph statistics for use with runoff-quality and flood-flow studies was done by the U.S. Geological Survey (USGS) in cooperation with the Federal Highway Administration. Although the triangular hydrograph is a simple linear approximation, the cumulative distribution of stormflow with a triangular hydrograph is a curvilinear S-curve that closely approximates the cumulative distribution of stormflows from measured data. The temporal distribution of flow within a runoff event can be estimated using the basin lagtime, (which is the time from the centroid of rainfall excess to the centroid of the corresponding runoff hydrograph) and the hydrograph recession ratio (which is the ratio of the duration of the falling limb to the rising limb of the hydrograph). This report documents results of the study, methods used to estimate the variables, and electronic files that facilitate calculation of variables. Ten viable multiple-linear regression equations were developed to estimate basin lagtimes from readily determined drainage basin properties using data published in 37 stormflow studies. Regression equations using the basin lag factor (BLF, which is a variable calculated as the main-channel length, in miles, divided by the square root of the main-channel slope in feet per mile) and two variables describing development in the drainage basin were selected as the best candidates, because each equation explains about 70 percent of the variability in the data. The variables describing development are the USGS basin development factor (BDF, which is a function of the amount of channel modifications, storm sewers, and curb-and-gutter streets in a basin) and the total impervious area variable (IMPERV) in the basin. Two datasets were used to develop regression equations. The primary dataset included data from 493 sites that have values for the BLF, BDF, and IMPERV variables. This dataset was used to develop the best-fit regression

  12. 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

  13. 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...

  14. 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.

  15. 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

  16. Performance Analysis of Low-Cost Single-Frequency GPS Receivers in Hydrographic Surveying

    Science.gov (United States)

    Elsobeiey, M.

    2017-10-01

    The International Hydrographic Organization (IHO) has issued standards that provide the minimum requirements for different types of hydrographic surveys execution to collect data to be used to compile navigational charts. Such standards are usually updated from time to time to reflect new survey techniques and practices and must be achieved to assure both surface navigation safety and marine environment protection. Hydrographic surveys can be classified to four orders namely, special order, order 1a, order 1b, and order 2. The order of hydrographic surveys to use should be determined in accordance with the importance to the safety of navigation in the surveyed area. Typically, geodetic-grade dual-frequency GPS receivers are utilized for position determination during data collection in hydrographic surveys. However, with the evolution of high-sensitivity low-cost single-frequency receivers, it is very important to evaluate the performance of such receivers. This paper investigates the performance of low-cost single-frequency GPS receivers in hydrographic surveying applications. The main objective is to examine whether low-cost single-frequency receivers fulfil the IHO standards for hydrographic surveys. It is shown that the low-cost single-frequency receivers meet the IHO horizontal accuracy for all hydrographic surveys orders at any depth. However, the single-frequency receivers meet only order 2 requirements for vertical accuracy at depth more than or equal 100 m.

  17. Simulation of Runoff Hydrograph on Soil Surfaces with Different Microtopography Using a Travel Time Method at the Plot Scale.

    Science.gov (United States)

    Zhao, Longshan; Wu, Faqi

    2015-01-01

    In this study, a simple travel time-based runoff model was proposed to simulate a runoff hydrograph on soil surfaces with different microtopographies. Three main parameters, i.e., rainfall intensity (I), mean flow velocity (vm) and ponding time of depression (tp), were inputted into this model. The soil surface was divided into numerous grid cells, and the flow length of each grid cell (li) was then calculated from a digital elevation model (DEM). The flow velocity in each grid cell (vi) was derived from the upstream flow accumulation area using vm. The total flow travel time through each grid cell to the surface outlet was the sum of the sum of flow travel times along the flow path (i.e., the sum of li/vi) and tp. The runoff rate at the slope outlet for each respective travel time was estimated by finding the sum of the rain rate from all contributing cells for all time intervals. The results show positive agreement between the measured and predicted runoff hydrographs.

  18. Simulation of Runoff Hydrograph on Soil Surfaces with Different Microtopography Using a Travel Time Method at the Plot Scale

    Science.gov (United States)

    Zhao, Longshan; Wu, Faqi

    2015-01-01

    In this study, a simple travel time-based runoff model was proposed to simulate a runoff hydrograph on soil surfaces with different microtopographies. Three main parameters, i.e., rainfall intensity (I), mean flow velocity (v m) and ponding time of depression (t p), were inputted into this model. The soil surface was divided into numerous grid cells, and the flow length of each grid cell (l i) was then calculated from a digital elevation model (DEM). The flow velocity in each grid cell (v i) was derived from the upstream flow accumulation area using v m. The total flow travel time through each grid cell to the surface outlet was the sum of the sum of flow travel times along the flow path (i.e., the sum of l i/v i) and t p. The runoff rate at the slope outlet for each respective travel time was estimated by finding the sum of the rain rate from all contributing cells for all time intervals. The results show positive agreement between the measured and predicted runoff hydrographs. PMID:26103635

  19. 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...

  20. 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.

  1. 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

  2. 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...

  3. 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

  4. 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 ...

  5. 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 ...

  6. 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…

  7. 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...

  8. Synthetic socioeconomic based domestic wastewater hydrographs for small arid communities

    KAUST Repository

    Elnakar, H.

    2012-06-04

    A model was developed to predict synthetic socioeconomic based domestic wastewater hydrographs for the small arid communities. The model predicts the flow hydrograph for random weekdays and weekends based on the specific socioeconomic characteristics of the community. The main socioeconomic characteristics are the composition of the community, the different user behaviours in using water appliances, and the unit discharges of such appliances. Use patterns of water appliances are assumed to vary for the various members of the community and the type of day. Each community is composed of several social categories such as the employee, working woman, stay home woman, stay home child, students etc. The use patterns account for the stochastic nature of use in terms of number of uses, duration of the use and times of use in the day. Randomly generated hydrographs are generated for weekdays and weekends along with synthetic hydrographs of non-exceedance. The model was verified for a small residential compound in Sharm El Shiekh - Egypt using 11 days of flow measurements performed in summer. The synthetic hydrographs based on assumed water use patterns of the various members of the community compared reasonably with the measured hydrographs. Synthetic hydrographs can be derived for a community under consideration to reflect its socioeconomic conditions and thus can be used to generate probability based peaking factors to be used in the design of sewerage systems pumping facilities, and treatment plants. © 201 WIT Press.

  9. Synthetic socioeconomic based domestic wastewater hydrographs for small arid communities

    KAUST Repository

    Elnakar, H.; Imam, E.; Nassar, K.

    2012-01-01

    A model was developed to predict synthetic socioeconomic based domestic wastewater hydrographs for the small arid communities. The model predicts the flow hydrograph for random weekdays and weekends based on the specific socioeconomic characteristics of the community. The main socioeconomic characteristics are the composition of the community, the different user behaviours in using water appliances, and the unit discharges of such appliances. Use patterns of water appliances are assumed to vary for the various members of the community and the type of day. Each community is composed of several social categories such as the employee, working woman, stay home woman, stay home child, students etc. The use patterns account for the stochastic nature of use in terms of number of uses, duration of the use and times of use in the day. Randomly generated hydrographs are generated for weekdays and weekends along with synthetic hydrographs of non-exceedance. The model was verified for a small residential compound in Sharm El Shiekh - Egypt using 11 days of flow measurements performed in summer. The synthetic hydrographs based on assumed water use patterns of the various members of the community compared reasonably with the measured hydrographs. Synthetic hydrographs can be derived for a community under consideration to reflect its socioeconomic conditions and thus can be used to generate probability based peaking factors to be used in the design of sewerage systems pumping facilities, and treatment plants. © 201 WIT Press.

  10. 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.

  11. 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 ...

  12. 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.

  13. 75 FR 20809 - Hydrographic Services Review Panel

    Science.gov (United States)

    2010-04-21

    ... hydrographic data and hydrographic services, marine transportation, port administration, vessel pilotage....'' NOAA encourages individuals with expertise in navigation data, products and services; coastal.... NOS collects and compiles hydrographic, tidal and current, geodetic, and a variety of other data in...

  14. 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.

  15. 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.

  16. 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.

  17. Simulating double-peak hydrographs from single storms over mixed-use watersheds

    Science.gov (United States)

    Yang Yang; Theodore A. Endreny; David J. Nowak

    2015-01-01

    Two-peak hydrographs after a single rain event are observed in watersheds and storms with distinct volumes contributing as fast and slow runoff. The authors developed a hydrograph model able to quantify these separate runoff volumes to help in estimation of runoff processes and residence times used by watershed managers. The model uses parallel application of two...

  18. 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.

  19. 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

  20. The Hydrograph Analyst, an Arcview GIS Extension That Integrates Point, Spatial, and Temporal Data Provides A Graphical User Interface for Hydrograph Analysis

    International Nuclear Information System (INIS)

    Jones, M.L.; O'Brien, G.M.; Jones, M.L.

    2000-01-01

    The Hydrograph Analyst (HA) is an ArcView GIS 3.2 extension developed by the authors to analyze hydrographs from a network of ground-water wells and springs in a regional ground-water flow model. ArcView GIS integrates geographic, hydrologic, and descriptive information and provides the base functionality needed for hydrograph analysis. The HA extends ArcView's base functionality by automating data integration procedures and by adding capabilities to visualize and analyze hydrologic data. Data integration procedures were automated by adding functionality to the View document's Document Graphical User Interface (DocGUI). A menu allows the user to query a relational database and select sites which are displayed as a point theme in a View document. An ''Identify One to Many'' tool is provided within the View DocGUI to retrieve all hydrologic information for a selected site and display it in a simple and concise tabular format. For example, the display could contain various records from many tables storing data for one site. Another HA menu allows the user to generate a hydrograph for sites selected from the point theme. Hydrographs generated by the HA are added as hydrograph documents and accessed by the user with the Hydrograph DocGUI, which contains tools and buttons for hydrograph analysis. The Hydrograph DocGUI has a ''Select By Polygon'' tool used for isolating particular points on the hydrograph inside a user-drawn polygon or the user could isolate the same points by constructing a logical expression with the ArcView GIS ''Query Builder'' dialog that is also accessible in the Hydrograph DocGUI. Other buttons can be selected to alter the query applied to the active hydrograph. The selected points on the active hydrograph can be attributed (or flagged) individually or as a group using the ''Flag'' tool found on the Hydrograph DocGUI. The ''Flag'' tool activates a dialog box that prompts the user to select an attribute and ''methods'' or ''conditions'' that qualify

  1. THE ANTHROPIC LAKES FROM THE HYDROGRAPHIC BASIN OF UPPER IALOMIŢA (ROMANIA

    Directory of Open Access Journals (Sweden)

    Ovidiu MURARESCU

    2008-06-01

    Full Text Available The hydrographic basin of upper Ialomiţa has become an area of interest for hydrotechnicians beginning with the period between the two World Wars, due to its hydroenergetic potential estimated at around 1 500-2 000 KWh. In this sense, in time, a series of hydrotechnical arrangements have been made, behind which important water resources have gathered. In this geographical area, there are nine accumulation lakes (without taking into account the years when they were achieved, both on the main river and on its tributaries. They serve different purposes: to regulate the regime of the liquid flow in order to attenuate the high floods, water resource for downstream consumers, electric energy production etc.

  2. 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.

  3. 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

  4. Developing Design Storm Hydrographs for Small Tropical ...

    African Journals Online (AJOL)

    Hydrographs are vital tools in the design and construction of water-control structures in urban and rural systems. The purpose of this study was to explore the development of design storm hydrographs for the small tropical catchment with limited data. In this study, Clark's Unit Hydrograph method was used to develop ...

  5. 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...

  6. Long-term retrospective analysis of mackerel spawning in the North Sea: a new time series and modeling approach to CPR data.

    Science.gov (United States)

    Jansen, Teunis; Kristensen, Kasper; Payne, Mark; Edwards, Martin; Schrum, Corinna; Pitois, Sophie

    2012-01-01

    We present a unique view of mackerel (Scomber scombrus) in the North Sea based on a new time series of larvae caught by the Continuous Plankton Recorder (CPR) survey from 1948-2005, covering the period both before and after the collapse of the North Sea stock. Hydrographic backtrack modelling suggested that the effect of advection is very limited between spawning and larvae capture in the CPR survey. Using a statistical technique not previously applied to CPR data, we then generated a larval index that accounts for both catchability as well as spatial and temporal autocorrelation. The resulting time series documents the significant decrease of spawning from before 1970 to recent depleted levels. Spatial distributions of the larvae, and thus the spawning area, showed a shift from early to recent decades, suggesting that the central North Sea is no longer as important as the areas further west and south. These results provide a consistent and unique perspective on the dynamics of mackerel in this region and can potentially resolve many of the unresolved questions about this stock.

  7. Long-term retrospective analysis of mackerel spawning in the North Sea: a new time series and modeling approach to CPR data.

    Directory of Open Access Journals (Sweden)

    Teunis Jansen

    Full Text Available We present a unique view of mackerel (Scomber scombrus in the North Sea based on a new time series of larvae caught by the Continuous Plankton Recorder (CPR survey from 1948-2005, covering the period both before and after the collapse of the North Sea stock. Hydrographic backtrack modelling suggested that the effect of advection is very limited between spawning and larvae capture in the CPR survey. Using a statistical technique not previously applied to CPR data, we then generated a larval index that accounts for both catchability as well as spatial and temporal autocorrelation. The resulting time series documents the significant decrease of spawning from before 1970 to recent depleted levels. Spatial distributions of the larvae, and thus the spawning area, showed a shift from early to recent decades, suggesting that the central North Sea is no longer as important as the areas further west and south. These results provide a consistent and unique perspective on the dynamics of mackerel in this region and can potentially resolve many of the unresolved questions about this stock.

  8. 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.

  9. 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.

  10. 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

  11. 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...

  12. 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.

  13. Equations for estimating synthetic unit-hydrograph parameter values for small watersheds in Lake County, Illinois

    Science.gov (United States)

    Melching, C.S.; Marquardt, J.S.

    1997-01-01

    Design hydrographs computed from design storms, simple models of abstractions (interception, depression storage, and infiltration), and synthetic unit hydrographs provide vital information for stormwater, flood-plain, and water-resources management throughout the United States. Rainfall and runoff data for small watersheds in Lake County collected between 1990 and 1995 were studied to develop equations for estimation of synthetic unit-hydrograph parameters on the basis of watershed and storm characteristics. The synthetic unit-hydrograph parameters of interest were the time of concentration (TC) and watershed-storage coefficient (R) for the Clark unit-hydrograph method, the unit-graph lag (UL) for the Soil Conservation Service (now known as the Natural Resources Conservation Service) dimensionless unit hydrograph, and the hydrograph-time lag (TL) for the linear-reservoir method for unit-hydrograph estimation. Data from 66 storms with effective-precipitation depths greater than 0.4 inches on 9 small watersheds (areas between 0.06 and 37 square miles (mi2)) were utilized to develop the estimation equations, and data from 11 storms on 8 of these watersheds were utilized to verify (test) the estimation equations. The synthetic unit-hydrograph parameters were determined by calibration using the U.S. Army Corps of Engineers Flood Hydrograph Package HEC-1 (TC, R, and UL) or by manual analysis of the rainfall and run-off data (TL). The relation between synthetic unit-hydrograph parameters, and watershed and storm characteristics was determined by multiple linear regression of the logarithms of the parameters and characteristics. Separate sets of equations were developed with watershed area and main channel length as the starting parameters. Percentage of impervious cover, main channel slope, and depth of effective precipitation also were identified as important characteristics for estimation of synthetic unit-hydrograph parameters. The estimation equations utilizing area

  14. 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

  15. 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.

  16. 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.

  17. DNA-based molecular fingerprinting of eukaryotic protists and cyanobacteria contributing to sinking particle flux at the Bermuda Atlantic time-series study

    Science.gov (United States)

    Amacher, Jessica; Neuer, Susanne; Lomas, Michael

    2013-09-01

    We used denaturing gradient gel electrophoresis (DGGE) to examine the protist and cyanobacterial communities in the euphotic zone (0-120 m) and in corresponding 150 m particle interceptor traps at the Bermuda Atlantic Time-series Study (BATS) in a two-year monthly time-series from May 2008 to April 2010. Dinoflagellates were the most commonly detected taxa in both water column and trap samples throughout the time series. Diatom sequences were found only eight times in the water column, and only four times in trap material. Small-sized eukaryotic taxa, including the prasinophyte genera Ostreococcus, Micromonas, and Bathycoccus, were present in trap samples, as were the cyanobacteria Prochlorococcus and Synechococcus. Synechococcus was usually overrepresented in trap material, whereas Prochlorococcus was underrepresented compared to the water column. Both seasonal and temporal variability affected patterns of ribosomal DNA found in sediment traps. The two years of this study were quite different hydrographically, with higher storm activity and the passing of a cyclonic eddy causing unusually deep mixing in winter 2010. This was reflected in the DGGE fingerprints of the water column, which showed greater phylotype richness of eukaryotes and a lesser richness of cyanobacteria in winter of 2010 compared with the winter of 2009. Increases in eukaryotic richness could be traced to increased diversity of prasinophytes and prymnesiophytes. The decrease in cyanobacterial richness was in turn reflected in the trap composition, but the increase in eukaryotes was not, indicating a disproportionate contribution of certain taxa to sinking particle flux.

  18. A methodology for investigation of the seasonal evolution in proglacial hydrograph form

    Science.gov (United States)

    Hannah, David M.; Gurnell, Angela M.; McGregor, Glenn R.

    1999-11-01

    This paper advances an objective method of diurnal hydrograph classification as an aid to exploring changes in the hydrological functioning of glacierized catchments over the ablation season. The temporal sequencing of different hydrograph classes allows identification of seasonal evolution in hydrograph form and also assists delimitation of hydrologically-meaningful time periods of similar diurnal discharge response. The effectiveness of this approach is illustrated by applying it to two contrasting summer discharge records for a small cirque basin. By comparing the results with patterns of surface energy receipt and glacier ablation, the seasonally transient relative influences of: (i) surface meltwater production and (ii) meltwater routing and storage conditions within the intervening glacier drainage system in determining runoff are elucidated. The method successfully characterizes distinct seasonal-scale changes in the diurnal outflow hydrograph during the ablation-dominated 1995 melt season but is also able to reveal underlying trends and short-term fluctuations in the precipitation-dominated, poorly ablation-regulated 1996 melt season. The limitations and benefits of this hydrograph classification technique are evaluated.

  19. Characterisation of dispersion mechanisms in an urban catchment using a deterministic spatially distributed direct hydrograph travel time model

    Science.gov (United States)

    Rossel, F.; Gironas, J. A.

    2012-12-01

    The link between stream network structure and hydrologic response for natural basins has been extensively studied. It is well known that stream network organization and flow dynamics in the reaches combine to shape the hydrologic response of natural basins. Geomorphologic dispersion and hydrodynamic dispersion along with hillslope processes control to a large extent the overall variance of the hydrograph, particularly under the assumption of constant celerity throughout the basin. In addition, a third mechanism referred as to kinematic dispersion becomes relevant when considering spatial variations of celerity. On contrary, the link between the drainage network structure and overall urban terrain, and the hydrologic response in urban catchments has been much less studied. In particular, the characterization of the different dispersion mechanisms within urban areas remains to be better understood. In such areas artificial elements are expected to contribute to the total dispersion due to the variety of geometries and the spatial distribution of imperviousness. This work quantifies the different dispersion mechanisms in an urban catchment, focusing on their relevance and the spatial scales involved. For this purpose we use the Urban Morpho-climatic Instantaneous Unit Hydrograph model, a deterministic spatially distributed direct hydrograph travel time model, which computes travel times in hillslope, pipe, street and channel cells using formulations derived from kinematic wave theory. The model was applied to the Aubeniere catchment, located in Nantes, France. Unlike stochastic models, this deterministic model allows the quantification of dispersion mechanism at the local scale (i.e. the grid-cell). We found that kinematic dispersion is more relevant for small storm events, whereas geomorphologic dispersion becomes more significant for larger storms, as the mean celerity within the catchment increases. In addition, the total dispersion relates to the drainage area in

  20. 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.

  1. 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

  2. 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.

  3. 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.

  4. 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)""…

  5. 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.

  6. 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.

  7. 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....

  8. Hydrograph separation techniques in snowmelt-dominated watersheds

    Science.gov (United States)

    Miller, S.; Miller, S. N.

    2017-12-01

    This study integrates hydrological, geochemical, and isotopic data for a better understanding of different streamflow generation pathways and residence times in a snowmelt-dominated region. A nested watershed design with ten stream gauging sites recording sub-hourly stream stage has been deployed in a snowmelt-dominated region in southeastern Wyoming, heavily impacted by the recent bark beetle epidemic. LiDAR-derived digital elevation models help elucidate effects from topography and watershed metrics. At each stream gauging site, sub-hourly stream water conductivity and temperature data are also recorded. Hydrograph separation is a useful technique for determining different sources of runoff and how volumes from each source vary over time. Following previous methods, diurnal cycles from sub-hourly recorded streamflow and specific conductance data are analyzed and used to separate hydrographs into overland flow and baseflow components, respectively. A final component, vadose-zone flow, is assumed to be the remaining water from the total hydrograph. With access to snowmelt and precipitation data from nearby instruments, runoff coefficients are calculated for the different mechanisms, providing information on watershed response. Catchments are compared to understand how different watershed characteristics translate snowmelt or precipitation events into runoff. Portable autosamplers were deployed at two of the gauging sites for high-frequency analysis of stream water isotopic composition during peak flow to compare methods of hydrograph separation. Sampling rates of one or two hours can detect the diurnal streamflow cycle common during peak snowmelt. Prior research suggests the bark beetle epidemic has had little effect on annual streamflow patterns; however, several results show an earlier shift in the day of year in which peak annual streamflow is observed. The diurnal cycle is likely to comprise a larger percentage of daily streamflow during snowmelt in post

  9. RUNOFF HYDROGRAPHS USING SNYDER AND SCS SYNTHETIC UNIT HYDROGRAPH METHODS: A CASE STUDY OF SELECTED RIVERS IN SOUTH WEST NIGERIA

    Directory of Open Access Journals (Sweden)

    Wahab Adebayo Salami

    2017-01-01

    Full Text Available This paper presents the development of runoff hydrographs for selected rivers in Ogun-Osun river catchment, south west, Nigeria using Snyder and Soil Conservation Service (SCS methods of synthetic unit hydrograph to determine the ordinates. The Soil Conservation Service (SCS curve Number method was used to estimate the excess rainfall from storm of different return periods. The peak runoff hydrographs were determined by convoluting the unit hydrographs ordinates with the excess rainfall and the value of peak flows obtained by both Snyder and SCS methods observed to vary from one river watershed to the other. The peak runoff hydrograph flows obtained based on the unit hydrograph ordinate determined with Snyder method for 20-yr, 50-yr, 100-yr, 200-yr and 500-yr, return period varied from 112.63m3/s and 13364.30m3/s, while those based on the SCS method varied from 304.43m3/s and 6466.84m3/s for the eight watersheds. However, the percentage difference shows that for values of peak flows obtained with Snyder and SCS methods varies from 13.14% to 63.30%. However, SCS method is recommended to estimate the ordinate required for the development of peak runoff hydrograph in the river watersheds because it utilized additional morphometric parameters such as watershed slope and the curve number (CN which is a function of the properties of the soil and vegetation cover of the watershed.

  10. 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.

  11. 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.

  12. 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.

  13. 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…

  14. 15 CFR 996.20 - Submission of a hydrographic product for certification.

    Science.gov (United States)

    2010-01-01

    ... QUALITY ASSURANCE AND CERTIFICATION REQUIREMENTS FOR NOAA HYDROGRAPHIC PRODUCTS AND SERVICES QUALITY ASSURANCE AND CERTIFICATION REQUIREMENTS FOR NOAA HYDROGRAPHIC PRODUCTS AND SERVICES Certification of a Hydrographic Product and Decertification. § 996.20 Submission of a hydrographic product for certification. (a...

  15. 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...

  16. 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...

  17. 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

  18. 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

  19. 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...

  20. Time series for water levels in virtual gauge stations in the Amazon basin using satellite radar altimetry

    Directory of Open Access Journals (Sweden)

    Juan Gabriel León Hernández

    2009-01-01

    Full Text Available Using satellite altimeter radar technology for monitoring changes in water levels at continental scale is a relatively recent ad- vance. Several studies have demonstrated the interest being shown in applying this technology to monitoring the hydrographic patterns of large-scale basins worldwide. The current study presents the inference of time series representing changes in water le- vel for bodies of water by defining virtual gauge stations deduced for two very different rivers in terms of their biophysical and to- pographic characteristics; the two rivers were the Rio Negro in the Brazilian Amazon Basin and the Caqueta River on the Colombian side. The differences between the two rivers revealed the limits of satellite radar altimeter when applied to continental waters (±20cm and ±40 cm precision for Río Negro and Río Caquetá, respectively. However, applying this technology seems very promising, since new missions have been scheduled to be put into orbit by the end of 2008.

  1. 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

  2. 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...

  3. 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.

  4. Development of storm hydrographs for three rivers within drainage ...

    African Journals Online (AJOL)

    The design storm hydrographs corresponding to.the excess rainfall values were determined based on the unit hydrograph ordinates established through convolution. The design storm hydrograph obtain~d for Moro River catchment based on 5-yr, 20~yr~ 50-yr, 100-yr and 200-yr return period ranged between 245.29m3/s ...

  5. 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...

  6. Identification of Flood Reactivity Regions via the Functional Clustering of Hydrographs

    Science.gov (United States)

    Brunner, Manuela I.; Viviroli, Daniel; Furrer, Reinhard; Seibert, Jan; Favre, Anne-Catherine

    2018-03-01

    Flood hydrograph shapes contain valuable information on the flood-generation mechanisms of a catchment. To make good use of this information, we express flood hydrograph shapes as continuous functions using a functional data approach. We propose a clustering approach based on functional data for flood hydrograph shapes to identify a set of representative hydrograph shapes on a catchment scale and use these catchment-specific sets of representative hydrographs to establish regions of catchments with similar flood reactivity on a regional scale. We applied this approach to flood samples of 163 medium-size Swiss catchments. The results indicate that three representative hydrograph shapes sufficiently describe the hydrograph shape variability within a catchment and therefore can be used as a proxy for the flood behavior of a catchment. These catchment-specific sets of three hydrographs were used to group the catchments into three reactivity regions of similar flood behavior. These regions were not only characterized by similar hydrograph shapes and reactivity but also by event magnitudes and triggering event conditions. We envision these regions to be useful in regionalization studies, regional flood frequency analyses, and to allow for the construction of synthetic design hydrographs in ungauged catchments. The clustering approach based on functional data which establish these regions is very flexible and has the potential to be extended to other geographical regions or toward the use in climate impact studies.

  7. 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).

  8. 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...

  9. 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.

  10. 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.

  11. 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

  12. 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...

  13. 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...

  14. 76 FR 32957 - Hydrographic Services Review Panel

    Science.gov (United States)

    2011-06-07

    .... SUMMARY: This notice responds to the Hydrographic Service Improvements Act Amendments of 2002, Public Law...), to solicit nominations for membership on the Hydrographic Services Review Panel (HSRP). The HSRP, a...; fisheries management; coastal and marine spatial planning; geodesy; water levels; and other science-related...

  15. 77 FR 76001 - Hydrographic Services Review Panel

    Science.gov (United States)

    2012-12-26

    ... disciplines and fields relating to hydrographic data and hydrographic services, marine transportation, port... represented on the Panel and encourages individuals with expertise in navigation data, products and services... variety of other data in order to fulfill this responsibility. The HSRP provides advice on current and...

  16. 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 ...

  17. 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...

  18. 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.

  19. 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...

  20. 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.

  1. 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. .

  2. 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...

  3. 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...

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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...

  9. 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.

  10. Providing Longitudinal Connection In Case Of Cross Sluicing On Water Bodies In Banat Hydrographic Area

    Directory of Open Access Journals (Sweden)

    Hoancă Diana

    2014-10-01

    Full Text Available On Banat Hydrographic Area level, there are a series of works which put hydrological pressures on bodies of water: accumulations, damming, water diversions, regulations, shore protection, etc. These works were created in order to ensure water demand, defend against floods, regulate discharges, and combat humidity excess. Speaking justly, they have an important socioeconomic role. Among the negative effects of longitudinal connection interruption of water bodies we can mention, the risk of not achieving the positive ecological potential of water bodies in accordance with the Water Framework Directive, the reduction of the aquatic biodiversity, the reduction or even extinction of certain aquatic species and the alteration of the flow process. Because the negative effects of the hydromorphological alterations, especially those due to the interruption of the longitudinal connection, have a significant impact on the aquatic biodiversity. At Banat Hydrographic Area level, a series of measures, have been identified for the rehabilitation of the affected water courses: the removal of the hydrotechnical constructions from the water body if they have lost their functional features, building of passages for the migration of the ichthyofauna, reconnecting of the affluents and the disconnected arms as well as other measures intended to bring things back to their natural state. The implementation of these measures is made according to the importance and the extent of their positive impact as opposed to the negative effect that might occur as a consequence of their application. Analyzing the measures aforementioned and taking into consideration the characteristics of the hydromorphological pressures on water bodies in Banat Hydrographic Area, a number of measures regarding control are supplied in this paper.

  11. 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.

  12. 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.

  13. 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

  14. 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.

  15. 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

  16. 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

  17. A measure of watershed nonlinearity: interpreting a variable instantaneous unit hydrograph model on two vastly different sized watersheds

    Directory of Open Access Journals (Sweden)

    J. Y. Ding

    2011-01-01

    Full Text Available The linear unit hydrograph used in hydrologic design analysis and flood forecasting is known as the transfer function and the kernel function in time series analysis and systems theory, respectively. This paper reviews the use of an input-dependent or variable kernel in a linear convolution integral as a quasi-nonlinear approach to unify nonlinear overland flow, channel routing and catchment runoff processes. The conceptual model of a variable instantaneous unit hydrograph (IUH is characterized by a nonlinear storage-discharge relation, q = cNsN, where the storage exponent N is an index or degree of watershed nonlinearity, and the scale parameter c is a discharge coefficient. When the causative rainfall excess intensity of a unit hydrograph is known, parameters N and c can be determined directly from its shape factor, which is the product of the unit peak ordinate and the time to peak, an application of the statistical method of moments in its simplest form. The 2-parameter variable IUH model is calibrated by the shape factor method and verified by convolution integral using both the direct and inverse Bakhmeteff varied-flow functions on two watersheds of vastly different sizes, each having a family of four or five unit hydrographs as reported by the well-known Minshall (1960 paper and the seldom-quoted Childs (1958 one, both located in the US. For an 11-hectare catchment near Edwardsville in southern Illinois, calibration for four moderate storms shows an average N value of 1.79, which is 7% higher than the theoretical value of 1.67 by Manning friction law, while the heaviest storm, which is three to six times larger than the next two events in terms of the peak discharge and runoff volume, follows the Chezy law of 1.5. At the other end of scale, for the Naugatuck River at Thomaston in Connecticut having a drainage area of 186.2 km2, the average calibrated

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. 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...

  7. 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...

  8. 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.

  9. NOS Hydrographic Surveys Collection

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  10. 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.

  11. 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...

  12. 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.

  13. 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.

  14. 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...

  15. 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...

  16. 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...

  17. 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.

  18. Integrated Hydrographical Basin Management. Study Case - Crasna River Basin

    Science.gov (United States)

    Visescu, Mircea; Beilicci, Erika; Beilicci, Robert

    2017-10-01

    Hydrographical basins are important from hydrological, economic and ecological points of view. They receive and channel the runoff from rainfall and snowmelt which, when adequate managed, can provide fresh water necessary for water supply, irrigation, food industry, animal husbandry, hydrotechnical arrangements and recreation. Hydrographical basin planning and management follows the efficient use of available water resources in order to satisfy environmental, economic and social necessities and constraints. This can be facilitated by a decision support system that links hydrological, meteorological, engineering, water quality, agriculture, environmental, and other information in an integrated framework. In the last few decades different modelling tools for resolving problems regarding water quantity and quality were developed, respectively water resources management. Watershed models have been developed to the understanding of water cycle and pollution dynamics, and used to evaluate the impacts of hydrotechnical arrangements and land use management options on water quantity, quality, mitigation measures and possible global changes. Models have been used for planning monitoring network and to develop plans for intervention in case of hydrological disasters: floods, flash floods, drought and pollution. MIKE HYDRO Basin is a multi-purpose, map-centric decision support tool for integrated hydrographical basin analysis, planning and management. MIKE HYDRO Basin is designed for analyzing water sharing issues at international, national and local hydrographical basin level. MIKE HYDRO Basin uses a simplified mathematical representation of the hydrographical basin including the configuration of river and reservoir systems, catchment hydrology and existing and potential water user schemes with their various demands including a rigorous irrigation scheme module. This paper analyzes the importance and principles of integrated hydrographical basin management and develop a case

  19. Effect of Hydrograph Characteristics on Vertical Grain Sorting in Gravel Bed Rivers

    Science.gov (United States)

    Hassan, M. A.; Parker, G.; Egozi, R.

    2005-12-01

    This study focuses on the formation of armour layers over a range of hydrologic conditions that includes two limiting cases; a relatively flat hydrograph that represents conditions produced by continuous snowmelt and a sharply peaked hydrograph that represents conditions associated with flash floods. To achieve our objective we analyzed field evidence, conducted flume experiments and performed numerical simulations. Sediment supply appears to be a first-order control on bed surface armouring, while the shape of the hydrograph plays a secondary role. All constant hydrograph experiments developed a well-armored structured surface while short asymmetrical hydrographs did not show substantial vertical sorting. All symmetrical hydrographs show some degree of sorting, and the sorting tended to become more pronounced with longer duration. Using the numerical framework of Parker, modified Powell, et al. and Wilcock and Crowe, we were able to achieve similar results.

  20. 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.

  1. 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.

  2. 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.

  3. 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

  4. 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.

  5. 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").

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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...

  11. F00190: NOS Hydrographic Survey , Hydrographic and Wire Drag Investigations, Connecticut, 1963-05-13

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  12. 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.

  13. Estimating retention potential of headwater catchment using Tritium time series

    Science.gov (United States)

    Hofmann, Harald; Cartwright, Ian; Morgenstern, Uwe

    2018-06-01

    Headwater catchments provide substantial streamflow to rivers even during long periods of drought. Documenting the mean transit times (MTT) of stream water in headwater catchments and therefore the retention capacities of these catchments is crucial for water management. This study uses time series of 3H activities in combination with major ion concentrations, stable isotope ratios and radon activities (222Rn) in the Lyrebird Creek catchment in Victoria, Australia to provide a unique insight into the mean transit time distributions and flow systems of this small temperate headwater catchment. At all streamflows, the stream has 3H activities (water in the stream is derived from stores with long transit times. If the water in the catchment can be represented by a single store with a continuum of ages, mean transit times of the stream water range from ∼6 up to 40 years, which indicates the large retention potential for this catchment. Alternatively, variations of 3H activities, stable isotopes and major ions can be explained by mixing between of young recent recharge and older water stored in the catchment. While surface runoff is negligible, the variation in stable isotope ratios, major ion concentrations and radon activities during most of the year is minimal (±12%) and only occurs during major storm events. This suggests that different subsurface water stores are activated during the storm events and that these cease to provide water to the stream within a few days or weeks after storm events. The stores comprise micro and macropore flow in the soils and saprolite as well as the boundary between the saprolite and the fractured bed rock. Hydrograph separations from three major storm events using Tritium, electrical conductivity and selected major ions as well a δ18O suggest a minimum of 50% baseflow at most flow conditions. We demonstrate that headwater catchments can have a significant storage capacity and that the relationship between long-water stores and

  14. 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.

  15. 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...

  16. 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.

  17. 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.

  18. 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

  19. 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...

  20. 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.

  1. 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

  2. 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

  3. 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...

  4. 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)…

  5. 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 ...

  6. The Irminger Sea and the Iceland Sea time series measurements of sea water carbon and nutrient chemistry 1983–2008

    Directory of Open Access Journals (Sweden)

    J. Olafsson

    2010-03-01

    Full Text Available This paper describes the ways and means of assembling and quality controling the Irminger Sea and Iceland Sea time-series biogeochemical data which are included in the CARINA data set. The Irminger Sea and the Iceland Sea are hydrographically different regions where measurements of sea water carbon and nutrient chemistry were started in 1983. The sampling is seasonal, four times a year. The carbon chemistry is studied with measurements of the partial pressure of carbon dioxide in seawater, pCO2, and total dissolved inorganic carbon, TCO2. The carbon chemistry data are for surface waters only until 1991 when water column sampling was initiated. Other measured parameters are salinity, dissolved oxygen and the inorganic nutrients nitrate, phosphate and silicate. Because of the CARINA criteria for secondary quality control, depth >1500 m, the IRM-TS could not be included in the routine QC and the IS-TS only in a limited way. However, with the information provided here, the quality of the data can be assessed, e.g. on the basis of the results obtained with the use of reference materials.

  7. 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.

  8. 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

  9. 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...

  10. 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.

  11. H01271: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  12. H03775: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  13. H04095: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  14. H03977: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  15. L01175: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  16. L00415: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  17. H06410: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  18. H01584: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  19. D00012: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  20. H01633: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  1. H01782: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  2. D00018: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  3. H02711: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  4. H02501: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  5. H04578: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  6. H06357: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  7. H03641: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  8. H02012: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  9. H02465: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  10. H04003: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  11. H04738: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  12. H04513: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  13. H00232: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  14. H00380: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  15. H01186: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  16. H06627: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  17. H04165: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  18. H04004: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  19. H00412: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  20. H02259: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  1. H02679: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  2. H03182: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  3. H04575: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  4. H04562: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  5. L01365: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  6. H02455: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  7. H01769: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  8. H02918: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  9. H03567: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  10. L00912: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  11. L00549: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  12. L01953: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  13. L00841: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  14. L02207: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  15. L00117: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  16. L01655: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  17. L00459: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  18. L01616: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  19. L00074: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  20. L00196: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  1. L00564: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  2. L01745: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  3. L02183: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  4. L01818: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  5. L01575: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  6. L01715: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  7. L02211: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  8. L00175: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  9. L01137: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  10. L00317: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  11. H01893: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  12. H04566: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  13. H00450: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  14. H00655: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  15. H02707: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  16. H09482: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  17. H02575: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  18. H02462: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  19. H01628: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  20. H03978: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  1. L02164: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  2. H04296: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  3. L00252: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  4. H02152: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  5. H01824: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  6. L02087: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  7. H02038: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  8. L01055: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  9. H00903: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  10. H02032: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  11. H01668: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  12. H00678: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  13. H01980: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  14. H01950: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  15. H02112: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  16. H04565: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  17. H02195: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  18. H02194: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  19. H02533: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  20. H02528: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  1. H02991: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  2. H02989: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  3. H02994: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  4. H02059: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  5. H03392: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  6. H03929: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  7. D00024: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  8. H02184: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  9. H02471: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  10. H00748: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  11. H00698: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  12. H00497: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  13. H03123: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  14. H02872: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  15. H02252: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  16. H01705: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  17. F00018: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  18. L00259: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  19. H04022: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  20. H03987: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  1. H00263: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  2. H03687: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  3. H01717: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  4. H02141: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  5. H00364: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  6. H00195: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  7. H00149: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  8. H00328: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  9. H02225: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  10. H06289: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  11. H00889: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  12. H04368: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  13. H04851: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  14. H04030: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  15. H03381: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  16. H00435: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  17. H03928: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  18. H00862: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  19. H02770: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  20. H02769: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  1. D00011: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  2. H00407: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  3. H03797: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  4. H04687: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  5. H00509: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  6. H04823: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  7. H00305: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  8. H02573: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  9. H04663: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  10. H01837: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  11. H01809: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  12. H03407: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  13. H03259: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  14. D00086: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  15. H00246: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  16. H13072: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  17. H01341: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  18. H02494: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  19. H02992: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  20. H00861: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  1. H07558: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  2. H02762: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  3. H07497: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  4. H05038: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  5. H00674: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  6. H12885: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  7. W00144: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  8. W00139: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  9. H00394: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  10. H02830: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  11. H01594: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  12. H02019: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  13. W00133: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  14. H07491: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  15. D00060: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  16. W00134: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  17. H00556: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  18. H00803: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  19. H03999: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  20. H00730: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  1. H04821: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  2. H04048: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  3. H03726: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  4. L00330: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  5. H00982: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  6. H00211: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  7. H01724: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  8. L00340: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  9. H07308: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  10. H05019: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  11. L02326: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  12. H02754: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  13. H00339: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  14. H01935: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  15. H03023: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  16. H02911: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  17. H06480: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  18. H00214: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  19. H02009: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  20. H01897: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  1. H00737: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  2. L01227: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  3. L00572: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  4. H03562: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  5. H02603: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  6. H03408: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  7. H03383: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  8. H00236: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  9. H00554: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  10. H02278: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  11. H00615: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  12. H03783: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  13. H00939: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  14. H00147: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  15. H02224: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  16. H01745: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  17. H02592: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  18. H04806: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  19. H01000: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  20. H04803: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  1. H04804: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  2. H00390: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  3. H00785: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  4. H00786: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  5. H00481: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  6. L00469: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  7. H00891: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  8. H00951: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  9. H02336: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  10. H00648: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  11. H00186: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  12. H02348: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  13. H03782: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  14. H02084: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  15. H04329: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  16. H00162: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  17. H02309: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  18. H02622: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  19. H04837: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  20. H00843: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  1. L00138: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  2. H02574: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  3. H03201: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  4. H02487: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  5. L00315: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  6. L00089: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  7. H00177: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  8. L01304: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  9. H04857: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  10. H01635: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  11. H00271: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  12. H00270: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  13. H00875: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  14. H02739: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  15. H07677: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  16. H04137: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  17. F00026: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  18. H03689: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  19. H00368: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  20. H04826: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  1. H00868: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  2. L02094: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  3. H02799: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  4. H03671: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  5. H06168: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  6. H04482: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  7. L00536: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  8. H00219: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  9. L00389: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  10. L00941: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  11. H03709: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  12. H12971: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  13. H02120: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  14. H00910: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  15. H01978: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  16. H00697: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  17. H00870: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  18. H07759: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  19. L01716: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  20. L00585: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  1. H00197: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  2. H02466: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  3. L00971: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  4. H00611: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  5. H03008: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  6. H07524: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  7. H00703: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  8. H03066: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  9. H02988: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  10. L01021: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  11. L00136: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  12. H00670: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  13. H00596: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  14. H03460: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  15. L00253: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  16. L01208: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  17. H01435: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  18. W00132: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  19. L00440: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  20. W00196: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  1. L00320: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  2. H00892: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  3. H05987: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  4. H01703: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  5. H00625: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  6. H02006: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  7. H04595: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  8. H03915: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  9. NOAA's Hydrographic Surveys and Reports

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data to support the compilation of nautical charts and...

  10. H00926: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  11. H03522: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  12. H01561: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  13. H01552: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  14. H04766: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  15. H00277: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  16. H04468: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  17. H04323: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  18. H00306: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  19. H00894: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  20. H04322: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  1. H04313: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  2. H00399: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  3. H00470: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  4. H02917: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  5. H00355: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  6. F00006: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  7. H00457: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  8. D00017: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  9. H06749: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  10. H00685: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  11. H03583: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  12. H04306: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  13. H00642: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  14. H04437: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  15. H04610: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  16. H01701: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  17. H00769: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  18. H05002: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  19. L01273: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...

  20. L00139: NOS Hydrographic Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe...