WorldWideScience

Sample records for ocean time series

  1. 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...

  2. 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.

  3. OceanXtremes: Scalable Anomaly Detection in Oceanographic Time-Series

    Science.gov (United States)

    Wilson, B. D.; Armstrong, E. M.; Chin, T. M.; Gill, K. M.; Greguska, F. R., III; Huang, T.; Jacob, J. C.; Quach, N.

    2016-12-01

    The oceanographic community must meet the challenge to rapidly identify features and anomalies in complex and voluminous observations to further science and improve decision support. Given this data-intensive reality, we are developing an anomaly detection system, called OceanXtremes, powered by an intelligent, elastic Cloud-based analytic service backend that enables execution of domain-specific, multi-scale anomaly and feature detection algorithms across the entire archive of 15 to 30-year ocean science datasets.Our parallel analytics engine is extending the NEXUS system and exploits multiple open-source technologies: Apache Cassandra as a distributed spatial "tile" cache, Apache Spark for in-memory parallel computation, and Apache Solr for spatial search and storing pre-computed tile statistics and other metadata. OceanXtremes provides these key capabilities: Parallel generation (Spark on a compute cluster) of 15 to 30-year Ocean Climatologies (e.g. sea surface temperature or SST) in hours or overnight, using simple pixel averages or customizable Gaussian-weighted "smoothing" over latitude, longitude, and time; Parallel pre-computation, tiling, and caching of anomaly fields (daily variables minus a chosen climatology) with pre-computed tile statistics; Parallel detection (over the time-series of tiles) of anomalies or phenomena by regional area-averages exceeding a specified threshold (e.g. high SST in El Nino or SST "blob" regions), or more complex, custom data mining algorithms; Shared discovery and exploration of ocean phenomena and anomalies (facet search using Solr), along with unexpected correlations between key measured variables; Scalable execution for all capabilities on a hybrid Cloud, using our on-premise OpenStack Cloud cluster or at Amazon. The key idea is that the parallel data-mining operations will be run "near" the ocean data archives (a local "network" hop) so that we can efficiently access the thousands of files making up a three decade time-series

  4. NODC Standard Format Ocean Wind Time Series from Buoys (F101) Data (1975-1985) (NODC Accession 0014194)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This file type contains time series measurements of wind and other surface meteorological parameters taken at fixed locations. The instrument arrays may be deployed...

  5. Ocean time-series reveals recurring seasonal patterns of virioplankton dynamics in the northwestern Sargasso Sea.

    Science.gov (United States)

    Parsons, Rachel J; Breitbart, Mya; Lomas, Michael W; Carlson, Craig A

    2012-02-01

    There are an estimated 10(30) virioplankton in the world oceans, the majority of which are phages (viruses that infect bacteria). Marine phages encompass enormous genetic diversity, affect biogeochemical cycling of elements, and partially control aspects of prokaryotic production and diversity. Despite their importance, there is a paucity of data describing virioplankton distributions over time and depth in oceanic systems. A decade of high-resolution time-series data collected from the upper 300 m in the northwestern Sargasso Sea revealed recurring temporal and vertical patterns of virioplankton abundance in unprecedented detail. An annual virioplankton maximum developed between 60 and 100 m during periods of summer stratification and eroded during winter convective mixing. The timing and vertical positioning of this seasonal pattern was related to variability in water column stability and the dynamics of specific picophytoplankton and heterotrophic bacterioplankton lineages. Between 60 and 100 m, virioplankton abundance was negatively correlated to the dominant heterotrophic bacterioplankton lineage SAR11, as well as the less abundant picophytoplankton, Synechococcus. In contrast, virioplankton abundance was positively correlated to the dominant picophytoplankton lineage Prochlorococcus, and the less abundant alpha-proteobacteria, Rhodobacteraceae. Seasonally, virioplankton abundances were highly synchronous with Prochlorococcus distributions and the virioplankton to Prochlorococcus ratio remained remarkably constant during periods of water column stratification. The data suggest that a significant fraction of viruses in the mid-euphotic zone of the subtropical gyres may be cyanophages and patterns in their abundance are largely determined by Prochlorococcus dynamics in response to water column stability. This high-resolution, decadal survey of virioplankton abundance provides insight into the possible controls of virioplankton dynamics in the open ocean.

  6. Reconstructing surface ocean circulation with 129I time series records from corals.

    Science.gov (United States)

    Chang, Ching-Chih; Burr, George S; Jull, A J Timothy; Russell, Joellen L; Biddulph, Dana; White, Lara; Prouty, Nancy G; Chen, Yue-Gau; Shen, Chuan-Chou; Zhou, Weijian; Lam, Doan Dinh

    2016-12-01

    The long-lived radionuclide 129 I (half-life: 15.7 × 10 6  yr) is well-known as a useful environmental tracer. At present, the global 129 I in surface water is about 1-2 orders of magnitude higher than pre-1960 levels. Since the 1990s, anthropogenic 129 I produced from industrial nuclear fuels reprocessing plants has been the primary source of 129 I in marine surface waters of the Atlantic and around the globe. Here we present four coral 129 I time series records from: 1) Con Dao and 2) Xisha Islands, the South China Sea, 3) Rabaul, Papua New Guinea and 4) Guam. The Con Dao coral 129 I record features a sudden increase in 129 I in 1959. The Xisha coral shows similar peak values for 129 I as the Con Dao coral, punctuated by distinct low values, likely due to the upwelling in the central South China Sea. The Rabaul coral features much more gradual 129 I increases in the 1970s, similar to a published record from the Solomon Islands. The Guam coral 129 I record contains the largest measured values for any site, with two large peaks, in 1955 and 1959. Nuclear weapons testing was the primary 129 I source in the Western Pacific in the latter part of the 20th Century, notably from testing in the Marshall Islands. The Guam 1955 peak and Con Dao 1959 increases are likely from the 1954 Castle Bravo test, and the Operation Hardtack I test is the most likely source of the 1959 peak observed at Guam. Radiogenic iodine found in coral was carried primarily through surface ocean currents. The coral 129 I time series data provide a broad picture of the surface distribution and depth penetration of 129 I in the Pacific Ocean over the past 60 years. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  7. 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

  8. Time-Series Data for Self-Employed Economic Activity Dependent on the Ocean and Great Lakes Economy for Counties, States, and the Nation between 2005 and 2014

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Nonemployer Statistics for Economics: National Ocean Watch (ENOW NES) contains annual time-series data for over 400 coastal counties, 30 coastal states, and the...

  9. Time-Series Data on the Ocean and Great Lakes Economy for Counties, States, and the Nation between 2005 and 2014 (Sector Level)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Economics: National Ocean Watch (ENOW) contains annual time-series data for over 400 coastal counties, 30 coastal states, 8 regions, and the nation, derived from the...

  10. Time-Series Data on the Ocean and Great Lakes Economy for Counties, States, and the Nation between 2005 and 2014(Sector and Industry Level)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Economics: National Ocean Watch (ENOW) contains annual time-series data for about 400 coastal counties, 30 coastal states, and the nation, derived from the Bureau of...

  11. Time-Series Data on the Ocean and Great Lakes Economy for Counties, States, and the Nation between 2005 and 2012 (Sector and Industry Level)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Economics: National Ocean Watch (ENOW) contains annual time-series data for about 400 coastal counties, 30 coastal states, and the nation, derived from the Bureau of...

  12. The Oceanic Flux Program: A three decade time-series of particle flux in the deep Sargasso Sea

    Science.gov (United States)

    Weber, J. C.; Conte, M. H.

    2010-12-01

    The Oceanic Flux Program (OFP), 75 km SE of Bermuda, is the longest running time-series of its kind. Initiated in 1978, the OFP has produced an unsurpassed, nearly continuous record of temporal variability in deep ocean fluxes, with a >90% temporal coverage at 3200m depth. The OFP, in conjunction with the co-located Bermuda-Atlantic Time Series (BATS) and the Bermuda Testbed Mooring (BTM) time-series, has provided key observations enabling detailed assessment of how seasonal and non-seasonal variability in the deep ocean is linked with the overlying physical and biogeochemical environment. This talk will focus on the short-term flux variability that overlies the seasonal flux pattern in the Sargasso Sea, emphasizing episodic extreme flux events. Extreme flux events are responsible for much of the year-to-year variability in mean annual flux and are most often observed during early winter and late spring when surface stratification is weak or transient. In addition to biological phenomena (e.g. salp blooms), passage of productive meso-scale features such as eddies, which alter surface water mixing characteristics and surface export fluxes, may initiate some extreme flux events. Yet other productive eddies show a minimal influence on the deep flux, underscoring the importance of upper ocean ecosystem structure and midwater processes on the coupling between the surface ocean environment and deep fluxes. Using key organic and inorganic tracers, causative processes that influence deep flux generation and the strength of the coupling with the surface ocean environment can be identified.

  13. Forecasting ocean wave energy: A Comparison of the ECMWF wave model with time series methods

    DEFF Research Database (Denmark)

    Reikard, Gordon; Pinson, Pierre; Bidlot, Jean

    2011-01-01

    Recently, the technology has been developed to make wave farms commercially viable. Since electricity is perishable, utilities will be interested in forecasting ocean wave energy. The horizons involved in short-term management of power grids range from as little as a few hours to as long as several...... days. In selecting a method, the forecaster has a choice between physics-based models and statistical techniques. A further idea is to combine both types of models. This paper analyzes the forecasting properties of a well-known physics-based model, the European Center for Medium-Range Weather Forecasts...... (ECMWF) Wave Model, and two statistical techniques, time-varying parameter regressions and neural networks. Thirteen data sets at locations in the Atlantic and Pacific Oceans and the Gulf of Mexico are tested. The quantities to be predicted are the significant wave height, the wave period, and the wave...

  14. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from TIME_SERIES_BATS_1994_1996 in the North Atlantic Ocean from 1994-01-01 to 1996-12-31 (NCEI Accession 0157610)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0157610 includes chemical, meteorological, physical and time series data collected from TIME_SERIES_BATS_1994_1996 in the North Atlantic Ocean from...

  15. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Oscar Elton Sette in the North Pacific Ocean on 2016-06-22 (NCEI Accession 0155170)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0155170 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  16. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean on 2016-08-26 (NCEI Accession 0162238)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0162238 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  17. Physics, Nonlinear Time Series Analysis, Data Assimilation and Hyperfast Modeling of Nonlinear Ocean Waves

    Science.gov (United States)

    2010-09-30

    Hyperfast Modeling of Nonlinear Ocean Waves A. R. Osborne Dipartimento di Fisica Generale, Università di Torino Via Pietro Giuria 1, 10125...PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Universit?i Torino,Dipartimento di Fisica Generale,Via Pietro Giuria 1,10125 Torino, Italy, 8. PERFORMING

  18. Carbon dioxide, temperature, salinity and other variables collected via time series monitoring from MOORINGS in the North Pacific Ocean from 1998-06-22 to 2004-11-23 (NODC Accession 0100079)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0100079 includes chemical, time series and underway - surface data collected from MOORINGS in the North Pacific Ocean and South Pacific Ocean from...

  19. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Ronald H. Brown in the North Pacific Ocean and South Pacific Ocean from 2014-11-05 to 2014-11-24 (NODC Accession 0123338)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0123338 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  20. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Ronald H. Brown in the Arctic Ocean and North Pacific Ocean from 2015-01-14 to 2015-02-13 (NODC Accession 0126056)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0126056 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  1. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Ronald H. Brown in the North Pacific Ocean and South Pacific Ocean from 2015-02-27 to 2015-03-30 (NODC Accession 0127092)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0127092 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  2. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Hi'ialakai in the North Pacific Ocean, Philippine Sea and South Pacific Ocean from 2017-03-26 to 2017-06-21 (NCEI Accession 0164429)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164429 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  3. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Hi'ialakai in the North Pacific Ocean and South Pacific Ocean from 2015-01-22 to 2015-05-04 (NCEI Accession 0127322)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0127322 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  4. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Elton Sette in the North Pacific Ocean and South Pacific Ocean from 2016-02-21 to 2016-03-25 (NCEI Accession 0155172)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0155172 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  5. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Ronald H. Brown in the Coral Sea, North Pacific Ocean and South Pacific Ocean from 2014-10-06 to 2014-11-01 (NODC Accession 0123096)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0123096 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  6. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Ronald H. Brown in the North Pacific Ocean and South Pacific Ocean from 2014-08-25 to 2014-09-27 (NODC Accession 0122504)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0122504 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  7. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Ronald H. Brown in the North Pacific Ocean and South Pacific Ocean from 2014-03-23 to 2014-04-08 (NODC Accession 0120490)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0120490 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  8. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Ronald H. Brown in the Gulf of Alaska, North Pacific Ocean and South Pacific Ocean from 2015-04-10 to 2015-06-24 (NCEI Accession 0129524)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0129524 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  9. A probabilistic method for the estimation of ocean surface currents from short time series of HF radar data

    Science.gov (United States)

    Guérin, Charles-Antoine; Grilli, Stéphan T.

    2018-01-01

    We present a new method for inverting ocean surface currents from beam-forming HF radar data. In contrast with the classical method, which inverts radial currents based on shifts of the main Bragg line in the radar Doppler spectrum, the method works in the temporal domain and inverts currents from the amplitude modulation of the I and Q radar time series. Based on this principle, we propose a Maximum Likelihood approach, which can be combined with a Bayesian inference method assuming a prior current distribution, to infer values of the radial surface currents. We assess the method performance by using synthetic radar signal as well as field data, and systematically comparing results with those of the Doppler method. The new method is found advantageous for its robustness to noise at long range, its ability to accommodate shorter time series, and the possibility to use a priori information to improve the estimates. Limitations are related to current sign errors at far-ranges and biased estimates for small current values and very short samples. We apply the new technique to a data set from a typical 13.5 MHz WERA radar, acquired off of Vancouver Island, BC, and show that it can potentially improve standard synoptic current mapping.

  10. Ichthyoplankton Time Series: A Potential Ocean Observing Network to Provide Indicators of Climate Impacts on Fish Communities along the West Coast of North America

    Science.gov (United States)

    Koslow, J. A.; Brodeur, R.; Duffy-Anderson, J. T.; Perry, I.; jimenez Rosenberg, S.; Aceves, G.

    2016-02-01

    Ichthyoplankton time series available from the Bering Sea, Gulf of Alaska and California Current (Oregon to Baja California) provide a potential ocean observing network to assess climate impacts on fish communities along the west coast of North America. Larval fish abundance reflects spawning stock biomass, so these data sets provide indicators of the status of a broad range of exploited and unexploited fish populations. Analyses to date have focused on individual time series, which generally exhibit significant change in relation to climate. Off California, a suite of 24 midwater fish taxa have declined > 60%, correlated with declining midwater oxygen concentrations, and overall larval fish abundance has declined 72% since 1969, a trend based on the decline of predominantly cool-water affinity taxa in response to warming ocean temperatures. Off Oregon, there were dramatic differences in community structure and abundance of larval fishes between warm and cool ocean conditions. Midwater deoxygenation and warming sea surface temperature trends are predicted to continue as a result of global climate change. US, Canadian, and Mexican fishery scientists are now collaborating in a virtual ocean observing network to synthesize available ichthyoplankton time series and compare patterns of change in relation to climate. This will provide regional indicators of populations and groups of taxa sensitive to warming, deoxygenation and potentially other stressors, establish the relevant scales of coherence among sub-regions and across Large Marine Ecosystems, and provide the basis for predicting future climate change impacts on these ecosystems.

  11. Atlantic Ocean Acidification Test-Bed -- OA Time-Series, Cheeca Rocks, Florida Reef Tract FY2012

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The AOAT project is engaged in monitoring/modeling efforts designed to: a) establish methodologies for monitoring, assessing, and modeling the impacts of Ocean...

  12. High-resolution ocean and atmosphere pCO2 time-series measurements from mooring WA_125W_47N in the North Pacific Ocean, US West Coast from 2006-06-23 to 2015-03-05 (NODC Accession 0115322)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0115322 includes chemical, meteorological, physical and time series data collected from MOORING_WA_125W_47N in the North Pacific Ocean, US West Coast...

  13. Overview of the US JGOFS Bermuda Atlantic Time-series Study (BATS): a decade-scale look at ocean biology and biogeochemistry

    Science.gov (United States)

    Steinberg, Deborah K.; Carlson, Craig A.; Bates, Nicholas R.; Johnson, Rodney J.; Michaels, Anthony F.; Knap, Anthony H.

    The Bermuda Atlantic Time-series Study (BATS) commenced monthly sampling in October 1988 as part of the US Joint Global Ocean Flux Study (JGOFS) program. The goals of the US JGOFS time-series research are to better understand the basic processes that control ocean biogeochemistry on seasonal to decadal time-scales, determine the role of the oceans in the global carbon budget, and ultimately improve our ability to predict the effects of climate change on ecosystems. The BATS program samples the ocean on a biweekly to monthly basis, a strategy that resolves major seasonal patterns and interannual variability. The core cruises last 4-5 d during which hydrography, nutrients, particle flux, pigments and primary production, bacterioplankton abundance and production, and often complementary ancillary measurements are made. This overview focuses on patterns in ocean biology and biogeochemistry over a decade at the BATS site, concentrating on seasonal and interannual changes in community structure, and the physical forcing and other factors controlling the temporal dynamics. Significant seasonal and interannual variability in phytoplankton and bacterioplankton production, biomass, and community structure exists at BATS. No strong relationship exists between primary production and particle flux during the 10 yr record, with the relationship slightly improved by applying an artificial lag of 1 week between production and flux. The prokaryotic picoplankton regularly dominate the phytoplankton community; diatom blooms are rare but occur periodically in the BATS time series. The increase in Chl a concentrations during bloom periods is due to increases by most of the taxa present, rather than by any single group, and there is seasonal succession of phytoplankton. The bacterioplankton often dominate the living biomass, indicating the potential to consume large amounts of carbon and play a major ecological role within the microbial food web. Bacterial biomass, production, and

  14. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Okeanos Explorer in the North Pacific Ocean from 2015-07-10 to 2015-09-03 (NCEI Accession 0141435)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0141435 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  15. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Reuben Lasker in the North Pacific Ocean from 2016-03-22 to 2016-04-23 (NCEI Accession 0150873)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0150873 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  16. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean from 2016-05-27 to 2016-05-28 (NCEI Accession 0164091)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164091 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  17. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean from 2016-03-23 to 2016-04-23 (NCEI Accession 0150875)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0150875 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  18. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Hi'ialakai in the North Pacific Ocean and Philippine Sea from 2014-03-05 to 2014-06-02 (NODC Accession 0119156)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0119156 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  19. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Hi'ialakai in the North Pacific Ocean from 2015-07-09 to 2015-07-16 (NCEI Accession 0129903)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0129903 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  20. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Reuben Lasker in the North Pacific Ocean from 2016-04-26 to 2016-05-19 (NCEI Accession 0153493)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0153493 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  1. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Okeanos Explorer in the North Atlantic Ocean from 2014-02-08 to 2014-02-10 (NODC Accession 0123613)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0123613 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  2. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Pisces in the North Atlantic Ocean from 2013-07-01 to 2013-07-12 (NODC Accession 0117838)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0117838 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  3. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Reuben Lasker in the North Pacific Ocean from 2016-10-06 to 2016-10-13 (NCEI Accession 0164089)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164089 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  4. Underway biological, meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2016-05-06 to 2016-06-05 (NCEI Accession 0153543)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0153543 contains raw underway biological, meteorological, navigational, physical, profile and time series data logged by the Scientific Computer...

  5. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Pisces in the Gulf of Mexico and North Atlantic Ocean from 2013-04-05 to 2013-06-07 (NODC Accession 0117812)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0117812 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  6. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean from 2016-04-28 to 2016-05-09 (NCEI Accession 0151241)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0151241 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  7. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Pisces in the Gulf of Mexico and North Atlantic Ocean from 2015-05-12 to 2015-06-10 (NCEI Accession 0129440)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0129440 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  8. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean from 2017-05-15 to 2017-05-24 (NCEI Accession 0164430)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164430 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  9. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean from 2015-01-31 to 2015-02-04 (NODC Accession 0125756)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0125756 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  10. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the Bay of Fundy and North Atlantic Ocean from 2013-09-06 to 2013-11-19 (NCEI Accession 0115901)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0115901 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  11. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Hi'ialakai in the North Pacific Ocean from 2013-06-26 to 2013-07-03 (NODC Accession 0099244)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0099244 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  12. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Nancy Foster in the North Atlantic Ocean from 2014-05-19 to 2014-05-20 (NODC Accession 0118685)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0118685 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  13. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Pisces in the North Atlantic Ocean from 2016-10-18 to 2016-10-20 (NCEI Accession 0164092)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164092 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  14. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Pisces in the North Atlantic Ocean from 2016-09-21 to 2016-09-29 (NCEI Accession 0164083)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164083 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  15. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Pisces in the North Atlantic Ocean from 2016-11-12 to 2016-11-18 (NCEI Accession 0164093)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164093 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  16. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Pisces in the North Atlantic Ocean from 2016-07-03 to 2016-08-03 (NCEI Accession 0155990)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0155990 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  17. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Pisces in the Caribbean Sea, Gulf of Mexico and North Atlantic Ocean from 2015-10-12 to 2015-11-24 (NCEI Accession 0138341)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0138341 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  18. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Pisces in the North Atlantic Ocean from 2016-10-04 to 2016-10-13 (NCEI Accession 0164086)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164086 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  19. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Pisces in the Gulf of Mexico and North Atlantic Ocean from 2017-04-11 to 2017-06-17 (NCEI Accession 0164341)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164341 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  20. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Pisces in the North Atlantic Ocean from 2017-06-20 to 2017-07-06 (NCEI Accession 0165349)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0165349 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  1. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Pisces in the North Atlantic Ocean from 2017-07-08 to 2017-07-26 (NCEI Accession 0165226)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0165226 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  2. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Gordon Gunter in the Gulf of Mexico and North Atlantic Ocean from 2016-04-10 to 2016-04-20 (NCEI Accession 0165360)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0165360 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  3. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Ronald H. Brown in the North Atlantic Ocean, Rio de la Plata and others from 2017-02-11 to 2017-03-15 (NCEI Accession 0164157)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164157 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  4. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Bering Sea, Gulf of Alaska and North Pacific Ocean from 2013-06-08 to 2013-08-09 (NODC Accession 0123940)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0123940 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  5. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2012-08-07 to 2012-08-24 (NODC Accession 0125711)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0125711 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  6. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2013-07-01 to 2013-08-18 (NODC Accession 0115902)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0115902 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  7. Underway biological, meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2015-03-13 to 2015-05-07 (NCEI Accession 0128346)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0128346 contains raw underway biological, meteorological, navigational, physical, profile and time series data logged by the Scientific Computer...

  8. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Hi'ialakai in the North Pacific Ocean from 2015-07-27 to 2015-08-27 (NCEI Accession 0133933)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0133933 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  9. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean from 2016-05-14 to 2016-05-28 (NCEI Accession 0164090)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164090 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  10. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Gordon Gunter in the Gulf of Mexico and North Atlantic Ocean from 2017-07-02 to 2017-07-18 (NCEI Accession 0165352)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0165352 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  11. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Fairweather in the Coastal Waters of SE Alaska and North Pacific Ocean from 2015-09-18 to 2015-11-13 (NCEI Accession 0137857)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0137857 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  12. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2015-08-12 to 2015-08-21 (NCEI Accession 0131861)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0131861 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  13. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Thomas Jefferson in the North Atlantic Ocean from 2014-04-04 to 2014-11-18 (NODC Accession 0122407)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0122407 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  14. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Okeanos Explorer in the North Atlantic Ocean from 2015-02-09 to 2015-02-13 (NODC Accession 0125757)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0125757 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  15. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Elton Sette in the North Pacific Ocean from 2017-03-29 to 2017-04-07 (NCEI Accession 0164431)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164431 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  16. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Okeanos Explorer in the North Pacific Ocean from 2015-09-12 to 2015-09-30 (NCEI Accession 0142173)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0142173 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  17. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Oscar Elton Sette in the North Pacific Ocean and Philippine Sea from 2015-06-11 to 2015-07-14 (NCEI Accession 0129902)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0129902 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  18. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Oregon II in the Gulf of Mexico and North Atlantic Ocean from 2014-05-04 to 2014-05-31 (NODC Accession 0118842)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0118842 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  19. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Gordon Gunter in the North Atlantic Ocean from 2014-05-05 to 2014-05-09 (NCEI Accession 0149716)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0149716 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  20. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Gulf of Alaska and North Pacific Ocean from 2014-02-21 to 2014-03-01 (NODC Accession 0125086)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0125086 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  1. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Bering Sea, Gulf of Alaska and North Pacific Ocean from 2016-03-02 to 2016-03-09 (NCEI Accession 0150732)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0150732 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  2. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Bering Sea, Gulf of Alaska and North Pacific Ocean from 2015-05-14 to 2015-06-05 (NCEI Accession 0130586)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0130586 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  3. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Bering Sea, Gulf of Alaska and North Pacific Ocean from 2014-03-13 to 2014-03-25 (NODC Accession 0124597)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0124597 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  4. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Bering Sea, Gulf of Alaska and North Pacific Ocean from 2013-08-15 to 2013-09-19 (NODC Accession 0123941)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0123941 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  5. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Bering Sea, Gulf of Alaska and North Pacific Ocean from 2014-08-17 to 2014-10-06 (NODC Accession 0124596)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0124596 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  6. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Bering Sea, Gulf of Alaska and North Pacific Ocean from 2015-04-24 to 2015-05-10 (NCEI Accession 0130737)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0130737 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  7. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Gordon Gunter in the Gulf of Mexico and North Atlantic Ocean from 2015-04-14 to 2015-06-13 (NCEI Accession 0128347)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0128347 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  8. Underway meteorological, time series, navigational, physical and optical data collected aboard NOAA Ship Okeanos Explorer in the Gulf of Mexico and North Atlantic Ocean from 2014-02-24 to 2014-03-18 (NCEI Accession 0123616)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0123616 contains raw underway meteorological, time series, navigational, physical and optical data logged by the Scientific Computer System (SCS)...

  9. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Gordon Gunter in the North Atlantic Ocean from 2017-06-10 to 2017-06-23 (NCEI Accession 0164441)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164441 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  10. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Gordon Gunter in the North Atlantic Ocean from 2016-05-21 to 2016-06-04 (NCEI Accession 0155169)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0155169 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  11. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Gordon Gunter in the Gulf of Mexico and North Atlantic Ocean from 2014-07-04 to 2014-07-31 (NODC Accession 0120740)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0120740 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  12. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Nancy Foster in the Gulf of Mexico and North Atlantic Ocean from 2015-06-15 to 2015-06-28 (NCEI Accession 0129875)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0129875 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  13. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Okeanos Explorer in the North Pacific Ocean from 2015-10-07 to 2015-10-16 (NCEI Accession 0150689)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0150689 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  14. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Reuben Lasker in the North Pacific Ocean from 2017-03-21 to 2017-04-22 (NCEI Accession 0164340)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164340 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  15. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Okeanos Explorer in the North Atlantic Ocean from 2014-08-09 to 2014-10-07 (NODC Accession 0125346)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0125346 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  16. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Reuben Lasker in the North Pacific Ocean from 2016-01-06 to 2016-01-30 (NCEI Accession 0150692)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0150692 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  17. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Ronald H. Brown in the Coastal Waters of SE Alaska and North Pacific Ocean from 2016-06-23 to 2016-07-09 (NCEI Accession 0155758)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0155758 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  18. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Ronald H. Brown in the North Pacific Ocean from 2016-03-29 to 2016-04-25 (NCEI Accession 0155759)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0155759 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  19. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Pisces in the North Atlantic Ocean from 2015-06-18 to 2015-07-01 (NCEI Accession 0129541)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0129541 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  20. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Hi'ialakai in the North Pacific Ocean from 2013-07-09 to 2013-07-16 (NODC Accession 0113243)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0113243 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  1. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Bering Sea, Gulf of Alaska and North Pacific Ocean from 2014-04-04 to 2014-05-02 (NODC Accession 0125006)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0125006 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  2. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Thomas Jefferson in the North Atlantic Ocean from 2013-09-24 to 2013-11-04 (NODC Accession 0123614)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0123614 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  3. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean from 2015-03-07 to 2015-03-22 (NODC Accession 0126660)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0126660 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  4. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean from 2015-05-05 to 2015-05-18 (NCEI Accession 0128172)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0128172 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  5. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Oregon II in the Gulf of Mexico and North Atlantic Ocean from 2015-07-25 to 2015-09-27 (NCEI Accession 0132051)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0132051 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  6. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Nancy Foster in the North Atlantic Ocean from 2013-07-12 to 2013-07-21 (NODC Accession 0113448)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0113448 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  7. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Gordon Gunter in the North Atlantic Ocean from 2016-06-30 to 2016-07-16 (NCEI Accession 0165361)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0165361 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  8. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Elton Sette in the North Pacific Ocean from 2014-09-25 to 2014-10-27 (NODC Accession 0123056)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0123056 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  9. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2015-05-19 to 2015-06-03 (NCEI Accession 0129421)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0129421 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  10. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Oregon II in the Gulf of Mexico and North Atlantic Ocean from 2014-07-26 to 2014-09-29 (NODC Accession 0122397)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0122397 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  11. Underway biological, meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2014-03-31 to 2014-05-23 (NODC Accession 0119096)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0119096 contains raw underway biological, meteorological, navigational, physical, profile and time series data logged by the Scientific Computer...

  12. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Elton Sette in the North Pacific Ocean and Philippine Sea from 2014-06-19 to 2014-07-19 (NODC Accession 0123094)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0123094 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  13. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Elton Sette in the North Pacific Ocean from 2014-08-30 to 2014-09-19 (NCEI Accession 0123092)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0123092 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  14. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean from 2017-04-27 to 2017-05-11 (NCEI Accession 0164342)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164342 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  15. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Thomas Jefferson in the North Atlantic Ocean from 2017-07-11 to 2017-07-22 (NCEI Accession 0164798)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164798 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  16. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Thomas Jefferson in the North Atlantic Ocean from 2017-07-22 to 2017-07-26 (NCEI Accession 0164960)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164960 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  17. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Elton Sette in the North Pacific Ocean from 2016-06-13 to 2016-06-22 (NCEI Accession 0155171)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0155171 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  18. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Oregon II in the Gulf of Mexico and North Atlantic Ocean from 2017-07-26 to 2017-08-10 (NCEI Accession 0164961)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164961 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  19. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Hi'ialakai in the North Pacific Ocean from 2013-09-05 to 2013-09-20 (NODC Accession 0113248)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0113248 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  20. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean on 2016-06-26 (NCEI Accession 0162236)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0162236 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  1. Underway biological, meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2015-09-01 to 2015-11-06 (NCEI Accession 0132052)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0132052 contains raw underway biological, meteorological, navigational, physical, profile and time series data logged by the Scientific Computer...

  2. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2014-07-24 to 2014-07-30 (NODC Accession 0125266)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0125266 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  3. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Oscar Elton Sette in the North Pacific Ocean from 2015-04-03 to 2015-04-15 (NCEI Accession 0130368)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0130368 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  4. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Nancy Foster in the North Atlantic Ocean from 2013-08-31 to 2013-09-07 (NODC Accession 0113487)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0113487 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  5. Underway meteorological, navigational, optical, physical, time series and trawl data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean from 2013-04-06 to 2013-04-30 (NCEI Accession 0115912)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0115912 contains raw underway meteorological, navigational, optical, physical, time series and trawl data logged by the Scientific Computer System...

  6. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean from 2016-01-09 to 2016-02-09 (NCEI Accession 0150817)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0150817 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  7. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean from 2017-03-29 to 2017-04-20 (NCEI Accession 0164320)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164320 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  8. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean from 2015-10-06 to 2015-10-13 (NCEI Accession 0164861)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164861 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  9. Underway meteorological, navigational, optical, physical, profile, time series and trawl data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean from 2013-09-09 to 2013-09-16 (NODC Accession 0116842)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0116842 contains raw underway meteorological, navigational, optical, physical, profile, time series and trawl data logged by the Scientific Computer...

  10. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Pisces in the North Atlantic Ocean from 2014-07-06 to 2014-08-02 (NODC Accession 0121197)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0121197 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  11. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean from 2014-04-15 to 2014-05-12 (NODC Accession 0118545)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0118545 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  12. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Pisces in the North Atlantic Ocean from 2015-07-10 to 2015-08-04 (NCEI Accession 0130690)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0130690 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  13. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Bell M. Shimada in the Coastal Waters of SE Alaska and North Pacific Ocean from 2016-02-21 to 2016-03-11 (NCEI Accession 0150967)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0150967 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  14. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Gulf of Alaska and North Pacific Ocean from 2013-02-08 to 2013-03-05 (NODC Accession 0124184)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0124184 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  15. Carbon dioxide, temperature, salinity and other variables collected via time series profile monitoring from Kairei, MIRAI and NATSUSHIMA in the North Pacific Ocean from 1999-05-28 to 2008-10-26 (NODC Accession 0100115)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0100115 includes chemical, discrete bottle, physical and time series profile data collected from Kairei, MIRAI and NATSUSHIMA in the North Pacific...

  16. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Gulf of Alaska and North Pacific Ocean from 2017-02-07 to 2017-02-16 (NCEI Accession 0164963)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164963 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  17. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Bering Sea, Gulf of Alaska and North Pacific Ocean from 2013-05-15 to 2013-06-01 (NODC Accession 0124207)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0124207 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  18. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Bering Sea, Gulf of Alaska and North Pacific Ocean from 2013-04-29 to 2013-05-11 (NODC Accession 0124208)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0124208 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  19. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Bering Sea, Gulf of Alaska and North Pacific Ocean from 2017-05-11 to 2017-06-02 (NCEI Accession 0165021)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0165021 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  20. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Bering Sea, Gulf of Alaska and North Pacific Ocean from 2015-08-20 to 2015-09-02 (NCEI Accession 0131578)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0131578 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  1. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Bering Sea, Gulf of Alaska and North Pacific Ocean from 2017-06-08 to 2017-07-15 (NCEI Accession 0165028)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0165028 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  2. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Bering Sea, Gulf of Alaska and North Pacific Ocean from 2015-09-23 to 2015-10-06 (NCEI Accession 0137392)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0137392 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  3. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Gulf of Alaska and North Pacific Ocean from 2015-02-09 to 2015-03-03 (NODC Accession 0127242)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0127242 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  4. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Bering Sea, Gulf of Alaska and North Pacific Ocean from 2016-09-30 to 2016-10-07 (NCEI Accession 0165090)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0165090 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  5. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Bering Sea, Gulf of Alaska and North Pacific Ocean from 2016-03-09 to 2016-03-24 (NCEI Accession 0150822)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0150822 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  6. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2012-05-31 to 2012-06-14 (NODC Accession 0125709)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0125709 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  7. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Gulf of Alaska and North Pacific Ocean from 2016-02-11 to 2016-02-20 (NCEI Accession 0150731)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0150731 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  8. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean from 2015-05-29 to 2015-06-10 (NCEI Accession 0129494)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0129494 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  9. Carbon dioxide, temperature, salinity and other variables collected via time series monitoring from METEOR, POSEIDON and others in the North Atlantic Ocean from 1995-10-02 to 2009-11-25 (NODC Accession 0100064)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0100064 includes chemical, physical, time series and underway - surface data collected from METEOR, POSEIDON, TALIARTE and VICTOR HENSEN in the North...

  10. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Bering Sea, Gulf of Alaska and North Pacific Ocean from 2017-04-22 to 2017-05-08 (NCEI Accession 0165013)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0165013 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  11. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Hi'ialakai in the North Pacific Ocean and Philippine Sea from 2014-05-11 to 2014-05-22 (NODC Accession 0119200)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0119200 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  12. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Fairweather in the Coastal Waters of Southeast Alaska and British Columbia and North Pacific Ocean from 2016-05-25 to 2016-06-18 (NCEI Accession 0162234)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0162234 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  13. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Nancy Foster in the North Atlantic Ocean from 2014-04-20 to 2014-04-30 (NODC Accession 0118187)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0118187 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  14. Underway biological, meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2016-09-07 to 2016-09-23 (NCEI Accession 0164080)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164080 contains raw underway biological, meteorological, navigational, physical, profile and time series data logged by the Scientific Computer...

  15. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Hi'ialakai in the North Pacific Ocean from 2014-09-06 to 2014-09-30 (NODC Accession 0122499)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0122499 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  16. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2017-07-06 to 2017-07-19 (NCEI Accession 0164783)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164783 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  17. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2012-02-27 to 2012-05-04 (NODC Accession 0125710)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0125710 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  18. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2015-07-27 to 2015-08-07 (NCEI Accession 0130538)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0130538 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  19. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2014-08-05 to 2014-08-16 (NODC Accession 0125265)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0125265 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  20. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Nancy Foster in the Gulf of Mexico and North Atlantic Ocean from 2014-09-01 to 2014-09-14 (NODC Accession 0123337)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0123337 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  1. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2017-06-08 to 2017-06-22 (NCEI Accession 0164795)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164795 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  2. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Elton Sette in the North Pacific Ocean and Philippine Sea from 2014-07-24 to 2014-08-25 (NODC Accession 0123095)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0123095 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  3. Underway biological, meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the Bay of Fundy and North Atlantic Ocean from 2014-09-07 to 2014-11-13 (NODC Accession 0123520)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0123520 contains raw underway biological, meteorological, navigational, physical, profile and time series data logged by the Scientific Computer...

  4. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Elton Sette in the North Pacific Ocean and Philippine Sea from 2014-05-31 to 2014-06-16 (NODC Accession 0123093)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0123093 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  5. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2015-11-12 to 2015-11-17 (NCEI Accession 0138157)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0138157 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  6. Underway navigational, physical and time series data collected aboard NOAA Ship Thomas Jefferson in the North Atlantic Ocean from 2013-06-17 to 2013-10-02 (NODC Accession 0123055)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0123055 contains raw underway navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA Ship Thomas...

  7. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2015-05-19 to 2015-06-03 (NCEI Accession 0134847)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0134847 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  8. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2014-06-18 to 2014-07-01 (NODC Accession 0125584)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0125584 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  9. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2013-06-10 to 2013-06-24 (NODC Accession 0115702)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0115702 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  10. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2012-06-18 to 2012-06-28 (NODC Accession 0125666)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0125666 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  11. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Thomas Jefferson in the North Atlantic Ocean from 2013-03-11 to 2013-03-13 (NODC Accession 0123054)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0123054 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  12. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2012-07-04 to 2012-07-18 (NODC Accession 0125758)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0125758 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  13. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2012-09-04 to 2012-11-11 (NODC Accession 0125919)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0125919 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  14. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2016-07-18 to 2016-08-25 (NCEI Accession 0162239)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0162239 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  15. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2013-03-14 to 2013-05-09 (NODC Accession 0115052)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0115052 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  16. Underway meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2015-06-10 to 2015-07-02 (NCEI Accession 0129527)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0129527 contains raw underway meteorological, navigational, physical, profile and time series data logged by the Scientific Computer System (SCS)...

  17. Underway biological, meteorological, navigational, physical, profile and time series data collected aboard NOAA Ship Henry B. Bigelow in the North Atlantic Ocean from 2017-03-28 to 2017-04-27 (NCEI Accession 0164797)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164797 contains raw underway biological, meteorological, navigational, physical, profile and time series data logged by the Scientific Computer...

  18. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Thomas Jefferson in the North Atlantic Ocean from 2015-06-24 to 2015-07-03 (NCEI Accession 0142627)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0142627 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  19. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Pisces in the North Atlantic Ocean from 2016-06-08 to 2016-06-25 (NCEI Accession 0155294)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0155294 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  20. Underway composition & location, meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Gordon Gunter in the Bay of Fundy and North Atlantic Ocean from 2015-06-18 to 2015-07-24 (NCEI Accession 0130005)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0130005 contains raw underway composition & location, meteorological, navigational, optical, physical, profile and time series data logged by the...

  1. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Nancy Foster in the North Atlantic Ocean from 2014-11-12 to 2014-11-21 (NODC Accession 0125582)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0125582 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  2. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Gordon Gunter in the Gulf of Mexico and North Atlantic Ocean from 2015-08-07 to 2015-09-28 (NCEI Accession 0131988)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0131988 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  3. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Nancy Foster in the Gulf of Mexico and North Atlantic Ocean from 2013-09-10 to 2013-09-20 (NODC Accession 0113488)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0113488 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  4. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean from 2013-03-01 to 2013-03-10 (NODC Accession 0116096)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0116096 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  5. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Ronald H. Brown in the Arctic Ocean, Beaufort Sea and others from 2015-08-06 to 2015-09-04 (NCEI Accession 0141104)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0141104 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  6. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean from 2013-01-11 to 2013-02-02 (NCEI Accession 0115911)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0115911 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  7. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Okeanos Explorer in the Gulf of Mexico and North Atlantic Ocean from 2014-05-07 to 2014-05-22 (NODC Accession 0125618)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0125618 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  8. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean from 2014-09-25 to 2014-09-30 (NCEI Accession 0136936)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0136936 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  9. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean from 2017-02-17 to 2017-02-25 (NCEI Accession 0164313)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164313 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  10. Underway meteorological, navigational, optical, physical, profile, time series and trawl data collected aboard NOAA Ship Gordon Gunter in the North Atlantic Ocean from 2014-03-11 to 2014-04-28 (NODC Accession 0118186)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0118186 contains raw underway meteorological, navigational, optical, physical, profile, time series and trawl data logged by the Scientific Computer...

  11. Temporal and spatial variability of phytoplankton pigment concentrations in the Indian Ocean, derived from the CZCS time series images

    Directory of Open Access Journals (Sweden)

    2005-01-01

    Full Text Available A total of 93 monthly global composite remotely sensed ocean color images from the Coastal Zone Color Scanner (CZCS on board the Nimbus-7 satellite were extracted for the Indian Ocean region (35ºN–55ºS; 30–120ºE to examine the seasonal variations in phytoplankton pigment concentrations, resulting from large-scale changes in physical oceanographic processes. The CZCS data sets were analyzed with the PC-SEAPAK software, and revealed large phytoplankton blooms in the northwest Arabian Sea and off the Somali coast. The blooms were triggered by wind-driven upwelling during the southwest monsoonal months of August and September. In the northern Arabian Sea, phytoplankton blooms, detected from January to March, appeared to be associated with nutrient enhancement resulting from winter convective mixing. In the Bay of Bengal, higher pigment concentrations were confined to the coastal regions but varied only marginally between seasons both in the coastal and offshore regions. Phytoplankton pigment concentrations were consistently low in the open Indian Ocean. Analysis of pigment concentrations extracted from the monthly-accumulated images revealed that the Arabian Sea sustained a greater biomass of phytoplankton compared with any other region of the Indian Ocean. Overall, the coastal regions of the Indian Ocean are richer in phytoplankton pigment than the open Indian Ocean. The number of images in individual areas was highly variable throughout the region due to varying cloud cover.

  12. A Coupled Epipelagic-Meso/Bathypelagic Particle Flux Model for the Bermuda Atlantic Time-series Station (BATS)/Oceanic Flux Program (OFP) Site

    Science.gov (United States)

    Glover, D. M.; Conte, M.

    2002-12-01

    Of considerable scientific interest is the role remineralization plays in the global carbon cycle. It is the ``biological pump'' that fixes carbon in the upper water column and exports it for long time periods to the deep ocean. From a global carbon cycle point-of-view, it is the processes that govern remineralization in the mid- to deep-ocean waters that provide the feedback to the biogeochemical carbon cycle. In this study we construct an ecosystem model that serves as a mechanistic link between euphotic processes and mesopelagic and bathypelagic processes. We then use this prognostic model to further our understanding of the unparalleled time-series of deep-water sediment traps (21+ years) at the Oceanic Flux Program (OFP) and the euphotic zone measurements (10+ years) at the Bermuda Atlantic Time-series Site (BATS). At the core of this mechanistic ecosystem model of the mesopelagic zone is a model that consists of an active feeding habit zooplankton, a passive feeding habit zooplankton, large detritus (sinks), small detritus (non-sinking), and a nutrient pool. As the detritus, the primary source of food, moves through the water column it is fed upon by the active/passive zooplankton pair and undergoes bacterially mediated remineralization into nutrients. The large detritus pool at depth gains material from the formation of fecal pellets from the passive and active zooplankton. Sloppy feeding habits of the active zooplankton contribute to the small detrital pool. Zooplankton mortality (both classes) also contribute directly to the large detritus pool. Aggregation and disaggregation transform detrital particles from one pool to the other and back again. The nutrients at each depth will gain from detrital remineralization and zooplankton excretion. The equations that model the active zooplankton, passive zooplankton, large detritus, small detritus, and nutrients will be reviewed, results shown and future model modifications discussed.

  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. 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...

  15. 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...

  16. 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...

  17. 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...

  18. 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)""…

  19. 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

  20. Biological, chemical, physical and time series data collected from station WQB04 by University of Hawai'i at Hilo and assembled by Pacific Islands Ocean Observing System (PacIOOS) in the North Pacific Ocean from 2010-10-23 to 2016-12-31 (NCEI Accession 0161523)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0161523 contains biological, chemical, physical and time series data in netCDF formatted files, which follow the Climate and Forecast metadata...

  1. 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...

  2. 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...

  3. Modelling bursty time series

    International Nuclear Information System (INIS)

    Vajna, Szabolcs; Kertész, János; Tóth, Bálint

    2013-01-01

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

  4. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING TAO155W and MOORING_TAO155W in the North Pacific Ocean and South Pacific Ocean from 1997-11-14 to 2012-06-11 (NODC Accession 0100084)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0100084 includes chemical, meteorological, physical and time series data collected from MOORING TAO155W and MOORING_TAO155W in the North Pacific Ocean...

  5. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING_TAO165E0N in the North Pacific Ocean and South Pacific Ocean from 2010-02-23 to 2013-02-03 (NODC Accession 0113238)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0113238 includes chemical, meteorological, physical and time series data collected from MOORING_TAO165E0N in the North Pacific Ocean and South Pacific...

  6. 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

  7. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, carbon dioxide (CO2) gas analyzer and other instruments from the Mooring LaPush_125W_48N in the North Pacific Ocean from 2010-07-16 to 2015-11-19 (NODC Accession 0100072)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0100072 includes chemical, physical and time series data collected from MOORINGS in the North Pacific Ocean and Olympic Coast National Marine...

  8. Partial pressure (or fugacity) of carbon dioxide, salinity and SEA SURFACE TEMPERATURE collected from time series observations using Carbon dioxide (CO2) gas analyzer, Shower head chamber equilibrator for autonomous carbon dioxide (CO2) measurement and other instruments from Polaris II in the South Pacific Ocean from 2006-08-29 to 2006-10-24 (NODC Accession 0112883)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0112883 includes time series data collected from Polaris II in the South Pacific Ocean from 2006-08-29 to 2006-10-24. These data include Partial...

  9. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING_SOFS_142W_46S in the Indian Ocean from 2011-11-24 to 2012-09-24 (NODC Accession 0118546)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0118546 includes chemical, meteorological, physical and time series data collected from MOORING_SOFS_142W_46S in the Indian Ocean from 2011-11-24 to...

  10. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING_ALAWAI_158W_21N in the North Pacific Ocean from 2008-06-06 to 2014-07-28 (NCEI Accession 0157360)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0157360 includes chemical, meteorological, physical and time series data collected from MOORING_ALAWAI_158W_21N in the North Pacific Ocean from...

  11. pH, alkalinity, temperature, salinity and other variables collected from time series profile observations using Alkalinity titrator, CTD and other instruments from the Al Amir Moulay Abdellah in the North Atlantic Ocean and Strait of Gibraltar from 2005-05-04 to 2007-05-08 (NODC Accession 0112928)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0112928 includes chemical, discrete sample, physical and time series profile data collected from Al Amir Moulay Abdellah in the North Atlantic Ocean...

  12. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING_KILO_NALU_158W_21N in the North Pacific Ocean from 2008-06-07 to 2015-01-21 (NCEI Accession 0157251)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0157251 includes chemical, meteorological, physical and time series data collected from MOORING_KILO_NALU_158W_21N in the North Pacific Ocean from...

  13. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING MOSEAN_158W_23N in the North Pacific Ocean from 2004-12-19 to 2007-07-30 (NODC Accession 0100073)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0100073 includes chemical, meteorological, physical and time series data collected from MOORING MOSEAN_158W_23N in the North Pacific Ocean from...

  14. Primary production and sediment trap flux measurements and calculations by the Hawaii Ocean Time-series (HOT) program at Station ALOHA in the North Pacific 100 miles north of Oahu, Hawaii for Cruises HOT1-227 during 1988-2010 (NODC Accession 0089168)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Hawaii Ocean Time-series (HOT) program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii....

  15. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING JKEO_147E_38N in the North Pacific Ocean from 2007-02-18 to 2007-10-03 (NODC Accession 0100070)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0100070 includes chemical, meteorological, physical and time series data collected from MOORING JKEO_147E_38N in the North Pacific Ocean from...

  16. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING STRATUS_85W_20S, in the South Pacific Ocean from 2006-10-16 to 2015-04-03 (NODC Accession 0100075)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0100075 includes chemical, meteorological, physical and time series data collected from MOORING STRATUS_85W_20S in the South Pacific Ocean from...

  17. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING_HOG_REEF_64W_32N in the North Atlantic Ocean from 2010-12-05 to 2015-01-07 (NODC Accession 0117060)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0117060 includes chemical, meteorological, physical and time series data collected from MOORING_HOG_REEF_64W_32N in the North Atlantic Ocean from...

  18. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING_NH_70W_43N in the North Atlantic Ocean from 2006-07-13 to 2014-07-19 (NODC Accession 0115402)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0115402 includes chemical, meteorological, physical and time series data collected from MOORING_NH_70W_43N in the North Atlantic Ocean from 2006-07-13...

  19. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORINGS in the North Atlantic Ocean from 2005-10-22 to 2007-10-01 (NODC Accession 0100065)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0100065 includes chemical, physical and time series data collected from MOORINGS in the North Atlantic Ocean from 2005-10-22 to 2007-10-01. These data...

  20. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING_NH10_124W in the North Pacific Ocean from 2014-04-03 to 2015-09-28 (NCEI Accession 0157247)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0157247 includes chemical, meteorological, physical and time series data collected from MOORING_NH10_124W in the North Pacific Ocean from 2014-04-03...

  1. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING CRIMP1_158W_21N in the North Pacific Ocean from 2005-12-01 to 2008-05-30 (NODC Accession 0100069)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0100069 includes chemical, meteorological, physical and time series data collected from MOORING CRIMP1_158W_21N in the North Pacific Ocean from...

  2. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING WHOTS_158W_23N in the North Pacific Ocean from 2007-06-26 to 2015-07-15 (NODC Accession 0100080)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0100080 includes chemical, meteorological, physical and time series data collected from WHOTS_158W_23N in the North Pacific Ocean from 2007-06-26 to...

  3. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING PIRATA6S10W in the South Atlantic Ocean from 2006-06-08 to 2013-10-25 (NODC Accession 0100217)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0100217 includes chemical, meteorological, physical and time series data collected from MOORING PIRATA6S10W in the South Atlantic Ocean from...

  4. Salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORINGS_PAPA_145W_50N in the North Pacific Ocean from 2007-06-08 to 2014-11-06 (NCEI Accession 0160486)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0160486 includes chemical, meteorological, physical and time series data collected from MOORINGS_PAPA_145W_50N in the North Pacific Ocean from...

  5. Dissolved inorganic carbon, alkalinity, temperature, salinity and other variables collected from discrete sample, profile and time series profile observations using CTD, bottle and other instruments from LA CURIEUSE in the Indian Ocean from 1990-01-27 to 1995-01-08 (NODC Accession 0112882)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0112882 includes biological, chemical, discrete sample, physical, profile and time series profile data collected from LA CURIEUSE in the Indian Ocean...

  6. 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

  7. 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.

  8. Inter-annual variability in the thermal structure of an oceanic time series station off Ecuador (1990-2003) associated with El Niño events

    Science.gov (United States)

    Garcés-Vargas, José; Schneider, Wolfgang; Abarca del Río, Rodrigo; Martínez, Rodney; Zambrano, Eduardo

    2005-10-01

    Previously unpublished data (1990-2003) from a marine station located 20 km off the coast of Ecuador (Station La Libertad, 02°12'S, 080°55'W) are employed to investigate oceanic inter-annual variability in the far eastern equatorial Pacific, and its relation to the central-eastern equatorial Pacific. La Libertad is the only time series station between the Galapagos Islands and the South American coast, the region most affected by El Niño events (El Niño 2 region, 0-5°S, 90°W-80°W). Although configured and serviced differently, station La Libertad can be looked at as an eastern extension of the TAO/TRITON monitoring system, whose easternmost mooring is located at 95°W, 1550 km offshore. This study of El Niño's impact on the thermocline and its relationship to sea surface temperature revealed anomalies in the thermocline at station La Libertad some 2-4 months before their appearance at the sea surface. Inter-annual variability, namely quasi-biennial and quasi-quadrennial oscillations, accounts for roughly 80% of the total variance in temperature anomalies observed in the water column at station La Libertad. The coincidence in both phase and amplitude of these inter-annual oscillations explains the strength of El Niño events in the water column off La Libertad. We further show that anomalies in heat content appear 8-9 weeks earlier at 140°W in the equatorial Pacific (6550 km away from the coast) than at the coast itself. The arrival of El Niño, which has important regional social consequences as well as those for local fisheries, could therefore be predicted in the sub-surface waters off Ecuador by using these anomalies as a complementary index. In addition, the speed of the eastward propagation of these El Niño-associated anomalies' suggests the possible participation of higher-order baroclinic mode Kelvin waves and associated interaction processes in the eastern Pacific, which should be further investigated.

  9. 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.

  10. Twice daily low-passed filtered time-series data from inverted echo sounders for the Hawaii Ocean Time Series (HOT) project north of Oahu, Hawaii from 19910201 to 19980715 (NODC Accession 9900215)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited...

  11. An overview of the SeaWiFS project and strategies for producing a climate research quality global ocean bio-optical time series

    Science.gov (United States)

    McClain, Charles R.; Feldman, Gene C.; Hooker, Stanford B.

    2004-01-01

    The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Project Office was formally initiated at the NASA Goddard Space Flight Center in 1990. Seven years later, the sensor was launched by Orbital Sciences Corporation under a data-buy contract to provide 5 years of science quality data for global ocean biogeochemistry research. To date, the SeaWiFS program has greatly exceeded the mission goals established over a decade ago in terms of data quality, data accessibility and usability, ocean community infrastructure development, cost efficiency, and community service. The SeaWiFS Project Office and its collaborators in the scientific community have made substantial contributions in the areas of satellite calibration, product validation, near-real time data access, field data collection, protocol development, in situ instrumentation technology, operational data system development, and desktop level-0 to level-3 processing software. One important aspect of the SeaWiFS program is the high level of science community cooperation and participation. This article summarizes the key activities and approaches the SeaWiFS Project Office pursued to define, achieve, and maintain the mission objectives. These achievements have enabled the user community to publish a large and growing volume of research such as those contributed to this special volume of Deep-Sea Research. Finally, some examples of major geophysical events (oceanic, atmospheric, and terrestrial) captured by SeaWiFS are presented to demonstrate the versatility of the sensor.

  12. Seasonal and interannual variability in deep ocean particle fluxes at the Oceanic Flux Program (OFP)/Bermuda Atlantic Time Series (BATS) site in the western Sargasso Sea near Bermuda

    Science.gov (United States)

    Conte, Maureen H.; Ralph, Nate; Ross, Edith H.

    Since 1978, the Oceanic Flux Program (OFP) time-series sediment traps have measured particle fluxes in the deep Sargasso Sea near Bermuda. There is currently a 20+yr flux record at 3200-m depth, a 12+yr flux at 1500-m depth, and a 9+yr record at 500-m depth. Strong seasonality is observed in mass flux at all depths, with a flux maximum in February-March and a smaller maximum in December-January. There is also significant interannual variability in the flux, especially with respect to the presence/absence of the December-January flux maximum and in the duration of the high flux period in the spring. The flux records at the three depths are surprisingly coherent, with no statistically significant temporal lag between 500 and 3200-m fluxes at our biweekly sample resolution. Bulk compositional data indicate an extremely rapid decrease in the flux of organic constituents with depth between 500 and 1500-m, and a smaller decrease with depth between 1500 and 3200-m depth. In contrast, carbonate flux is uniform or increases slightly between 500 and 1500-m, possibly reflecting deep secondary calcification by foraminifera. The lithogenic flux increases by over 50% between 500 and 3200-m depth, indicating strong deep water scavenging/repackaging of suspended lithogenic material. Concurrent with the rapid changes in flux composition, there is a marked reduction in the heterogeneity of the sinking particle pool with depth, especially within the mesopelagic zone. By 3200-m depth, the bulk composition of the sinking particle pool is strikingly uniform, both seasonally and over variations in mass flux of more than an order of magnitude. These OFP results provide strong indirect evidence for the intensity of reprocessing of the particle pool by resident zooplankton within mesopelagic and bathypelagic waters. The rapid loss of organic components, the marked reduction in the heterogeneity of the bulk composition of the flux, and the increase in terrigenous fluxes with depth are most

  13. Ocean water temperature from data loggers from the HALE-ALOHA Moorings in the North Pacific Ocean as part of the Joint Global Ocean Flux (JGOFS), the World Ocean Circulation Experiment (WOCE), and Hawaii Ocean Time-series (HOT) from 24 April 1998 to 03 May 1999 (NODC Accession 9900212)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Ocean water temperature data were collected from data loggers attached to the HALE-ALOHA Moorings in the North Pacific Ocean from 24 April 1998 to 03 May 1999. Data...

  14. 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...

  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. 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

  17. 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.

  18. 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...

  19. 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...

  20. AFSC/ABL: Ugashik sockeye salmon scale time series

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — A time series of scale samples (1956 b?? 2002) collected from adult sockeye salmon returning to Ugashik River were retrieved from the Alaska Department of Fish and...

  1. AFSC/ABL: Naknek sockeye salmon scale time series

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — A time series of scale samples (1956 2002) collected from adult sockeye salmon returning to Naknek River were retrieved from the Alaska Department of Fish and Game....

  2. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING TAO140W and MOORING_TAO140W_0N in the North Pacific Ocean and South Pacific Ocean from 2004-05-23 to 2013-07-02 (NODC Accession 0100077)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0100077 includes chemical, meteorological, physical and time series data collected from MOORING TAO140W and MOORING_TAO140W_0N in the North Pacific...

  3. Partial pressure (or fugacity) of carbon dioxide, temperature, salinity and other variables collected from Surface underway and time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from the NATHANIEL B. PALMER and ROGER REVELLE in the South Atlantic Ocean and South Pacific Ocean from 1994-11-01 to 1998-04-30 (NODC Accession 0112324)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0112324 includes Surface underway, chemical, meteorological, physical and time series data collected from NATHANIEL B. PALMER and ROGER REVELLE in the...

  4. 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 ...

  5. Metagenomics meets time series analysis: unraveling microbial community dynamics

    NARCIS (Netherlands)

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

    2015-01-01

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

  6. NODC Standard Product: International ocean atlas Volume 7 - 36 year time series (1963-1998) of zooplankton, temperature and salinity in the White Sea (NCEI Accession 0099242)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The present study is based on marine physical and biological observations since 1961. The data on zooplankton has been collected since 1963 in the vicinity of the...

  7. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Bell M. Shimada in the Coastal Waters of SE Alaska and North Pacific Ocean from 2016-03-16 to 2016-03-20 (NCEI Accession 0151240)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0151240 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  8. NOAA Laboratory for Satellite Altimetry Sea Level Rise Products: Global and regional sea level time series and trend maps for the major ocean basins and marginal seas, based on measurements from satellite radar altimeters, from 1992-12-17 to 2017-08-11 (NCEI Accession 0125535)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This archival package contains global and regional mean sea level time series and trend maps calculated on a continual basis since December 1992 by Laboratory for...

  9. Partial pressure (or fugacity) of carbon dioxide, dissolved inorganic carbon, temperature, salinity and other variables collected from discrete sample, profile and time series profile observations using CTD, bottle and other instruments from ARNI FRIDRIKSSON and BJARNI SAEMUNDSSON in the North Atlantic Ocean from 1991-08-08 to 2006-02-02 (NODC Accession 0100114)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0100114 includes chemical, discrete sample, physical, profile and time series profile data collected from ARNI FRIDRIKSSON and BJARNI SAEMUNDSSON in...

  10. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Coastal Waters of Southeast Alaska and British Columbia, Gulf of Alaska and North Pacific Ocean from 2016-10-13 to 2016-10-19 (NCEI Accession 0165091)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0165091 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  11. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Coastal Waters of SE Alaska, Gulf of Alaska and North Pacific Ocean from 2013-04-04 to 2013-04-15 (NODC Accession 0124185)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0124185 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  12. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Coastal Waters of SE Alaska, Gulf of Alaska and North Pacific Ocean from 2016-01-30 to 2016-02-09 (NCEI Accession 0150729)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0150729 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  13. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Coastal Waters of SE Alaska, Gulf of Alaska and North Pacific Ocean from 2016-02-03 to 2016-02-09 (NCEI Accession 0150730)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0150730 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  14. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Bell M. Shimada in the Coastal Waters of Southeast Alaska and British Columbia and North Pacific Ocean from 2015-09-16 to 2015-10-13 (NCEI Accession 0135733)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0135733 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  15. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Bell M. Shimada in the Coastal Waters of Southeast Alaska and British Columbia and North Pacific Ocean from 2016-06-30 to 2016-08-14 (NCEI Accession 0162237)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0162237 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  16. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Bell M. Shimada in the Coastal Waters of SE Alaska and North Pacific Ocean from 2014-06-24 to 2014-09-14 (NCEI Accession 0121964)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0121964 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  17. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Bell M. Shimada in the Coastal Waters of SE Alaska and North Pacific Ocean from 2015-02-11 to 2015-03-03 (NODC Accession 0126536)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0126536 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  18. Underway meteorological, navigational, optical, physical, profile, time series and trawl data collected aboard NOAA Ship Bell M. Shimada in the Coastal Waters of SE Alaska and North Pacific Ocean from 2013-06-06 to 2013-09-09 (NCEI Accession 0116843)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0116843 contains raw underway meteorological, navigational, optical, physical, profile, time series and trawl data logged by the Scientific Computer...

  19. Dissolved inorganic carbon, total alkalinity, temperature, salinity and other variables collected via time series monitoring from BOSEI MARU NO. 2, HAKUREI MARU and others in the North Pacific Ocean from 1992-06-23 to 2008-10-31 (NODC Accession 0100219)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0100219 includes chemical, discrete bottle, physical and time series data collected from BOSEI MARU NO. 2, HAKUREI MARU, Hakuho Maru, Hokusei Maru,...

  20. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Coastal Waters of Southeast Alaska and British Columbia, Gulf of Alaska and North Pacific Ocean from 2017-01-26 to 2017-02-01 (NCEI Accession 0165022)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0165022 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  1. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Coastal Waters of SE Alaska, Gulf of Alaska and North Pacific Ocean from 2013-09-24 to 2013-11-03 (NODC Accession 0124206)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0124206 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  2. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Coastal Waters of SE Alaska, Gulf of Alaska and North Pacific Ocean from 2015-01-24 to 2015-01-30 (NODC Accession 0126876)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0126876 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  3. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Ronald H. Brown in the Coastal Waters of SE Alaska, Gulf of Alaska and North Pacific Ocean from 2015-07-14 to 2015-08-03 (NCEI Accession 0130369)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0130369 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  4. Underway meteorological, navigational, physical and time series data collected aboard NOAA Ship Ronald H. Brown in the Coastal Waters of Southeast Alaska and British Columbia, Columbia River estuary - Washington/Oregon and North Pacific Ocean from 2016-05-05 to 2016-06-07 (NCEI Accession 0155887)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0155887 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...

  5. Underway meteorological, navigational, optical, physical and time series data collected aboard NOAA Ship Oscar Dyson in the Coastal Waters of SE Alaska, Gulf of Alaska and North Pacific Ocean from 2013-01-28 to 2013-02-03 (NODC Accession 0124297)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0124297 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...

  6. Partial pressure (or fugacity) of carbon dioxide, pH (total scale), salinity and other variables collected from time series observations from Mooring_GraysRf_81W_31N in the Gray's Reef National Marine Sanctuary, North Atlantic Ocean from 2006-07-18 to 2015-10-15 (NODC Accession 0109904)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0109904 includes chemical, meteorological, physical and time series data collected from MOORING GRAYSRF_81W_31N and Mooring_GraysRf_81W_31N in the...

  7. Underway meteorological, navigational, optical, physical, profile and time series data collected aboard NOAA Ship Bell M. Shimada in the Coastal Waters of SE Alaska and North Pacific Ocean from 2015-06-20 to 2015-09-10 (NCEI Accession 0131258)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0131258 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...

  8. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from TAO165E8S and TAO165E_8S in the South Pacific Ocean from 2009-06-22 to 2011-11-15 (NODC Accession 0117073)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0117073 includes chemical, meteorological, physical and time series data collected from TAO165E8S and TAO165E_8S in the South Pacific Ocean from...

  9. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING TAO170W and TAO170W0N in the South Pacific Ocean from 2005-07-04 to 2011-02-04 (NODC Accession 0100078)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0100078 includes chemical, meteorological, physical and time series data collected from MOORING TAO170W and TAO170W0N in the South Pacific Ocean from...

  10. Partial pressure (or fugacity) of carbon dioxide, pH, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING_KANEOHE_158W_21N in the North Pacific Ocean from 2011-09-30 to 2015-05-06 (NCEI Accession 0157297)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0157297 includes chemical, meteorological, physical and time series data collected from MOORING_KANEOHE_158W_21N in the North Pacific Ocean from...

  11. Partial pressure (or fugacity) of carbon dioxide, pH, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING_CHUUKK1_152E_7N in the North Pacific Ocean from 2011-11-18 to 2015-11-28 (NCEI Accession 0157443)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0157443 includes chemical, meteorological, physical and time series data collected from MOORING_CHUUKK1_152E_7N in the North Pacific Ocean from...

  12. Partial pressure (or fugacity) of carbon dioxide, temperature, salinity and other variables collected from Surface underway and time series observations using Carbon dioxide (CO2) gas analyzer, Shower head chamber equilibrator for autonomous carbon dioxide (CO2) measurement and other instruments from GULF CHALLENGER in the North Atlantic Ocean and Stellwagen Bank National Marine Sanctuary from 2004-05-10 to 2016-12-07 (NODC Accession 0073808)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0073808 includes Surface underway, chemical, meteorological, physical and time series data collected from GULF CHALLENGER in the North Atlantic Ocean...

  13. 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

  14. 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.

  15. The Blue Planet: Seas & Oceans. Young Discovery Library Series.

    Science.gov (United States)

    de Beauregard, Diane Costa

    This book is written for children ages 5 through 10. Part of a series designed to develop their curiosity, facinate them and educate them, this volume explores the physical and environmental characteristics of the world's oceans. Topics are: (1) human exploration; (2) the food chain; (3) coral reefs; (4) currents and tides; (5) waves; (6)…

  16. 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...

  17. 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…

  18. Nineteen-year time-series sediment trap study of Coccolithus pelagicus and Emiliania huxleyi (calcareous nannoplankton) fluxes in the Bering Sea and subarctic Pacific Ocean

    Science.gov (United States)

    Tsutsui, Hideto; Takahashi, Kozo; Asahi, Hirofumi; Jordan, Richard W.; Nishida, Shiro; Nishiwaki, Niichi; Yamamoto, Sumito

    2016-03-01

    Coccolithophore fluxes at two sediment trap stations, Station AB in the Bering Sea and Station SA in the subarctic Pacific Ocean, were studied over a nineteen-year (August 1990-July 2009) interval. Two major species, Coccolithus pelagicus and Emiliania huxleyi, occur at both stations, with Gephyrocapsa oceanica, Umbilicosphaera sibogae, Braarudosphaera bigelowii, and Syracosphaera spp. as minor components. The mean coccolithophore fluxes at Stations AB and SA increased from 28.9×106 m2 d-1 and 61.9×106 m2 d-1 in 1990-1999 to 54.4×106 m2 d-1 and 130.2×106 m2 d-1 in 2002-2009, respectively. Furthermore, in late 1999 to early 2000, there was a significant shift in the most dominant species from E. huxleyi to C. pelagicus. High abundances of E. huxleyi correspond to the positive mode of the Pacific Decadal Oscillation (PDO), while those of C. pelagicus respond to the PDO negative mode and are related to water temperature changes at huxleyi. At both stations the mean seawater temperature in the top 45 m from August to October increased ca. 1 °C with linear recurrence from 1990 to 2008. The coccosphere fluxes after Year 2000 at Stations AB and SA, and the shift in species dominance, may have been influenced by this warming.

  19. 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

  20. 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...

  1. 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...

  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. 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 ...

  4. 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....

  5. 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

  6. 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

  7. 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.

  8. 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.

  9. 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

  10. Chemical and physical data from Niskin bottles from the World Ocean Circulation Experiment and Joint Global Ocean Flux Study Hawaii Ocean Time-series (HOT) database during 1988-1998 in the North Pacific Ocean 100 miles north of Oahu, Hawaii (NODC Accession 9900208)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited...

  11. Partial pressure (or fugacity) of carbon dioxide, pH (on total scale), salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING BOBOA_90E_15N deployments 1 and 2 in the Bay of Bengal, Indian Ocean from 2013-11-24 to 2015-06-19 (NCEI Accession 0162473)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0162473 includes chemical, meteorological, physical time series data collected from BOBOA_90E_15N deployments 1 and 2 in the Bay of Bengal, Indian...

  12. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING PAPA_145W_50N and MOORINGS_PAPA_145W_50N in the North Pacific Ocean from 2010-06-15 to 2014-06-19 (NODC Accession 0100074)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0100074 includes chemical, meteorological, physical and time series data collected from MOORING PAPA_145W_50N and MOORINGS_PAPA_145W_50N in the North...

  13. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING TAO125W and MOORING_TAO125W_0 in the South Pacific Ocean from 2004-05-08 to 2012-05-25 (NODC Accession 0100076)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0100076 includes chemical, meteorological, physical and time series data collected from MOORING TAO125W and MOORING_TAO125W_0 in the South Pacific...

  14. Partial pressure (or fugacity) of carbon dioxide, pH, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING KEO_145E_32N and MOORING_KEO_145E_32N in the North Pacific Ocean from 2007-09-26 to 2014-06-26 (NODC Accession 0100071)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0100071 includes chemical, meteorological, physical and time series data collected from MOORING KEO_145E_32N and MOORING_KEO_145E_32N in the North...

  15. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING_CRIMP2_158W_21N in the Kaneohe Bay and North Pacific Ocean from 2008-06-11 to 2015-05-13 (NCEI Accession 0157415)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0157415 includes chemical, meteorological, physical and time series data collected from MOORING_CRIMP2_158W_21N in the Kaneohe Bay, North Pacific...

  16. Partial pressure (or fugacity) of carbon dioxide, pH, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING_CHEECA_80W_25N in the Coastal Waters of Florida, Florida Keys National Marine Sanctuary and North Atlantic Ocean from 2011-12-07 to 2015-03-22 (NCEI Accession 0157417)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0157417 includes chemical, meteorological, physical and time series data collected from MOORING_CHEECA_80W_25N in the Coastal Waters of Florida,...

  17. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORINGS_CRESCENT_64W_32N and MOORING_CRESCENT_64W_32N in the North Atlantic Ocean from 2010-11-27 to 2015-12-31 (NODC Accession 0117059)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0117059 includes chemical, meteorological, physical and time series data collected from MOORINGS_CRESCENT_64W_32N and MOORING_CRESCENT_64W_32N in the...

  18. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING_TAO110W and MOORING_TAO110W0N in the North Pacific Ocean from 2009-09-19 to 2013-09-25 (NODC Accession 0112885)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0112885 includes chemical, meteorological, physical and time series data collected from MOORING_TAO110W and MOORING_TAO110W0N in the North Pacific...

  19. 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.

  20. Incorporating Satellite Time-Series Data into Modeling

    Science.gov (United States)

    Gregg, Watson

    2008-01-01

    In situ time series observations have provided a multi-decadal view of long-term changes in ocean biology. These observations are sufficiently reliable to enable discernment of even relatively small changes, and provide continuous information on a host of variables. Their key drawback is their limited domain. Satellite observations from ocean color sensors do not suffer the drawback of domain, and simultaneously view the global oceans. This attribute lends credence to their use in global and regional model validation and data assimilation. We focus on these applications using the NASA Ocean Biogeochemical Model. The enhancement of the satellite data using data assimilation is featured and the limitation of tongterm satellite data sets is also discussed.

  1. 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.

  2. 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...

  3. 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.

  4. 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...

  5. 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 ...

  6. 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…

  7. Time, space, & the ocean wanders

    OpenAIRE

    Gatt, Marie Claire

    2016-01-01

    Malta is one of the bird migration hotspots in the Mediterranean. As an archipelago, the Maltese Islands have been a hotspot for seabird nesting since time immemorial. Marie Claire Gatt talks about her research and a major EU project determining sea bird colony location and which areas need to be saved.

  8. 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...

  9. 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.

  10. 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.

  11. 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...

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. Modeling Time Series Data for Supervised Learning

    Science.gov (United States)

    Baydogan, Mustafa Gokce

    2012-01-01

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

  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. 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.

  20. 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.

  1. 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.

  2. 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.

  3. The oceanic chemistry of the U- and Th-series nuclides

    International Nuclear Information System (INIS)

    Cochran, J.K.

    1982-01-01

    The subject is discussed under the headings: input and removal of U- and Th-series nuclides in the oceans; uranium (input to the oceans; in the coastal ocean; in the open ocean; in sediment pore water; removal from the oceans; sources and sinks of 234 U in the oceans); thorium (scavenging in the deep sea; 230 Th and 231 Pa balance; removal from the coastal and surface ocean); Ra-226 and Ra-228; radon (in surface waters; near bottom 222 Rn as a tracer for vertical mixing); lead-210; polonium-210. (U.K.)

  4. 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

  5. Nonparametric factor analysis of time series

    OpenAIRE

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

    1998-01-01

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

  6. Applied time series analysis and innovative computing

    CERN Document Server

    Ao, Sio-Iong

    2010-01-01

    This text is a systematic, state-of-the-art introduction to the use of innovative computing paradigms as an investigative tool for applications in time series analysis. It includes frontier case studies based on recent research.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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...

  15. 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.

  16. 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.

  17. Turbulencelike Behavior of Seismic Time Series

    International Nuclear Information System (INIS)

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

    2009-01-01

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

  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. 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.

  20. Forecasting autoregressive time series under changing persistence

    DEFF Research Database (Denmark)

    Kruse, Robinson

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

  1. 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

  2. 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.

  3. 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.

  4. 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...

  5. Complex dynamic in ecological time series

    Science.gov (United States)

    Peter Turchin; Andrew D. Taylor

    1992-01-01

    Although the possibility of complex dynamical behaviors-limit cycles, quasiperiodic oscillations, and aperiodic chaos-has been recognized theoretically, most ecologists are skeptical of their importance in nature. In this paper we develop a methodology for reconstructing endogenous (or deterministic) dynamics from ecological time series. Our method consists of fitting...

  6. Inferring interdependencies from short time series

    Indian Academy of Sciences (India)

    Abstract. Complex networks provide an invaluable framework for the study of interlinked dynamical systems. In many cases, such networks are constructed from observed time series by first estimating the ...... does not quantify causal relations (unlike IOTA, or .... Africa_map_regions.svg, which is under public domain.

  7. On modeling panels of time series

    NARCIS (Netherlands)

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

    2002-01-01

    textabstractThis paper reviews research issues in modeling panels of time series. Examples of this type of data are annually observed macroeconomic indicators for all countries in the world, daily returns on the individual stocks listed in the S&P500, and the sales records of all items in a

  8. 25 years of time series forecasting

    NARCIS (Netherlands)

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

    2006-01-01

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

  9. Nonlinear Time Series Analysis via Neural Networks

    Science.gov (United States)

    Volná, Eva; Janošek, Michal; Kocian, Václav; Kotyrba, Martin

    This article deals with a time series analysis based on neural networks in order to make an effective forex market [Moore and Roche, J. Int. Econ. 58, 387-411 (2002)] pattern recognition. Our goal is to find and recognize important patterns which repeatedly appear in the market history to adapt our trading system behaviour based on them.

  10. Markov Trends in Macroeconomic Time Series

    NARCIS (Netherlands)

    R. Paap (Richard)

    1997-01-01

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

  11. Modeling vector nonlinear time series using POLYMARS

    NARCIS (Netherlands)

    de Gooijer, J.G.; Ray, B.K.

    2003-01-01

    A modified multivariate adaptive regression splines method for modeling vector nonlinear time series is investigated. The method results in models that can capture certain types of vector self-exciting threshold autoregressive behavior, as well as provide good predictions for more general vector

  12. Modeling seasonality in bimonthly time series

    NARCIS (Netherlands)

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

    1992-01-01

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

  13. Time Series Modelling using Proc Varmax

    DEFF Research Database (Denmark)

    Milhøj, Anders

    2007-01-01

    In this paper it will be demonstrated how various time series problems could be met using Proc Varmax. The procedure is rather new and hence new features like cointegration, testing for Granger causality are included, but it also means that more traditional ARIMA modelling as outlined by Box...

  14. On clustering fMRI time series

    DEFF Research Database (Denmark)

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

    1999-01-01

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

  15. Robust Control Charts for Time Series Data

    NARCIS (Netherlands)

    Croux, C.; Gelper, S.; Mahieu, K.

    2010-01-01

    This article presents a control chart for time series data, based on the one-step- ahead forecast errors of the Holt-Winters forecasting method. We use robust techniques to prevent that outliers affect the estimation of the control limits of the chart. Moreover, robustness is important to maintain

  16. Optimal transformations for categorical autoregressive time series

    NARCIS (Netherlands)

    Buuren, S. van

    1996-01-01

    This paper describes a method for finding optimal transformations for analyzing time series by autoregressive models. 'Optimal' implies that the agreement between the autoregressive model and the transformed data is maximal. Such transformations help 1) to increase the model fit, and 2) to analyze

  17. Lecture notes for Advanced Time Series Analysis

    DEFF Research Database (Denmark)

    Madsen, Henrik; Holst, Jan

    1997-01-01

    A first version of this notes was used at the lectures in Grenoble, and they are now extended and improved (together with Jan Holst), and used in Ph.D. courses on Advanced Time Series Analysis at IMM and at the Department of Mathematical Statistics, University of Lund, 1994, 1997, ...

  18. Forecasting with periodic autoregressive time series models

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)

    1999-01-01

    textabstractThis paper is concerned with forecasting univariate seasonal time series data using periodic autoregressive models. We show how one should account for unit roots and deterministic terms when generating out-of-sample forecasts. We illustrate the models for various quarterly UK consumption

  19. 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.

  20. The Statistical Analysis of Time Series

    CERN Document Server

    Anderson, T W

    2011-01-01

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

  1. 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

  2. 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").

  3. Inverse statistical approach in heartbeat time series

    International Nuclear Information System (INIS)

    Ebadi, H; Shirazi, A H; Mani, Ali R; Jafari, G R

    2011-01-01

    We present an investigation on heart cycle time series, using inverse statistical analysis, a concept borrowed from studying turbulence. Using this approach, we studied the distribution of the exit times needed to achieve a predefined level of heart rate alteration. Such analysis uncovers the most likely waiting time needed to reach a certain change in the rate of heart beat. This analysis showed a significant difference between the raw data and shuffled data, when the heart rate accelerates or decelerates to a rare event. We also report that inverse statistical analysis can distinguish between the electrocardiograms taken from healthy volunteers and patients with heart failure

  4. Visibility graphlet approach to chaotic time series

    Energy Technology Data Exchange (ETDEWEB)

    Mutua, Stephen [Business School, University of Shanghai for Science and Technology, Shanghai 200093 (China); Computer Science Department, Masinde Muliro University of Science and Technology, P.O. Box 190-50100, Kakamega (Kenya); Gu, Changgui, E-mail: gu-changgui@163.com, E-mail: hjyang@ustc.edu.cn; Yang, Huijie, E-mail: gu-changgui@163.com, E-mail: hjyang@ustc.edu.cn [Business School, University of Shanghai for Science and Technology, Shanghai 200093 (China)

    2016-05-15

    Many novel methods have been proposed for mapping time series into complex networks. Although some dynamical behaviors can be effectively captured by existing approaches, the preservation and tracking of the temporal behaviors of a chaotic system remains an open problem. In this work, we extended the visibility graphlet approach to investigate both discrete and continuous chaotic time series. We applied visibility graphlets to capture the reconstructed local states, so that each is treated as a node and tracked downstream to create a temporal chain link. Our empirical findings show that the approach accurately captures the dynamical properties of chaotic systems. Networks constructed from periodic dynamic phases all converge to regular networks and to unique network structures for each model in the chaotic zones. Furthermore, our results show that the characterization of chaotic and non-chaotic zones in the Lorenz system corresponds to the maximal Lyapunov exponent, thus providing a simple and straightforward way to analyze chaotic systems.

  5. Time-Series Analysis: A Cautionary Tale

    Science.gov (United States)

    Damadeo, Robert

    2015-01-01

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

  6. Time Series Analysis Using Geometric Template Matching.

    Science.gov (United States)

    Frank, Jordan; Mannor, Shie; Pineau, Joelle; Precup, Doina

    2013-03-01

    We present a novel framework for analyzing univariate time series data. At the heart of the approach is a versatile algorithm for measuring the similarity of two segments of time series called geometric template matching (GeTeM). First, we use GeTeM to compute a similarity measure for clustering and nearest-neighbor classification. Next, we present a semi-supervised learning algorithm that uses the similarity measure with hierarchical clustering in order to improve classification performance when unlabeled training data are available. Finally, we present a boosting framework called TDEBOOST, which uses an ensemble of GeTeM classifiers. TDEBOOST augments the traditional boosting approach with an additional step in which the features used as inputs to the classifier are adapted at each step to improve the training error. We empirically evaluate the proposed approaches on several datasets, such as accelerometer data collected from wearable sensors and ECG data.

  7. Forecasting with nonlinear time series models

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl; Teräsvirta, Timo

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

  8. Nonlinear time series analysis with R

    CERN Document Server

    Huffaker, Ray; Rosa, Rodolfo

    2017-01-01

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

  9. Reconstruction of tritium time series in precipitation

    International Nuclear Information System (INIS)

    Celle-Jeanton, H.; Gourcy, L.; Aggarwal, P.K.

    2002-01-01

    Tritium is commonly used in groundwaters studies to calculate the recharge rate and to identify the presence of a modern recharge. The knowledge of 3 H precipitation time series is then very important for the study of groundwater recharge. Rozanski and Araguas provided good information on precipitation tritium content in 180 stations of the GNIP network to the end of 1987, but it shows some lacks of measurements either within one chronicle or within one region (the Southern hemisphere for instance). Therefore, it seems to be essential to find a method to recalculate data for a region where no measurement is available.To solve this problem, we propose another method which is based on triangulation. It needs the knowledge of 3 H time series of 3 stations surrounding geographically the 4-th station for which tritium input curve has to be reconstructed

  10. Time Series Forecasting with Missing Values

    OpenAIRE

    Shin-Fu Wu; Chia-Yung Chang; Shie-Jue Lee

    2015-01-01

    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, o...

  11. Time series analysis of barometric pressure data

    International Nuclear Information System (INIS)

    La Rocca, Paola; Riggi, Francesco; Riggi, Daniele

    2010-01-01

    Time series of atmospheric pressure data, collected over a period of several years, were analysed to provide undergraduate students with educational examples of application of simple statistical methods of analysis. In addition to basic methods for the analysis of periodicities, a comparison of two forecast models, one based on autoregression algorithms, and the other making use of an artificial neural network, was made. Results show that the application of artificial neural networks may give slightly better results compared to traditional methods.

  12. Causal strength induction from time series data.

    Science.gov (United States)

    Soo, Kevin W; Rottman, Benjamin M

    2018-04-01

    One challenge when inferring the strength of cause-effect relations from time series data is that the cause and/or effect can exhibit temporal trends. If temporal trends are not accounted for, a learner could infer that a causal relation exists when it does not, or even infer that there is a positive causal relation when the relation is negative, or vice versa. We propose that learners use a simple heuristic to control for temporal trends-that they focus not on the states of the cause and effect at a given instant, but on how the cause and effect change from one observation to the next, which we call transitions. Six experiments were conducted to understand how people infer causal strength from time series data. We found that participants indeed use transitions in addition to states, which helps them to reach more accurate causal judgments (Experiments 1A and 1B). Participants use transitions more when the stimuli are presented in a naturalistic visual format than a numerical format (Experiment 2), and the effect of transitions is not driven by primacy or recency effects (Experiment 3). Finally, we found that participants primarily use the direction in which variables change rather than the magnitude of the change for estimating causal strength (Experiments 4 and 5). Collectively, these studies provide evidence that people often use a simple yet effective heuristic for inferring causal strength from time series data. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  13. Interpretable Categorization of Heterogeneous Time Series Data

    Science.gov (United States)

    Lee, Ritchie; Kochenderfer, Mykel J.; Mengshoel, Ole J.; Silbermann, Joshua

    2017-01-01

    We analyze data from simulated aircraft encounters to validate and inform the development of a prototype aircraft collision avoidance system. The high-dimensional and heterogeneous time series dataset is analyzed to discover properties of near mid-air collisions (NMACs) and categorize the NMAC encounters. Domain experts use these properties to better organize and understand NMAC occurrences. Existing solutions either are not capable of handling high-dimensional and heterogeneous time series datasets or do not provide explanations that are interpretable by a domain expert. The latter is critical to the acceptance and deployment of safety-critical systems. To address this gap, we propose grammar-based decision trees along with a learning algorithm. Our approach extends decision trees with a grammar framework for classifying heterogeneous time series data. A context-free grammar is used to derive decision expressions that are interpretable, application-specific, and support heterogeneous data types. In addition to classification, we show how grammar-based decision trees can also be used for categorization, which is a combination of clustering and generating interpretable explanations for each cluster. We apply grammar-based decision trees to a simulated aircraft encounter dataset and evaluate the performance of four variants of our learning algorithm. The best algorithm is used to analyze and categorize near mid-air collisions in the aircraft encounter dataset. We describe each discovered category in detail and discuss its relevance to aircraft collision avoidance.

  14. 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

  15. Timing calibration and spectral cleaning of LOFAR time series data

    NARCIS (Netherlands)

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

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

  16. Outlier Detection in Structural Time Series Models

    DEFF Research Database (Denmark)

    Marczak, Martyna; Proietti, Tommaso

    investigate via Monte Carlo simulations how this approach performs for detecting additive outliers and level shifts in the analysis of nonstationary seasonal time series. The reference model is the basic structural model, featuring a local linear trend, possibly integrated of order two, stochastic seasonality......Structural change affects the estimation of economic signals, like the underlying growth rate or the seasonally adjusted series. An important issue, which has attracted a great deal of attention also in the seasonal adjustment literature, is its detection by an expert procedure. The general......–to–specific approach to the detection of structural change, currently implemented in Autometrics via indicator saturation, has proven to be both practical and effective in the context of stationary dynamic regression models and unit–root autoregressions. By focusing on impulse– and step–indicator saturation, we...

  17. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING CCE1_122W_33N and MOORING_CCE1_122W_33N in the North Pacific Ocean from 2008-11-11 to 2014-10-26 (NCEI Accession 0144245)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0144245 includes chemical, meteorological, physical and time series data collected from MOORING CCE1_122W_33N and MOORING_CCE1_122W_33N in the North...

  18. Partial pressure (or fugacity) of carbon dioxide, pH, salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORING CCE2_121W_34N and MOORING_CCE2_121W_34N in the North Pacific Ocean from 2010-01-17 to 2015-04-30 (NODC Accession 0084099)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0084099 includes chemical, meteorological, physical and time series data collected from MOORING CCE2_121W_34N and MOORING_CCE2_121W_34N in the North...

  19. Analysis of JET ELMy time series

    International Nuclear Information System (INIS)

    Zvejnieks, G.; Kuzovkov, V.N.

    2005-01-01

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

  20. Thermosalinograph data of the Hawaii Ocean Time-series (HOT) program in the North Pacific 100 miles north of Oahu, Hawaii for cruises HOT155 - 176 during 2004 - 2005 (NODC Accession 0011142)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited...

  1. Niskin bottle data of the Hawaii Ocean Time-series (HOT) program in the North Pacific 100 miles north of Oahu, Hawaii for cruises HOT228-238 during 2011 (NODC Accession 0101146)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited...

  2. Niskin bottle data of the Hawaii Ocean Time-series (HOT) program in the North Pacific 100 miles north of Oahu, Hawaii for cruises HOT249-258 during 2013 (NODC Accession 0125579)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited...

  3. Niskin Bottle Data of the Hawaii Ocean Time-series (HOT) program in the North Pacific 100 Miles North of Oahu, Hawaii for Cruises HOT122-154 during 2001-2003 (NODC Accession 0001707)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited...

  4. Niskin bottle data of the Hawaii Ocean Time-series (HOT) program in the North Pacific, 100 miles north of Oahu, Hawaii, for cruises HOT155-176 during 2004 - 2005 (NODC Accession 0010624)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited...

  5. Niskin Bottle Data of the Hawaii Ocean Time-series (HOT) program in the North Pacific 100 Miles North of Oahu, Hawaii for Cruises HOT218-227 during 2010 (NODC Accession 0087596)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited...

  6. Hydrographic data from the Hawaii Ocean Time-series (HOT) program in the North Pacific, 100 miles north of Oahu, Hawaii for cruises HOT 101-121 during 1999-2000 (NODC Accession 0000639)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited...

  7. Thermosalinograph data of the Hawaii Ocean Time-series (HOT) program in the North Pacific, 100 Miles North of Oahu, Hawaii for cruises HOT208-217 during 2009 (NODC Accession 0069501)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited...

  8. Water Column Chemical Data of the Hawaii Ocean Time-series (HOT) program in the North Pacific 100 Miles North of Oahu, Hawaii for Cruises HOT199-227 during 2008-2010 (NODC Accession 0088839)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited...

  9. CTD data of the Hawaii Ocean Time-series (HOT) program in the North Pacific 100 Miles North of Oahu, Hawaii for Cruises HOT199-206 during 2008 (NODC Accession 0059842)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited...

  10. CTD Data of the Hawaii Ocean Time-series (HOT) program in the North Pacific 100 Miles North of Oahu, Hawaii for Cruises HOT122-154 during 2001-2003 (NODC Accession 0001704)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited...

  11. CTD data of the Hawaii Ocean Time-series (HOT) program in the North Pacific 100 miles north of Oahu, Hawaii for cruises HOT177-188 during 2006 (NODC Accession 0042029)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited...

  12. CTD data of the Hawaii Ocean Time-series (HOT) program in the North Pacific 100 miles North of Oahu, Hawaii for cruises HOT155-176 during 2004 - 2005 (NODC Accession 0010740)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited...

  13. CTD data of the Hawaii Ocean Time-series (HOT) program in the North Pacific 100 miles north of Oahu, Hawaii for cruises HOT239-248 during 2012 (NODC Accession 0119895)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited...

  14. CTD data of the Hawaii Ocean Time-series (HOT) Program in the North Pacific 100 miles north of Oahu, Hawaii for Cruises HOT 101-121 during 1999 - 2000 (NODC Accession 0000640)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited...

  15. CTD Data of the Hawaii Ocean Time-series (HOT) program in the North Pacific 100 Miles North of Oahu, Hawaii for Cruises HOT189-198 during 2007 (NODC Accession 0048725)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited...

  16. CTD Data of the Hawaii Ocean Time-series (HOT) program in the North Pacific 100 Miles North of Oahu, Hawaii for Cruises HOT218-227 during 2010) (NODC Accession 0087584)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited...

  17. CTD data of the Hawaii Ocean Time-series (HOT) program in the North Pacific 100 miles north of Oahu, Hawaii for cruises HOT249-258 during 2013 (NODC Accession 0125647)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. The program began in 1988....

  18. CTD data of the Hawaii Ocean Time-series (HOT) program in the North Pacific 100 miles north of Oahu, Hawaii for cruises HOT228-237 during 2011 (NODC Accession 0101727)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited...

  19. Ocean Current Velocity Moored Time-Series Records, collected from moored Acoustic Doppler Current Profilers (ADCP) during 2011 near Grammanik Bank SPAG and Frenchcap Cay, USVI (NODC Accession 0088064)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Nortek 600kHz Aquadopp acoustic current profilers were deployed between April 2011 and September 2011 on shallow water moorings located on the coastal shelf south of...

  20. Niskin bottle data of the Hawaii Ocean Time-series (HOT) program in the North Pacific 100 miles north of Oahu, Hawaii for cruises HOT239-248 during 2012 (NCEI Accession 0119430)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited...

  1. Biogeochemical and microbiological variables measured by CTD and Niskin bottles from the Hermano Gines in the Caribbean Sea for the CARIACO Ocean Time-Series Program from 1995-11-13 to 2015-11-14 (NCEI Accession 0164194)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The goal of this project was to examine the interrelationship between microbial activity and water column geochemistry in the world’s largest, truly marine anoxic...

  2. Thermosalinograph data of the Hawaii Ocean Time-series (HOT) Program in the North Pacific 100 miles north of Oahu, Hawaii for cruises HOT101-121 during 1999-2000 (NODC Accession 0000641)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited...

  3. CTD data of the Hawaii Ocean Time-series (HOT) program in the North Pacific 100 miles north of Oahu, Hawaii for cruises HOT208-217 during 2009 (NODC Accession 0068957)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited...

  4. Niskin bottle data of the Hawaii Ocean Time-series (HOT) program in the North Pacific 100 miles north of Oahu, Hawaii for cruises HOT208-217 during 2009 (NODC Accession 0069177)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. Two stations are visited...

  5. Thermosalinograph data of the Hawaii Ocean Time-series (HOT) program in the North Pacific 100 miles north of Oahu, Hawaii for cruises HOT259-268 during 2014 (NCEI Accession 0140225)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The HOT program makes repeated observations of the physics, biology and chemistry at a site approximately 100 km north of Oahu, Hawaii. The program began in 1988....

  6. Fourier analysis of time series an introduction

    CERN Document Server

    Bloomfield, Peter

    2000-01-01

    A new, revised edition of a yet unrivaled work on frequency domain analysis Long recognized for his unique focus on frequency domain methods for the analysis of time series data as well as for his applied, easy-to-understand approach, Peter Bloomfield brings his well-known 1976 work thoroughly up to date. With a minimum of mathematics and an engaging, highly rewarding style, Bloomfield provides in-depth discussions of harmonic regression, harmonic analysis, complex demodulation, and spectrum analysis. All methods are clearly illustrated using examples of specific data sets, while ample

  7. Estimating High-Dimensional Time Series Models

    DEFF Research Database (Denmark)

    Medeiros, Marcelo C.; Mendes, Eduardo F.

    We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-dimensional, linear time-series models. We assume both the number of covariates in the model and candidate variables can increase with the number of observations and the number of candidate variables is, possibly......, larger than the number of observations. We show the adaLASSO consistently chooses the relevant variables as the number of observations increases (model selection consistency), and has the oracle property, even when the errors are non-Gaussian and conditionally heteroskedastic. A simulation study shows...

  8. Inferring causality from noisy time series data

    DEFF Research Database (Denmark)

    Mønster, Dan; Fusaroli, Riccardo; Tylén, Kristian

    2016-01-01

    Convergent Cross-Mapping (CCM) has shown high potential to perform causal inference in the absence of models. We assess the strengths and weaknesses of the method by varying coupling strength and noise levels in coupled logistic maps. We find that CCM fails to infer accurate coupling strength...... and even causality direction in synchronized time-series and in the presence of intermediate coupling. We find that the presence of noise deterministically reduces the level of cross-mapping fidelity, while the convergence rate exhibits higher levels of robustness. Finally, we propose that controlled noise...

  9. Useful Pattern Mining on Time Series

    DEFF Research Database (Denmark)

    Goumatianos, Nikitas; Christou, Ioannis T; Lindgren, Peter

    2013-01-01

    We present the architecture of a “useful pattern” mining system that is capable of detecting thousands of different candlestick sequence patterns at the tick or any higher granularity levels. The system architecture is highly distributed and performs most of its highly compute-intensive aggregation...... calculations as complex but efficient distributed SQL queries on the relational databases that store the time-series. We present initial results from mining all frequent candlestick sequences with the characteristic property that when they occur then, with an average at least 60% probability, they signal a 2...

  10. 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 ...

  11. Time series analysis of temporal networks

    Science.gov (United States)

    Sikdar, Sandipan; Ganguly, Niloy; Mukherjee, Animesh

    2016-01-01

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

  12. Anomaly on Superspace of Time Series Data

    Science.gov (United States)

    Capozziello, Salvatore; Pincak, Richard; Kanjamapornkul, Kabin

    2017-11-01

    We apply the G-theory and anomaly of ghost and antighost fields in the theory of supersymmetry to study a superspace over time series data for the detection of hidden general supply and demand equilibrium in the financial market. We provide proof of the existence of a general equilibrium point over 14 extradimensions of the new G-theory compared with the M-theory of the 11 dimensions model of Edward Witten. We found that the process of coupling between nonequilibrium and equilibrium spinor fields of expectation ghost fields in the superspace of time series data induces an infinitely long exact sequence of cohomology from a short exact sequence of moduli state space model. If we assume that the financial market is separated into two topological spaces of supply and demand as the D-brane and anti-D-brane model, then we can use a cohomology group to compute the stability of the market as a stable point of the general equilibrium of the interaction between D-branes of the market. We obtain the result that the general equilibrium will exist if and only if the 14th Batalin-Vilkovisky cohomology group with the negative dimensions underlying 14 major hidden factors influencing the market is zero.

  13. Tool Wear Monitoring Using Time Series Analysis

    Science.gov (United States)

    Song, Dong Yeul; Ohara, Yasuhiro; Tamaki, Haruo; Suga, Masanobu

    A tool wear monitoring approach considering the nonlinear behavior of cutting mechanism caused by tool wear and/or localized chipping is proposed, and its effectiveness is verified through the cutting experiment and actual turning machining. Moreover, the variation in the surface roughness of the machined workpiece is also discussed using this approach. In this approach, the residual error between the actually measured vibration signal and the estimated signal obtained from the time series model corresponding to dynamic model of cutting is introduced as the feature of diagnosis. Consequently, it is found that the early tool wear state (i.e. flank wear under 40µm) can be monitored, and also the optimal tool exchange time and the tool wear state for actual turning machining can be judged by this change in the residual error. Moreover, the variation of surface roughness Pz in the range of 3 to 8µm can be estimated by the monitoring of the residual error.

  14. Time Series Based for Online Signature Verification

    Directory of Open Access Journals (Sweden)

    I Ketut Gede Darma Putra

    2013-11-01

    Full Text Available Signature verification system is to match the tested signature with a claimed signature. This paper proposes time series based for feature extraction method and dynamic time warping for match method. The system made by process of testing 900 signatures belong to 50 participants, 3 signatures for reference and 5 signatures from original user, simple imposters and trained imposters for signatures test. The final result system was tested with 50 participants with 3 references. This test obtained that system accuracy without imposters is 90,44897959% at threshold 44 with rejection errors (FNMR is 5,2% and acceptance errors (FMR is 4,35102%, when with imposters system accuracy is 80,1361% at threshold 27 with error rejection (FNMR is 15,6% and acceptance errors (average FMR is 4,263946%, with details as follows: acceptance errors is 0,391837%, acceptance errors simple imposters is 3,2% and acceptance errors trained imposters is 9,2%.

  15. Automated time series forecasting for biosurveillance.

    Science.gov (United States)

    Burkom, Howard S; Murphy, Sean Patrick; Shmueli, Galit

    2007-09-30

    For robust detection performance, traditional control chart monitoring for biosurveillance is based on input data free of trends, day-of-week effects, and other systematic behaviour. Time series forecasting methods may be used to remove this behaviour by subtracting forecasts from observations to form residuals for algorithmic input. We describe three forecast methods and compare their predictive accuracy on each of 16 authentic syndromic data streams. The methods are (1) a non-adaptive regression model using a long historical baseline, (2) an adaptive regression model with a shorter, sliding baseline, and (3) the Holt-Winters method for generalized exponential smoothing. Criteria for comparing the forecasts were the root-mean-square error, the median absolute per cent error (MedAPE), and the median absolute deviation. The median-based criteria showed best overall performance for the Holt-Winters method. The MedAPE measures over the 16 test series averaged 16.5, 11.6, and 9.7 for the non-adaptive regression, adaptive regression, and Holt-Winters methods, respectively. The non-adaptive regression forecasts were degraded by changes in the data behaviour in the fixed baseline period used to compute model coefficients. The mean-based criterion was less conclusive because of the effects of poor forecasts on a small number of calendar holidays. The Holt-Winters method was also most effective at removing serial autocorrelation, with most 1-day-lag autocorrelation coefficients below 0.15. The forecast methods were compared without tuning them to the behaviour of individual series. We achieved improved predictions with such tuning of the Holt-Winters method, but practical use of such improvements for routine surveillance will require reliable data classification methods.

  16. Palmprint Verification Using Time Series Method

    Directory of Open Access Journals (Sweden)

    A. A. Ketut Agung Cahyawan Wiranatha

    2013-11-01

    Full Text Available The use of biometrics as an automatic recognition system is growing rapidly in solving security problems, palmprint is one of biometric system which often used. This paper used two steps in center of mass moment method for region of interest (ROI segmentation and apply the time series method combined with block window method as feature representation. Normalized Euclidean Distance is used to measure the similarity degrees of two feature vectors of palmprint. System testing is done using 500 samples palms, with 4 samples as the reference image and the 6 samples as test images. Experiment results show that this system can achieve a high performance with success rate about 97.33% (FNMR=1.67%, FMR=1.00 %, T=0.036.

  17. Deconvolution of time series in the laboratory

    Science.gov (United States)

    John, Thomas; Pietschmann, Dirk; Becker, Volker; Wagner, Christian

    2016-10-01

    In this study, we present two practical applications of the deconvolution of time series in Fourier space. First, we reconstruct a filtered input signal of sound cards that has been heavily distorted by a built-in high-pass filter using a software approach. Using deconvolution, we can partially bypass the filter and extend the dynamic frequency range by two orders of magnitude. Second, we construct required input signals for a mechanical shaker in order to obtain arbitrary acceleration waveforms, referred to as feedforward control. For both situations, experimental and theoretical approaches are discussed to determine the system-dependent frequency response. Moreover, for the shaker, we propose a simple feedback loop as an extension to the feedforward control in order to handle nonlinearities of the system.

  18. Using entropy to cut complex time series

    Science.gov (United States)

    Mertens, David; Poncela Casasnovas, Julia; Spring, Bonnie; Amaral, L. A. N.

    2013-03-01

    Using techniques from statistical physics, physicists have modeled and analyzed human phenomena varying from academic citation rates to disease spreading to vehicular traffic jams. The last decade's explosion of digital information and the growing ubiquity of smartphones has led to a wealth of human self-reported data. This wealth of data comes at a cost, including non-uniform sampling and statistically significant but physically insignificant correlations. In this talk I present our work using entropy to identify stationary sub-sequences of self-reported human weight from a weight management web site. Our entropic approach-inspired by the infomap network community detection algorithm-is far less biased by rare fluctuations than more traditional time series segmentation techniques. Supported by the Howard Hughes Medical Institute

  19. Normalizing the causality between time series

    Science.gov (United States)

    Liang, X. San

    2015-08-01

    Recently, a rigorous yet concise formula was derived to evaluate information flow, and hence the causality in a quantitative sense, between time series. To assess the importance of a resulting causality, it needs to be normalized. The normalization is achieved through distinguishing a Lyapunov exponent-like, one-dimensional phase-space stretching rate and a noise-to-signal ratio from the rate of information flow in the balance of the marginal entropy evolution of the flow recipient. It is verified with autoregressive models and applied to a real financial analysis problem. An unusually strong one-way causality is identified from IBM (International Business Machines Corporation) to GE (General Electric Company) in their early era, revealing to us an old story, which has almost faded into oblivion, about "Seven Dwarfs" competing with a giant for the mainframe computer market.

  20. Phase correlation of foreign exchange time series

    Science.gov (United States)

    Wu, Ming-Chya

    2007-03-01

    Correlation of foreign exchange rates in currency markets is investigated based on the empirical data of USD/DEM and USD/JPY exchange rates for a period from February 1 1986 to December 31 1996. The return of exchange time series is first decomposed into a number of intrinsic mode functions (IMFs) by the empirical mode decomposition method. The instantaneous phases of the resultant IMFs calculated by the Hilbert transform are then used to characterize the behaviors of pricing transmissions, and the correlation is probed by measuring the phase differences between two IMFs in the same order. From the distribution of phase differences, our results show explicitly that the correlations are stronger in daily time scale than in longer time scales. The demonstration for the correlations in periods of 1986-1989 and 1990-1993 indicates two exchange rates in the former period were more correlated than in the latter period. The result is consistent with the observations from the cross-correlation calculation.

  1. Single-Index Additive Vector Autoregressive Time Series Models

    KAUST Repository

    LI, YEHUA

    2009-09-01

    We study a new class of nonlinear autoregressive models for vector time series, where the current vector depends on single-indexes defined on the past lags and the effects of different lags have an additive form. A sufficient condition is provided for stationarity of such models. We also study estimation of the proposed model using P-splines, hypothesis testing, asymptotics, selection of the order of the autoregression and of the smoothing parameters and nonlinear forecasting. We perform simulation experiments to evaluate our model in various settings. We illustrate our methodology on a climate data set and show that our model provides more accurate yearly forecasts of the El Niño phenomenon, the unusual warming of water in the Pacific Ocean. © 2009 Board of the Foundation of the Scandinavian Journal of Statistics.

  2. Cycles, scaling and crossover phenomenon in length of the day (LOD) time series

    Science.gov (United States)

    Telesca, Luciano

    2007-06-01

    The dynamics of the temporal fluctuations of the length of the day (LOD) time series from January 1, 1962 to November 2, 2006 were investigated. The power spectrum of the whole time series has revealed annual, semi-annual, decadal and daily oscillatory behaviors, correlated with oceanic-atmospheric processes and interactions. The scaling behavior was analyzed by using the detrended fluctuation analysis (DFA), which has revealed two different scaling regimes, separated by a crossover timescale at approximately 23 days. Flicker-noise process can describe the dynamics of the LOD time regime involving intermediate and long timescales, while Brownian dynamics characterizes the LOD time series for small timescales.

  3. 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...

  4. Ocean Wireless Networking and Real Time Data Management

    Science.gov (United States)

    Berger, J.; Orcutt, J. A.; Vernon, F. L.; Braun, H. W.; Rajasekar, A.

    2001-12-01

    Recent advances in technology have enabled the exploitation of satellite communications for high-speed (> 64 kbps) duplex communications with oceanographic ships at sea. Furthermore, decreasing costs for high-speed communications have made possible continuous connectivity to the global Internet for delivery of data ashore and communications with scientists and engineers on the ship. Through support from the Office of Naval Research, we have planned a series of tests using the R/V Revelle for real time data delivery of large quantities of underway data (e.g. continuous multibeam profiling) to shore for quality control, archiving, and real-time data availability. The Cecil H. and Ida M. Green Institute of Geophysics and Planetary Physics (IGPP) and the San Diego Supercomputer Center (SDSC) were funded by the NSF Information Technology Research (ITR) Program, the California Institute for Telecommunications and Information Technology [Cal-(IT)2] and the Scripps Institution of Oceanography for research entitled: "Exploring the Environment in Time: Wireless Networks & Real-Time Management." We will describe the technology to be used for the real-time seagoing experiment and the planned expansion of the project through support from the ITR grant. The short-term goal is to exercise the communications system aboard ship in various weather conditions and sea states while testing and developing the real-time data quality control and archiving methodology. The long-term goal is to enable continuous observations in the ocean, specifically supporting the goals of the DEOS (Dynamics of Earth and Ocean Systems) observatory program supported through a NSF Major Research Equipment (MRE) program - a permanent presence in the oceans. The impact on scientific work aboard ships, however, is likely to be fundamental. It will be possible to go to sea in the future with limited engineering capability for scientific operations by allowing shore-based quality control of data collected and

  5. Fisher information framework for time series modeling

    Science.gov (United States)

    Venkatesan, R. C.; Plastino, A.

    2017-08-01

    A robust prediction model invoking the Takens embedding theorem, whose working hypothesis is obtained via an inference procedure based on the minimum Fisher information principle, is presented. The coefficients of the ansatz, central to the working hypothesis satisfy a time independent Schrödinger-like equation in a vector setting. The inference of (i) the probability density function of the coefficients of the working hypothesis and (ii) the establishing of constraint driven pseudo-inverse condition for the modeling phase of the prediction scheme, is made, for the case of normal distributions, with the aid of the quantum mechanical virial theorem. The well-known reciprocity relations and the associated Legendre transform structure for the Fisher information measure (FIM, hereafter)-based model in a vector setting (with least square constraints) are self-consistently derived. These relations are demonstrated to yield an intriguing form of the FIM for the modeling phase, which defines the working hypothesis, solely in terms of the observed data. Cases for prediction employing time series' obtained from the: (i) the Mackey-Glass delay-differential equation, (ii) one ECG signal from the MIT-Beth Israel Deaconess Hospital (MIT-BIH) cardiac arrhythmia database, and (iii) one ECG signal from the Creighton University ventricular tachyarrhythmia database. The ECG samples were obtained from the Physionet online repository. These examples demonstrate the efficiency of the prediction model. Numerical examples for exemplary cases are provided.

  6. Time series modeling for syndromic surveillance

    Directory of Open Access Journals (Sweden)

    Mandl Kenneth D

    2003-01-01

    Full Text Available Abstract Background Emergency department (ED based syndromic surveillance systems identify abnormally high visit rates that may be an early signal of a bioterrorist attack. For example, an anthrax outbreak might first be detectable as an unusual increase in the number of patients reporting to the ED with respiratory symptoms. Reliably identifying these abnormal visit patterns requires a good understanding of the normal patterns of healthcare usage. Unfortunately, systematic methods for determining the expected number of (ED visits on a particular day have not yet been well established. We present here a generalized methodology for developing models of expected ED visit rates. Methods Using time-series methods, we developed robust models of ED utilization for the purpose of defining expected visit rates. The models were based on nearly a decade of historical data at a major metropolitan academic, tertiary care pediatric emergency department. The historical data were fit using trimmed-mean seasonal models, and additional models were fit with autoregressive integrated moving average (ARIMA residuals to account for recent trends in the data. The detection capabilities of the model were tested with simulated outbreaks. Results Models were built both for overall visits and for respiratory-related visits, classified according to the chief complaint recorded at the beginning of each visit. The mean absolute percentage error of the ARIMA models was 9.37% for overall visits and 27.54% for respiratory visits. A simple detection system based on the ARIMA model of overall visits was able to detect 7-day-long simulated outbreaks of 30 visits per day with 100% sensitivity and 97% specificity. Sensitivity decreased with outbreak size, dropping to 94% for outbreaks of 20 visits per day, and 57% for 10 visits per day, all while maintaining a 97% benchmark specificity. Conclusions Time series methods applied to historical ED utilization data are an important tool

  7. Pigment species, depth, and pressure data from bottle and CTD casts in the North Atlantic Ocean as part of the Joint Global Ocean Flux Study / Time Series Site "BATS" (JGOFS/BATS) project, from 1993-10-01 to 1996-12-31 (NODC Accession 9800186)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Pigment species, depth, and pressure data were collected using bottle and CTD casts from the R/V WEATHERBIRD in the North Atlantic Ocean from October 1, 1993 to...

  8. 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...

  9. 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.

  10. 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 ...

  11. 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.

  12. Hidden Markov Models for Time Series An Introduction Using R

    CERN Document Server

    Zucchini, Walter

    2009-01-01

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

  13. 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.

  14. 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.

  15. 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.

  16. 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...

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

    African Journals Online (AJOL)

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

  18. Ocean acoustic tomography - Travel time biases

    Science.gov (United States)

    Spiesberger, J. L.

    1985-01-01

    The travel times of acoustic rays traced through a climatological sound-speed profile are compared with travel times computed through the same profile containing an eddy field. The accuracy of linearizing the relations between the travel time difference and the sound-speed deviation at long ranges is assessed using calculations made for two different eddy fields measured in the eastern Atlantic. Significant nonlinearities are found in some cases, and the relationships of the values of these nonlinearities to the range between source and receiver, to the anomaly size associated with the eddies, and to the positions of the eddies are studied. An analytical model of the nonlinearities is discussed.

  19. 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.

  20. 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...

  1. 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.

  2. 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.

  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. Stochastic time series analysis of hydrology data for water resources

    Science.gov (United States)

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

    2017-11-01

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

  5. Meteorological, physical and time series data collected from station (48114) King Island Buoy by Alaska Ocean Observing System (AOOS) and assembled by Alaska Ocean Observing System (AOOS) in the Bering Sea from 2015-07-23 to 2015-10-21 (NCEI Accession 0163742)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0163742 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention...

  6. Interpretable Early Classification of Multivariate Time Series

    Science.gov (United States)

    Ghalwash, Mohamed F.

    2013-01-01

    Recent advances in technology have led to an explosion in data collection over time rather than in a single snapshot. For example, microarray technology allows us to measure gene expression levels in different conditions over time. Such temporal data grants the opportunity for data miners to develop algorithms to address domain-related problems,…

  7. An accuracy assessment of realtime GNSS time series toward semi- real time seafloor geodetic observation

    Science.gov (United States)

    Osada, Y.; Ohta, Y.; Demachi, T.; Kido, M.; Fujimoto, H.; Azuma, R.; Hino, R.

    2013-12-01

    Large interplate earthquake repeatedly occurred in Japan Trench. Recently, the detail crustal deformation revealed by the nation-wide inland GPS network called as GEONET by GSI. However, the maximum displacement region for interplate earthquake is mainly located offshore region. GPS/Acoustic seafloor geodetic observation (hereafter GPS/A) is quite important and useful for understanding of shallower part of the interplate coupling between subducting and overriding plates. We typically conduct GPS/A in specific ocean area based on repeated campaign style using research vessel or buoy. Therefore, we cannot monitor the temporal variation of seafloor crustal deformation in real time. The one of technical issue on real time observation is kinematic GPS analysis because kinematic GPS analysis based on reference and rover data. If the precise kinematic GPS analysis will be possible in the offshore region, it should be promising method for real time GPS/A with USV (Unmanned Surface Vehicle) and a moored buoy. We assessed stability, precision and accuracy of StarFireTM global satellites based augmentation system. We primarily tested for StarFire in the static condition. In order to assess coordinate precision and accuracy, we compared 1Hz StarFire time series and post-processed precise point positioning (PPP) 1Hz time series by GIPSY-OASIS II processing software Ver. 6.1.2 with three difference product types (ultra-rapid, rapid, and final orbits). We also used difference interval clock information (30 and 300 seconds) for the post-processed PPP processing. The standard deviation of real time StarFire time series is less than 30 mm (horizontal components) and 60 mm (vertical component) based on 1 month continuous processing. We also assessed noise spectrum of the estimated time series by StarFire and post-processed GIPSY PPP results. We found that the noise spectrum of StarFire time series is similar pattern with GIPSY-OASIS II processing result based on JPL rapid orbit

  8. Using NASA's Giovanni System to Simulate Time-Series Stations in the Outflow Region of California's Eel River

    Science.gov (United States)

    Acker, James G.; Shen, Suhung; Leptoukh, Gregory G.; Lee, Zhongping

    2012-01-01

    Oceanographic time-series stations provide vital data for the monitoring of oceanic processes, particularly those associated with trends over time and interannual variability. There are likely numerous locations where the establishment of a time-series station would be desirable, but for reasons of funding or logistics, such establishment may not be feasible. An alternative to an operational time-series station is monitoring of sites via remote sensing. In this study, the NASA Giovanni data system is employed to simulate the establishment of two time-series stations near the outflow region of California s Eel River, which carries a high sediment load. Previous time-series analysis of this location (Acker et al. 2009) indicated that remotely-sensed chl a exhibits a statistically significant increasing trend during summer (low flow) months, but no apparent trend during winter (high flow) months. Examination of several newly-available ocean data parameters in Giovanni, including 8-day resolution data, demonstrates the differences in ocean parameter trends at the two locations compared to regionally-averaged time-series. The hypothesis that the increased summer chl a values are related to increasing SST is evaluated, and the signature of the Eel River plume is defined with ocean optical parameters.

  9. 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. .

  10. Documentation of the U.S. Geological Survey Oceanographic Time-Series Measurement Database

    Science.gov (United States)

    Montgomery, Ellyn T.; Martini, Marinna A.; Lightsom, Frances L.; Butman, Bradford

    2008-01-02

    The U.S. Geological Survey (USGS) Oceanographic Time-Series Data Collection (previously named the USGS Oceanographic Time-Series Measurement Database) contains oceanographic observations made as part of studies designed to increase understanding of sediment transport processes and associated dynamics. Analysis of these data has contributed to more accurate prediction of the movement and fate of sediments and other suspended materials in the coastal ocean. The measurements were collected primarily by investigators at the USGS Woods Hole Coastal and Marine Science Center (WHCMSC) and colleagues, beginning in 1975. Most of the field experiments were carried out on the U.S. continental shelf and slope.

  11. 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...

  12. The importance of time-stepping errors in ocean models

    Science.gov (United States)

    Williams, P. D.

    2011-12-01

    Many ocean models use leapfrog time stepping. The Robert-Asselin (RA) filter is usually applied after each leapfrog step, to control the computational mode. However, it will be shown in this presentation that the RA filter generates very large amounts of numerical diapycnal mixing. In some ocean models, the numerical diapycnal mixing from the RA filter is as large as the physical diapycnal mixing. This lowers our confidence in the fidelity of the simulations. In addition to the above problem, the RA filter also damps the physical solution and degrades the numerical accuracy. These two concomitant problems occur because the RA filter does not conserve the mean state, averaged over the three time slices on which it operates. The presenter has recently proposed a simple modification to the RA filter, which does conserve the three-time-level mean state. The modified filter has become known as the Robert-Asselin-Williams (RAW) filter. When used in conjunction with the leapfrog scheme, the RAW filter eliminates the numerical damping of the physical solution and increases the amplitude accuracy by two orders, yielding third-order accuracy. The phase accuracy is unaffected and remains second-order. The RAW filter can easily be incorporated into existing models of the ocean, typically via the insertion of just a single line of code. Better simulations are obtained, at almost no additional computational expense. Results will be shown from recent implementations of the RAW filter in various ocean models. For example, in the UK Met Office Hadley Centre ocean model, sea-surface temperature and sea-ice biases in the North Atlantic Ocean are found to be reduced. These improvements are encouraging for the use of the RAW filter in other ocean models.

  13. Two-fractal overlap time series: Earthquakes and market crashes

    Indian Academy of Sciences (India)

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

  14. forecasting with nonlinear time series model: a monte-carlo

    African Journals Online (AJOL)

    PUBLICATIONS1

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

  15. Critical values for unit root tests in seasonal time series

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); B. Hobijn (Bart)

    1997-01-01

    textabstractIn this paper, we present tables with critical values for a variety of tests for seasonal and non-seasonal unit roots in seasonal time series. We consider (extensions of) the Hylleberg et al. and Osborn et al. test procedures. These extensions concern time series with increasing seasonal

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

    Science.gov (United States)

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

    2015-09-01

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

  17. 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.

  18. 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)…

  19. The Prediction of Teacher Turnover Employing Time Series Analysis.

    Science.gov (United States)

    Costa, Crist H.

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

  20. Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

    Science.gov (United States)

    Price, Larry R.

    2012-01-01

    The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

  1. Time series forecasting based on deep extreme learning machine

    NARCIS (Netherlands)

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

    2017-01-01

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

  2. Parameterizing unconditional skewness in models for financial time series

    DEFF Research Database (Denmark)

    He, Changli; Silvennoinen, Annastiina; Teräsvirta, Timo

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

  3. 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

  4. Efficient use of correlation entropy for analysing time series data

    Indian Academy of Sciences (India)

    Abstract. The correlation dimension D2 and correlation entropy K2 are both important quantifiers in nonlinear time series analysis. However, use of D2 has been more common compared to K2 as a discriminating measure. One reason for this is that D2 is a static measure and can be easily evaluated from a time series.

  5. Time series prediction of apple scab using meteorological ...

    African Journals Online (AJOL)

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

  6. 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.

  7. 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.

  8. Frontiers in Time Series and Financial Econometrics : An overview

    NARCIS (Netherlands)

    S. Ling (Shiqing); M.J. McAleer (Michael); H. Tong (Howell)

    2015-01-01

    markdownabstract__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

  9. Frontiers in Time Series and Financial Econometrics: An Overview

    NARCIS (Netherlands)

    S. Ling (Shiqing); M.J. McAleer (Michael); H. Tong (Howell)

    2015-01-01

    markdownabstract__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

  10. vector bilinear autoregressive time series model and its superiority

    African Journals Online (AJOL)

    KEYWORDS: Linear time series, Autoregressive process, Autocorrelation function, Partial autocorrelation function,. Vector time .... important result on matrix algebra with respect to the spectral ..... application to covariance analysis of super-.

  11. 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.

  12. Pseudo-random bit generator based on lag time series

    Science.gov (United States)

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

    2014-12-01

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

  13. Mackenzie River Delta morphological change based on Landsat time series

    Science.gov (United States)

    Vesakoski, Jenni-Mari; Alho, Petteri; Gustafsson, David; Arheimer, Berit; Isberg, Kristina

    2015-04-01

    Arctic rivers are sensitive and yet quite unexplored river systems to which the climate change will impact on. Research has not focused in detail on the fluvial geomorphology of the Arctic rivers mainly due to the remoteness and wideness of the watersheds, problems with data availability and difficult accessibility. Nowadays wide collaborative spatial databases in hydrology as well as extensive remote sensing datasets over the Arctic are available and they enable improved investigation of the Arctic watersheds. Thereby, it is also important to develop and improve methods that enable detecting the fluvio-morphological processes based on the available data. Furthermore, it is essential to reconstruct and improve the understanding of the past fluvial processes in order to better understand prevailing and future fluvial processes. In this study we sum up the fluvial geomorphological change in the Mackenzie River Delta during the last ~30 years. The Mackenzie River Delta (~13 000 km2) is situated in the North Western Territories, Canada where the Mackenzie River enters to the Beaufort Sea, Arctic Ocean near the city of Inuvik. Mackenzie River Delta is lake-rich, productive ecosystem and ecologically sensitive environment. Research objective is achieved through two sub-objectives: 1) Interpretation of the deltaic river channel planform change by applying Landsat time series. 2) Definition of the variables that have impacted the most on detected changes by applying statistics and long hydrological time series derived from Arctic-HYPE model (HYdrologic Predictions for Environment) developed by Swedish Meteorological and Hydrological Institute. According to our satellite interpretation, field observations and statistical analyses, notable spatio-temporal changes have occurred in the morphology of the river channel and delta during the past 30 years. For example, the channels have been developing in braiding and sinuosity. In addition, various linkages between the studied

  14. Analysis of time series and size of equivalent sample

    International Nuclear Information System (INIS)

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

    2004-01-01

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

  15. Characterizing time series: when Granger causality triggers complex networks

    International Nuclear Information System (INIS)

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

    2012-01-01

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

  16. Characterizing time series: when Granger causality triggers complex networks

    Science.gov (United States)

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

    2012-08-01

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

  17. Time-series analysis of climatologic measurements: a method to distinguish future climatic changes

    International Nuclear Information System (INIS)

    Duband, D.

    1992-01-01

    Time-series analysis of climatic parameters as air temperature, rivers flow rate, lakes or seas level is an indispensable basis to detect a possible significant climatic change. These observations, when they are carefully analyzed and criticized, constitute the necessary reference for testing and validation numerical climatic models which try to simulate the physical and dynamical process of the ocean-atmosphere couple, taking continents into account. 32 refs., 13 figs

  18. 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.

  19. 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.

  20. Signal Processing for Time-Series Functions on a Graph

    Science.gov (United States)

    2018-02-01

    Figures Fig. 1 Time -series function on a fixed graph.............................................2 iv Approved for public release; distribution is...φi〉`2(V)φi (39) 6= f̄ (40) Instead, we simply recover the average of f over time . 13 Approved for public release; distribution is unlimited. This...ARL-TR-8276• FEB 2018 US Army Research Laboratory Signal Processing for Time -Series Functions on a Graph by Humberto Muñoz-Barona, Jean Vettel, and

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

    Science.gov (United States)

    Liu, Zitao; Hauskrecht, Milos

    2015-09-01

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

  2. Clinical time series prediction: towards a hierarchical dynamical system framework

    Science.gov (United States)

    Liu, Zitao; Hauskrecht, Milos

    2014-01-01

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

  3. Conditional time series forecasting with convolutional neural networks

    NARCIS (Netherlands)

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

    2017-01-01

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

  4. Analysis of complex time series using refined composite multiscale entropy

    International Nuclear Information System (INIS)

    Wu, Shuen-De; Wu, Chiu-Wen; Lin, Shiou-Gwo; Lee, Kung-Yen; Peng, Chung-Kang

    2014-01-01

    Multiscale entropy (MSE) is an effective algorithm for measuring the complexity of a time series that has been applied in many fields successfully. However, MSE may yield an inaccurate estimation of entropy or induce undefined entropy because the coarse-graining procedure reduces the length of a time series considerably at large scales. Composite multiscale entropy (CMSE) was recently proposed to improve the accuracy of MSE, but it does not resolve undefined entropy. Here we propose a refined composite multiscale entropy (RCMSE) to improve CMSE. For short time series analyses, we demonstrate that RCMSE increases the accuracy of entropy estimation and reduces the probability of inducing undefined entropy.

  5. Forecasting daily meteorological time series using ARIMA and regression models

    Science.gov (United States)

    Murat, Małgorzata; Malinowska, Iwona; Gos, Magdalena; Krzyszczak, Jaromir

    2018-04-01

    The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.

  6. 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...

  7. Modelling road accidents: An approach using structural time series

    Science.gov (United States)

    Junus, Noor Wahida Md; Ismail, Mohd Tahir

    2014-09-01

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

  8. Multivariate time series analysis with R and financial applications

    CERN Document Server

    Tsay, Ruey S

    2013-01-01

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

  9. Revealing the timing of ocean stratification using remotely-sensed ocean fronts: links with marine predators

    Science.gov (United States)

    Miller, P. I.; Loveday, B. R.

    2016-02-01

    Stratification is of critical importance to the mixing and productivity of the ocean, though currently it can only be measured using in situ sampling, profiling buoys or underwater autonomous vehicles. Stratification is understood to affect the surface aggregation of pelagic fish and hence the foraging behaviour and distribution of their predators such as seabirds and cetaceans. Satellite Earth observation sensors cannot directly detect stratification, but can observe surface features related to the presence of stratification, for example shelf-sea fronts that separate tidally-mixed water from seasonally stratified water. This presentation describes a novel algorithm that accumulates evidence for stratification from a sequence of oceanic front maps, and in certain regions can reveal the timing of the seasonal onset and breakdown of stratification. Initial comparisons will be made with seabird locations acquired through GPS tagging. If successful, a remotely-sensed stratification timing index would augment the ocean front metrics already developed at PML, that have been applied in over 20 journal articles relating marine predators to ocean fronts. The figure below shows a preliminary remotely-sensed 'stratification' index, for 25-31 Jul. 2010, where red indicates water with stronger evidence for stratification.

  10. Scalable Prediction of Energy Consumption using Incremental Time Series Clustering

    Energy Technology Data Exchange (ETDEWEB)

    Simmhan, Yogesh; Noor, Muhammad Usman

    2013-10-09

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

  11. Characterizing interdependencies of multiple time series theory and applications

    CERN Document Server

    Hosoya, Yuzo; Takimoto, Taro; Kinoshita, Ryo

    2017-01-01

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

  12. Aerosol climate time series from ESA Aerosol_cci (Invited)

    Science.gov (United States)

    Holzer-Popp, T.

    2013-12-01

    developed further, to evaluate the datasets and their regional and seasonal merits. The validation showed that most datasets have improved significantly and in particular PARASOL (ocean only) provides excellent results. The metrics for AATSR (land and ocean) datasets are similar to those of MODIS and MISR, with AATSR better in some land regions and less good in some others (ocean). However, AATSR coverage is smaller than that of MODIS due to swath width. The MERIS dataset provides better coverage than AATSR but has lower quality (especially over land) than the other datasets. Also the synergetic AATSR/SCIAMACHY dataset has lower quality. The evaluation of the pixel uncertainties shows first good results but also reveals that more work needs to be done to provide comprehensive information for data assimilation. Users (MACC/ECMWF, AEROCOM) confirmed the relevance of this additional information and encouraged Aerosol_cci to release the current uncertainties. The paper will summarize and discuss the results of three year work in Aerosol_cci, extract the lessons learned and conclude with an outlook to the work proposed for the next three years. In this second phase a cyclic effort of algorithm evolution, dataset generation, validation and assessment will be applied to produce and further improve complete time series from all sensors under investigation, new sensors will be added (e.g. IASI), and preparation for the Sentinel missions will be made.

  13. 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).

  14. 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.

  15. 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...

  16. 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.

  17. Growth And Export Expansion In Mauritius - A Time Series Analysis ...

    African Journals Online (AJOL)

    Growth And Export Expansion In Mauritius - A Time Series Analysis. ... RV Sannassee, R Pearce ... Using Granger Causality tests, the short-run analysis results revealed that there is significant reciprocal causality between real export earnings ...

  18. On robust forecasting of autoregressive time series under censoring

    OpenAIRE

    Kharin, Y.; Badziahin, I.

    2009-01-01

    Problems of robust statistical forecasting are considered for autoregressive time series observed under distortions generated by interval censoring. Three types of robust forecasting statistics are developed; meansquare risk is evaluated for the developed forecasting statistics. Numerical results are given.

  19. Unsupervised land cover change detection: meaningful sequential time series analysis

    CSIR Research Space (South Africa)

    Salmon, BP

    2011-06-01

    Full Text Available An automated land cover change detection method is proposed that uses coarse spatial resolution hyper-temporal earth observation satellite time series data. The study compared three different unsupervised clustering approaches that operate on short...

  20. Fast and Flexible Multivariate Time Series Subsequence 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...