Sample records for ocean time series
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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...
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Ocean time-series near Bermuda: Hydrostation S and the US JGOFS Bermuda Atlantic time-series study
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.
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OceanXtremes: Scalable Anomaly Detection in Oceanographic Time-Series
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
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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
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The Oceanic Flux Program: A three decade time-series of particle flux in the deep Sargasso Sea
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.
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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...
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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...
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Climate Prediction Center (CPC) Global Precipitation Time Series
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...
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Climate Prediction Center (CPC) Global Temperature Time Series
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...
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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...
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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...
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Reconstructing surface ocean circulation with 129I time series records from corals.
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.
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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...
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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...
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Metagenomics meets time series analysis: unraveling microbial community dynamics
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
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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.
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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...
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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...
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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.
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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
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Incorporating Satellite Time-Series Data into Modeling
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.
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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...
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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)...
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AFSC/ABL: Ugashik sockeye salmon scale time series
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...
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AFSC/ABL: Naknek sockeye salmon scale time series
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....
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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...
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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...
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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)...
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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)...
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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)...
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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...
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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.)
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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.
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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...
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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...
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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)...
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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.
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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)...
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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...
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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...
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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)...
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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)...
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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)...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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.
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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...
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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...
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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...
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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...
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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...
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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...
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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)...
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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...
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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...
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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)...
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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...
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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...
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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...
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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...
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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)...
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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)...
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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)...
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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)...
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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...
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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...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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Cycles, scaling and crossover phenomenon in length of the day (LOD) time series
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.
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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Cox, C.; Au, A.; Klosko, S.; Chao, B.; Smith, David E. (Technical Monitor)
2001-01-01
The upcoming GRACE mission promises to open a window on details of the global mass budget that will have remarkable clarity, but it will not directly answer the question of what the state of the Earth's mass budget is over the critical last quarter of the 20th century. To address that problem we must draw upon existing technologies such as SLR, DORIS, and GPS, and climate modeling runs in order to improve our understanding. Analysis of long-period geopotential changes based on SLR and DORIS tracking has shown that addition of post 1996 satellite tracking data has a significant impact on the recovered zonal rates and long-period tides. Interannual effects such as those causing the post 1996 anomalies must be better characterized before refined estimates of the decadal period changes in the geopotential can be derived from the historical database of satellite tracking. A possible cause of this anomaly is variations in ocean mass distribution, perhaps associated with the recent large El Nino/La Nina. In this study, a low-degree spherical harmonic gravity time series derived from satellite tracking is compared with a TOPEX/POSEIDON-derived sea surface height time series. Corrections for atmospheric mass effects, continental hydrology, snowfall accumulation, and ocean steric model predictions will be considered.
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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....
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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)...
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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)...
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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...
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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)...
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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...
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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...
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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...
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Piecuch, Christopher G.; Landerer, Felix W.; Ponte, Rui M.
2018-05-01
Monthly ocean bottom pressure solutions from the Gravity Recovery and Climate Experiment (GRACE), derived using surface spherical cap mass concentration (MC) blocks and spherical harmonics (SH) basis functions, are compared to tide gauge (TG) monthly averaged sea level data over 2003-2015 to evaluate improved gravimetric data processing methods near the coast. MC solutions can explain ≳ 42% of the monthly variance in TG time series over broad shelf regions and in semi-enclosed marginal seas. MC solutions also generally explain ˜5-32 % more TG data variance than SH estimates. Applying a coastline resolution improvement algorithm in the GRACE data processing leads to ˜ 31% more variance in TG records explained by the MC solution on average compared to not using this algorithm. Synthetic observations sampled from an ocean general circulation model exhibit similar patterns of correspondence between modeled TG and MC time series and differences between MC and SH time series in terms of their relationship with TG time series, suggesting that observational results here are generally consistent with expectations from ocean dynamics. This work demonstrates the improved quality of recent MC solutions compared to earlier SH estimates over the coastal ocean, and suggests that the MC solutions could be a useful tool for understanding contemporary coastal sea level variability and change.
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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...
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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...
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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...
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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...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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The Blue Planet: Seas & Oceans. Young Discovery Library Series.
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)…
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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...
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Documentation of the U.S. Geological Survey Oceanographic Time-Series Measurement Database
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.
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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)...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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)...
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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...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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GPS Position Time Series @ JPL
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
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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...
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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...
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Time series analysis time series analysis methods and applications
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...
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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...
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Highly comparative time-series analysis: the empirical structure of time series and their methods.
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.
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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...
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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)...
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Introduction to Time Series Modeling
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
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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...
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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...
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Ocean Wireless Networking and Real Time Data Management
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
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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...
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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...
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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...
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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...
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International Work-Conference on Time Series
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...
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Online Time Series Analysis of Land Products over Asia Monsoon Region via Giovanni
Shen, Suhung; Leptoukh, Gregory G.; Gerasimov, Irina
2011-01-01
Time series analysis is critical to the study of land cover/land use changes and climate. Time series studies at local-to-regional scales require higher spatial resolution, such as 1km or less, data. MODIS land products of 250m to 1km resolution enable such studies. However, such MODIS land data files are distributed in 10ox10o tiles, due to large data volumes. Conducting a time series study requires downloading all tiles that include the study area for the time period of interest, and mosaicking the tiles spatially. This can be an extremely time-consuming process. In support of the Monsoon Asia Integrated Regional Study (MAIRS) program, NASA GES DISC (Goddard Earth Sciences Data and Information Services Center) has processed MODIS land products at 1 km resolution over the Asia monsoon region (0o-60oN, 60o-150oE) with a common data structure and format. The processed data have been integrated into the Giovanni system (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) that enables users to explore, analyze, and download data over an area and time period of interest easily. Currently, the following regional MODIS land products are available in Giovanni: 8-day 1km land surface temperature and active fire, monthly 1km vegetation index, and yearly 0.05o, 500m land cover types. More data will be added in the near future. By combining atmospheric and oceanic data products in the Giovanni system, it is possible to do further analyses of environmental and climate changes associated with the land, ocean, and atmosphere. This presentation demonstrates exploring land products in the Giovanni system with sample case scenarios.
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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...
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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...
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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)...
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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)...
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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)...
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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)...
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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)...
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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)...
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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...
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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...
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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.
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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
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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,...
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Duality between Time Series and Networks
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
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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...
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National Oceanic and Atmospheric Administration, Department of Commerce — The Cross-Calibrated Multi-Platform (CCMP) Ocean Surface Wind Vector Analyses (Atlas et al., 2011) provide a consistent, gap-free long-term time-series of monthly...
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A Course in Time Series Analysis
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
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Kolmogorov Space in Time Series Data
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 ...
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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 ...
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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...
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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)...
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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)...
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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...
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Multiple Indicator Stationary Time Series Models.
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…
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Seasonality in ocean microbial communities.
Giovannoni, Stephen J; Vergin, Kevin L
2012-02-10
Ocean warming occurs every year in seasonal cycles that can help us to understand long-term responses of plankton to climate change. Rhythmic seasonal patterns of microbial community turnover are revealed when high-resolution measurements of microbial plankton diversity are applied to samples collected in lengthy time series. Seasonal cycles in microbial plankton are complex, but the expansion of fixed ocean stations monitoring long-term change and the development of automated instrumentation are providing the time-series data needed to understand how these cycles vary across broad geographical scales. By accumulating data and using predictive modeling, we gain insights into changes that will occur as the ocean surface continues to warm and as the extent and duration of ocean stratification increase. These developments will enable marine scientists to predict changes in geochemical cycles mediated by microbial communities and to gauge their broader impacts.
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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...
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International Work-Conference on Time Series
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.
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OceanNOMADS: Real-time and retrospective access to operational U.S. ocean prediction products
Harding, J. M.; Cross, S. L.; Bub, F.; Ji, M.
2011-12-01
The National Oceanic and Atmospheric Administration (NOAA) National Operational Model Archive and Distribution System (NOMADS) provides both real-time and archived atmospheric model output from servers at the National Centers for Environmental Prediction (NCEP) and National Climatic Data Center (NCDC) respectively (http://nomads.ncep.noaa.gov/txt_descriptions/marRutledge-1.pdf). The NOAA National Ocean Data Center (NODC) with NCEP is developing a complementary capability called OceanNOMADS for operational ocean prediction models. An NCEP ftp server currently provides real-time ocean forecast output (http://www.opc.ncep.noaa.gov/newNCOM/NCOM_currents.shtml) with retrospective access through NODC. A joint effort between the Northern Gulf Institute (NGI; a NOAA Cooperative Institute) and the NOAA National Coastal Data Development Center (NCDDC; a division of NODC) created the developmental version of the retrospective OceanNOMADS capability (http://www.northerngulfinstitute.org/edac/ocean_nomads.php) under the NGI Ecosystem Data Assembly Center (EDAC) project (http://www.northerngulfinstitute.org/edac/). Complementary funding support for the developmental OceanNOMADS from U.S. Integrated Ocean Observing System (IOOS) through the Southeastern University Research Association (SURA) Model Testbed (http://testbed.sura.org/) this past year provided NODC the analogue that facilitated the creation of an NCDDC production version of OceanNOMADS (http://www.ncddc.noaa.gov/ocean-nomads/). Access tool development and storage of initial archival data sets occur on the NGI/NCDDC developmental servers with transition to NODC/NCCDC production servers as the model archives mature and operational space and distribution capability grow. Navy operational global ocean forecast subsets for U.S waters comprise the initial ocean prediction fields resident on the NCDDC production server. The NGI/NCDDC developmental server currently includes the Naval Research Laboratory Inter-America Seas
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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
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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...
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Stochastic models for time series
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 ...
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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.
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Nickles, C.; Zhao, Y.; Beighley, E.; Durand, M. T.; David, C. H.; Lee, H.
2017-12-01
The Surface Water and Ocean Topography (SWOT) satellite mission is jointly developed by NASA, the French space agency (CNES), with participation from the Canadian and UK space agencies to serve both the hydrology and oceanography communities. The SWOT mission will sample global surface water extents and elevations (lakes/reservoirs, rivers, estuaries, oceans, sea and land ice) at a finer spatial resolution than is currently possible enabling hydrologic discovery, model advancements and new applications that are not currently possible or likely even conceivable. Although the mission will provide global cover, analysis and interpolation of the data generated from the irregular space/time sampling represents a significant challenge. In this study, we explore the applicability of the unique space/time sampling for understanding river discharge dynamics throughout the Ohio River Basin. River network topology, SWOT sampling (i.e., orbit and identified SWOT river reaches) and spatial interpolation concepts are used to quantify the fraction of effective sampling of river reaches each day of the three-year mission. Streamflow statistics for SWOT generated river discharge time series are compared to continuous daily river discharge series. Relationships are presented to transform SWOT generated streamflow statistics to equivalent continuous daily discharge time series statistics intended to support hydrologic applications using low-flow and annual flow duration statistics.
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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.
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Visibility Graph Based Time Series Analysis.
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.
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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.
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Atlantic Ocean Acidification Test-Bed -- OA Time-Series, Cheeca Rocks, Florida Reef Tract FY2012
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...
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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...
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Single-Index Additive Vector Autoregressive Time Series Models
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.
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Time-variable gravity fields and ocean mass change from 37 months of kinematic Swarm orbits
Lück, Christina; Kusche, Jürgen; Rietbroek, Roelof; Löcher, Anno
2018-03-01
Measuring the spatiotemporal variation of ocean mass allows for partitioning of volumetric sea level change, sampled by radar altimeters, into mass-driven and steric parts. The latter is related to ocean heat change and the current Earth's energy imbalance. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) mission has provided monthly snapshots of the Earth's time-variable gravity field, from which one can derive ocean mass variability. However, GRACE has reached the end of its lifetime with data degradation and several gaps occurred during the last years, and there will be a prolonged gap until the launch of the follow-on mission GRACE-FO. Therefore, efforts focus on generating a long and consistent ocean mass time series by analyzing kinematic orbits from other low-flying satellites, i.e. extending the GRACE time series. Here we utilize data from the European Space Agency's (ESA) Swarm Earth Explorer satellites to derive and investigate ocean mass variations. For this aim, we use the integral equation approach with short arcs (Mayer-Gürr, 2006) to compute more than 500 time-variable gravity fields with different parameterizations from kinematic orbits. We investigate the potential to bridge the gap between the GRACE and the GRACE-FO mission and to substitute missing monthly solutions with Swarm results of significantly lower resolution. Our monthly Swarm solutions have a root mean square error (RMSE) of 4.0 mm with respect to GRACE, whereas directly estimating constant, trend, annual, and semiannual (CTAS) signal terms leads to an RMSE of only 1.7 mm. Concerning monthly gaps, our CTAS Swarm solution appears better than interpolating existing GRACE data in 13.5 % of all cases, when artificially removing one solution. In the case of an 18-month artificial gap, 80.0 % of all CTAS Swarm solutions were found closer to the observed GRACE data compared to interpolated GRACE data. Furthermore, we show that precise modeling of non-gravitational forces
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Network structure of multivariate time series.
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.
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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
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Data mining in time series databases
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.
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Models for dependent time series
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
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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...
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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.
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A review of subsequence time series clustering.
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.
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A Review of Subsequence Time Series Clustering
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
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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...
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National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains time series coastal ocean wave and current data collected during the Atlantic Remote Sensing Land/Ocean Experiment (ARSLOE). ARSLOE was...
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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...
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Adaptive time-variant models for fuzzy-time-series forecasting.
Wong, Wai-Keung; Bai, Enjian; Chu, Alice Wai-Ching
2010-12-01
A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.
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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...
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Time Series Analysis and Forecasting by Example
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
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Time series with tailored nonlinearities
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.
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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...
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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...
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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...
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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...
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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...
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Clustering of financial time series
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.
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Time-variable gravity fields and ocean mass change from 37 months of kinematic Swarm orbits
Directory of Open Access Journals (Sweden)
C. Lück
2018-03-01
Full Text Available Measuring the spatiotemporal variation of ocean mass allows for partitioning of volumetric sea level change, sampled by radar altimeters, into mass-driven and steric parts. The latter is related to ocean heat change and the current Earth's energy imbalance. Since 2002, the Gravity Recovery and Climate Experiment (GRACE mission has provided monthly snapshots of the Earth's time-variable gravity field, from which one can derive ocean mass variability. However, GRACE has reached the end of its lifetime with data degradation and several gaps occurred during the last years, and there will be a prolonged gap until the launch of the follow-on mission GRACE-FO. Therefore, efforts focus on generating a long and consistent ocean mass time series by analyzing kinematic orbits from other low-flying satellites, i.e. extending the GRACE time series. Here we utilize data from the European Space Agency's (ESA Swarm Earth Explorer satellites to derive and investigate ocean mass variations. For this aim, we use the integral equation approach with short arcs (Mayer-Gürr, 2006 to compute more than 500 time-variable gravity fields with different parameterizations from kinematic orbits. We investigate the potential to bridge the gap between the GRACE and the GRACE-FO mission and to substitute missing monthly solutions with Swarm results of significantly lower resolution. Our monthly Swarm solutions have a root mean square error (RMSE of 4.0 mm with respect to GRACE, whereas directly estimating constant, trend, annual, and semiannual (CTAS signal terms leads to an RMSE of only 1.7 mm. Concerning monthly gaps, our CTAS Swarm solution appears better than interpolating existing GRACE data in 13.5 % of all cases, when artificially removing one solution. In the case of an 18-month artificial gap, 80.0 % of all CTAS Swarm solutions were found closer to the observed GRACE data compared to interpolated GRACE data. Furthermore, we show that precise modeling of non
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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...
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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.
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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
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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...
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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...
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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...
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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...
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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.
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Time averaging, ageing and delay analysis of financial time series
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.
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Forootan, Ehsan; Kusche, Jürgen
2016-04-01
Geodetic/geophysical observations, such as the time series of global terrestrial water storage change or sea level and temperature change, represent samples of physical processes and therefore contain information about complex physical interactionswith many inherent time scales. Extracting relevant information from these samples, for example quantifying the seasonality of a physical process or its variability due to large-scale ocean-atmosphere interactions, is not possible by rendering simple time series approaches. In the last decades, decomposition techniques have found increasing interest for extracting patterns from geophysical observations. Traditionally, principal component analysis (PCA) and more recently independent component analysis (ICA) are common techniques to extract statistical orthogonal (uncorrelated) and independent modes that represent the maximum variance of observations, respectively. PCA and ICA can be classified as stationary signal decomposition techniques since they are based on decomposing the auto-covariance matrix or diagonalizing higher (than two)-order statistical tensors from centered time series. However, the stationary assumption is obviously not justifiable for many geophysical and climate variables even after removing cyclic components e.g., the seasonal cycles. In this paper, we present a new decomposition method, the complex independent component analysis (CICA, Forootan, PhD-2014), which can be applied to extract to non-stationary (changing in space and time) patterns from geophysical time series. Here, CICA is derived as an extension of real-valued ICA (Forootan and Kusche, JoG-2012), where we (i) define a new complex data set using a Hilbert transformation. The complex time series contain the observed values in their real part, and the temporal rate of variability in their imaginary part. (ii) An ICA algorithm based on diagonalization of fourth-order cumulants is then applied to decompose the new complex data set in (i
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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)""…
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Entropic Analysis of Electromyography Time Series
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.
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Directory of Open Access Journals (Sweden)
A. Devasthale
2012-02-01
Full Text Available The Advanced Very High Resolution Radiometer (AVHRR instruments onboard the series of National Oceanic and Atmospheric Administration (NOAA satellites offer the longest available meteorological data records from space. These satellites have drifted in orbit resulting in shifts in the local time sampling during the life span of the sensors onboard. Depending upon the amplitude of the diurnal cycle of the geophysical parameters derived, orbital drift may cause spurious trends in their time series. We investigate tropical deep convective clouds, which show pronounced diurnal cycle amplitude, to estimate an upper bound of the impact of orbital drift on their time series. We carry out a rotated empirical orthogonal function analysis (REOF and show that the REOFs are useful in delineating orbital drift signal and, more importantly, in subtracting this signal in the time series of convective cloud amount. These results will help facilitate the derivation of homogenized data series of cloud amount from NOAA satellite sensors and ultimately analyzing trends from them. However, we suggest detailed comparison of various methods and rigorous testing thereof applying final orbital drift corrections.
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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...
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Quantifying memory in complex physiological time-series.
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.
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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....
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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...
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International Nuclear Information System (INIS)
Groves-Kirkby, C.J.; Denman, A.R.; Crockett, R.G.M.; Phillips, P.S.; Gillmore, G.K.
2006-01-01
Analysis of data from extended radon concentration time-series obtained from domestic and public-sector premises in the vicinity of Northampton, UK, and elsewhere, confirms that, in addition to the generally recognised climatic influences, 'Earth Tides' and 'Ocean Tidal Loading' drive periodic radon liberation via geophysically driven groundwater level variations. Regression and cross-correlation with environmental parameters showed some degree of association between radon concentration and mean temperature and rainfall. Fourier analysis of radon time-series identified periodicities of the order of 23.9 h (luni-solar diurnal, K 1 ), 24.0 h (solar day, S 1 ), 168 h (1 week) and 661.3 h (lunar month, M m ), while cross-correlation with tidal strength demonstrated periodicity of the order of 14 days (lunar-solar fortnight, M f ). These results suggest that astronomical influences, including tides, play a part in controlling radon release from the soil
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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.
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Homogenising time series: beliefs, dogmas and facts
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.
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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...
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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...
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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...
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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...
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Multivariate Time Series Decomposition into Oscillation Components.
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.
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Forecasting Enrollments with Fuzzy Time Series.
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…
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Causality as a Rigorous Notion and Quantitative Causality Analysis with Time Series
Liang, X. S.
2017-12-01
Given two time series, can one faithfully tell, in a rigorous and quantitative way, the cause and effect between them? Here we show that this important and challenging question (one of the major challenges in the science of big data), which is of interest in a wide variety of disciplines, has a positive answer. Particularly, for linear systems, the maximal likelihood estimator of the causality from a series X2 to another series X1, written T2→1, turns out to be concise in form: T2→1 = [C11 C12 C2,d1 — C112 C1,d1] / [C112 C22 — C11C122] where Cij (i,j=1,2) is the sample covariance between Xi and Xj, and Ci,dj the covariance between Xi and ΔXj/Δt, the difference approximation of dXj/dt using the Euler forward scheme. An immediate corollary is that causation implies correlation, but not vice versa, resolving the long-standing debate over causation versus correlation. The above formula has been validated with touchstone series purportedly generated with one-way causality that evades the classical approaches such as Granger causality test and transfer entropy analysis. It has also been applied successfully to the investigation of many real problems. Through a simple analysis with the stock series of IBM and GE, an unusually strong one-way causality is identified from the former to the latter 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 computer market. Another example presented here regards the cause-effect relation between the two climate modes, El Niño and Indian Ocean Dipole (IOD). In general, these modes are mutually causal, but the causality is asymmetric. To El Niño, the information flowing from IOD manifests itself as a propagation of uncertainty from the Indian Ocean. In the third example, an unambiguous one-way causality is found between CO2 and the global mean temperature anomaly. While it is confirmed that CO2 indeed drives the recent global warming
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Factors challenging our ability to detect long-term trends in ocean chlorophyll
Directory of Open Access Journals (Sweden)
C. Beaulieu
2013-04-01
Full Text Available Global climate change is expected to affect the ocean's biological productivity. The most comprehensive information available about the global distribution of contemporary ocean primary productivity is derived from satellite data. Large spatial patchiness and interannual to multidecadal variability in chlorophyll a concentration challenges efforts to distinguish a global, secular trend given satellite records which are limited in duration and continuity. The longest ocean color satellite record comes from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS, which failed in December 2010. The Moderate Resolution Imaging Spectroradiometer (MODIS ocean color sensors are beyond their originally planned operational lifetime. Successful retrieval of a quality signal from the current Visible Infrared Imager Radiometer Suite (VIIRS instrument, or successful launch of the Ocean and Land Colour Instrument (OLCI expected in 2014 will hopefully extend the ocean color time series and increase the potential for detecting trends in ocean productivity in the future. Alternatively, a potential discontinuity in the time series of ocean chlorophyll a, introduced by a change of instrument without overlap and opportunity for cross-calibration, would make trend detection even more challenging. In this paper, we demonstrate that there are a few regions with statistically significant trends over the ten years of SeaWiFS data, but at a global scale the trend is not large enough to be distinguished from noise. We quantify the degree to which red noise (autocorrelation especially challenges trend detection in these observational time series. We further demonstrate how discontinuities in the time series at various points would affect our ability to detect trends in ocean chlorophyll a. We highlight the importance of maintaining continuous, climate-quality satellite data records for climate-change detection and attribution studies.
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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...
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Forecasting Cryptocurrencies Financial Time Series
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...
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Time series modeling, computation, and inference
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
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Time Series Analysis Forecasting and Control
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
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Costationarity of Locally Stationary Time Series Using costat
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...
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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...
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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...
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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
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Introduction to time series and forecasting
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...
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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...
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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.
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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...
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Frontiers in Time Series and Financial Econometrics
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...
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Scale-dependent intrinsic entropies of complex time series.
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).
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Elements of nonlinear time series analysis and forecasting
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...
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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.
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Pelz, M.; Hoeberechts, M.; McLean, M. A.; Riddell, D. J.; Ewing, N.; Brown, J. C.
2016-12-01
This presentation outlines the authentic research experiences created by Ocean Networks Canada's Ocean Sense program, a transformative education program that connects students and teachers with place-based, real-time data via the Internet. This program, developed in collaboration with community educators, features student-centric activities, clearly outlined learning outcomes, assessment tools and curriculum aligned content. Ocean Networks Canada (ONC), an initiative of the University of Victoria, develops, operates, and maintains cabled ocean observatory systems. Technologies developed on the world-leading NEPTUNE and VENUS observatories have been adapted for small coastal installations called "community observatories," which enable community members to directly monitor conditions in the local ocean environment. Data from these observatories are fundamental to lessons and activities in the Ocean Sense program. Marketed as Ocean Sense: Local observations, global connections, the program introduces middle and high school students to research methods in biology, oceanography and ocean engineering. It includes a variety of resources and opportunities to excite students and spark curiosity about the ocean environment. The program encourages students to connect their local observations to global ocean processes and the observations of students in other geographic regions. Connection to place and local relevance of the program is enhanced through an emphasis on Indigenous and place-based knowledge. The program promotes of cross-cultural learning with the inclusion of Indigenous knowledge of the ocean. Ocean Sense provides students with an authentic research experience by connecting them to real-time data, often within their own communities. Using the freely accessible data portal, students can curate the data they need from a range of instruments and time periods. Further, students are not restricted to their local community; if their question requires a greater range of
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The Baltic Sea as a time machine for the future coastal ocean
DEFF Research Database (Denmark)
Reusch, Thorsten B. H.; Dierking, Jan; Andersson, Helen C.
2018-01-01
Coastal global oceans are expected to undergo drastic changes driven by climate change and increasing anthropogenic pressures in coming decades. Predicting specific future conditions and assessing the best management strategies to maintain ecosystem integrity and sustainable resource use are diff......Coastal global oceans are expected to undergo drastic changes driven by climate change and increasing anthropogenic pressures in coming decades. Predicting specific future conditions and assessing the best management strategies to maintain ecosystem integrity and sustainable resource use...... are difficult, because of multiple interacting pressures, uncertain projections, and a lack of test cases for management. We argue that the Baltic Sea can serve as a time machine to study consequences and mitigation of future coastal perturbations, due to its unique combination of an early history...... of multistressor disturbance and ecosystem deterioration and early implementation of cross-border environmental management to address these problems. The Baltic Sea also stands out in providing a strong scientific foundation and accessibility to long-term data series that provide a unique opportunity to assess...
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Detecting chaos in irregularly sampled time series.
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.
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Building Chaotic Model From Incomplete Time Series
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
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Directional spectrum of ocean waves
Digital Repository Service at National Institute of Oceanography (India)
Fernandes, A.A; Gouveia, A; Nagarajan, R.
This paper describes a methodology for obtaining the directional spectrum of ocean waves from time series measurement of wave elevation at several gauges arranged in linear or polygonal arrays. Results of simulated studies using sinusoidal wave...
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Multivariate Time Series Search
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...
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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...
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Neural Network Models for Time Series Forecasts
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 ...
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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...
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Geometric noise reduction for multivariate time series.
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.
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BRITS: Bidirectional Recurrent Imputation for Time Series
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...
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Efficient Algorithms for Segmentation of Item-Set Time Series
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.
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Studies on time series applications in environmental sciences
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. .
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Global Population Density Grid Time Series Estimates
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...
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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...
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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
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Revealing the timing of ocean stratification using remotely sensed ocean fronts
Miller, Peter I.; Loveday, Benjamin R.
2017-10-01
Stratification is of critical importance to the circulation, mixing and productivity of the ocean, and is expected to be modified by climate change. Stratification is also understood to affect the surface aggregation of pelagic fish and hence the foraging behaviour and distribution of their predators such as seabirds and cetaceans. Hence it would be prudent to monitor the stratification of the global ocean, though this is currently only possible using in situ sampling, profiling buoys or underwater autonomous vehicles. Earth observation (EO) 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 paper describes a novel algorithm that accumulates evidence for stratification from a sequence of oceanic front maps, and discusses preliminary results in comparison with in situ data and simulations from 3D hydrodynamic models. In certain regions, this method can reveal the timing of the seasonal onset and breakdown of stratification.
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Sensor-Generated Time Series Events: A Definition Language
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.
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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...
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National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0137446 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...
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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.
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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.
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Reconstruction of ensembles of coupled time-delay systems from time series.
Sysoev, I V; Prokhorov, M D; Ponomarenko, V I; Bezruchko, B P
2014-06-01
We propose a method to recover from time series the parameters of coupled time-delay systems and the architecture of couplings between them. The method is based on a reconstruction of model delay-differential equations and estimation of statistical significance of couplings. It can be applied to networks composed of nonidentical nodes with an arbitrary number of unidirectional and bidirectional couplings. We test our method on chaotic and periodic time series produced by model equations of ensembles of diffusively coupled time-delay systems in the presence of noise, and apply it to experimental time series obtained from electronic oscillators with delayed feedback coupled by resistors.
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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...
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Time series modeling in traffic safety research.
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.
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Time series prediction: statistical and neural techniques
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.
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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.
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The importance of time-stepping errors in ocean models
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.
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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...
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Time-series-analysis techniques applied to nuclear-material accounting
International Nuclear Information System (INIS)
Pike, D.H.; Morrison, G.W.; Downing, D.J.
1982-05-01
This document is designed to introduce the reader to the applications of Time Series Analysis techniques to Nuclear Material Accountability data. Time series analysis techniques are designed to extract information from a collection of random variables ordered by time by seeking to identify any trends, patterns, or other structure in the series. Since nuclear material accountability data is a time series, one can extract more information using time series analysis techniques than by using other statistical techniques. Specifically, the objective of this document is to examine the applicability of time series analysis techniques to enhance loss detection of special nuclear materials. An introductory section examines the current industry approach which utilizes inventory differences. The error structure of inventory differences is presented. Time series analysis techniques discussed include the Shewhart Control Chart, the Cumulative Summation of Inventory Differences Statistics (CUSUM) and the Kalman Filter and Linear Smoother
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National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164087 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...
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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.
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Li, William K. W.; Carmack, Eddy C.; McLaughlin, Fiona A.; Nelson, R. John; Williams, William J.
2013-10-01
The Arctic Ocean is changing rapidly but there are no long-term time series observations on the state of the phytoplankton community that could allow a link to be made from physical/chemical pressures to the impact on marine ecosystems. Here, we test the idea that space-for-time (SFT) substitution might predict temporal change in the Canada Basin premised on differences in the present state of phytoplankton in other geographic zones, specifically the ratio in the abundance of picophytoplankton to nanophytoplankton (Pico:Nano). We compared the change in Pico:Nano observed in the Canada Basin from 2004 to 2012 to the different average states of this ratio in 26 other ocean ecological regions. Our results show that as upper ocean nitrate concentration changed in the Canada Basin from year to year, the concomitant change in Pico:Nano was statistically commensurate with the difference that this ratio exhibits between Longhurst ecological provinces in relation to nitrate concentration. Lower average concentration of nitrate in the upper water column is associated with a higher value of Pico:Nano, a result consistent with resource control of phytoplankton size structure in the ocean. We suggest that SFT substitution allows an explanation of temporal progression from spatial pattern as a test of mechanism, but such statistical prediction is not necessarily a projection of future states.
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Directory of Open Access Journals (Sweden)
Antonio Pepe
2016-11-01
Full Text Available In this work, we investigate the temporal evolution of ground deformation affecting the ocean-reclaimed lands of the Shanghai (China megacity, from 2007 to 2016, by applying the Differential Synthetic Aperture Radar Interferometry (DInSAR technique known as the Small BAseline Subset (SBAS algorithm. For the analysis, we exploited two sets of non-time-overlapped synthetic aperture radar (SAR data, acquired from 2007 to 2010, by the ASAR/ENVISAT (C-band instrument, and from 2014 to 2016 by the X-band COSMO-SkyMed (CSK sensors. The long time gap (of about three years existing between the available C- and X-band datasets made the generation of unique displacement time-series more difficult. Nonetheless, this problem was successfully solved by benefiting from knowledge of time-dependent geotechnical models, which describe the temporal evolution of the expected deformation affecting Shanghai’s ocean-reclaimed platforms. The combined ENVISAT/CSK (vertical deformation time-series were analyzed to gain insight into the future evolution of displacement signals within the investigated area. As an outcome, we find that ocean-reclaimed lands in Shanghai experienced, between 2007 and 2016, average cumulative (vertical displacements extending down to 25 centimeters.
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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
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Robust Forecasting of Non-Stationary Time Series
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
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Meredith, Michael P.; Nicholls, Keith W.; Renfrew, Ian A.; Boehme, Lars; Biuw, Martin; Fedak, Mike
2011-07-01
A serendipitous >8-month time series of hydrographic properties was obtained from the vicinity of the South Orkney Islands, Southern Ocean, by tagging a southern elephant seal ( Mirounga leonina) on Signy Island with a Conductivity-Temperature-Depth/Satellite-Relay Data Logger (CTD-SRDL) in March 2007. Such a time series (including data from the austral autumn and winter) would have been extremely difficult to obtain via other means, and it illustrates with unprecedented temporal resolution the seasonal progression of upper-ocean water mass properties and stratification at this location. Sea ice production values of around 0.15-0.4 m month -1 for April to July were inferred from the progression of salinity, with significant levels still in September (around 0.2 m month -1). However, these values presume that advective processes have negligible effect on the salinity changes observed locally; this presumption is seen to be inappropriate in this case, and it is argued that the ice production rates inferred are better considered as "smeared averages" for the region of the northwestern Weddell Sea upstream from the South Orkneys. The impact of such advective effects is illustrated by contrasting the observed hydrographic series with the output of a one-dimensional model of the upper-ocean forced with local fluxes. It is found that the difference in magnitude between local (modelled) and regional (inferred) ice production is significant, with estimates differing by around a factor of two. A halo of markedly low sea ice concentration around the South Orkneys during the austral winter offers at least a partial explanation for this, since it enabled stronger atmosphere/ocean fluxes to persist and hence stronger ice production to prevail locally compared with the upstream region. The year of data collection was an El Niño year, and it is well-established that this phenomenon can impact strongly on the surface ocean and ice field in this sector of the Southern Ocean, thus
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Characterizing time series via complexity-entropy curves
Ribeiro, Haroldo V.; Jauregui, Max; Zunino, Luciano; Lenzi, Ervin K.
2017-06-01
The search for patterns in time series is a very common task when dealing with complex systems. This is usually accomplished by employing a complexity measure such as entropies and fractal dimensions. However, such measures usually only capture a single aspect of the system dynamics. Here, we propose a family of complexity measures for time series based on a generalization of the complexity-entropy causality plane. By replacing the Shannon entropy by a monoparametric entropy (Tsallis q entropy) and after considering the proper generalization of the statistical complexity (q complexity), we build up a parametric curve (the q -complexity-entropy curve) that is used for characterizing and classifying time series. Based on simple exact results and numerical simulations of stochastic processes, we show that these curves can distinguish among different long-range, short-range, and oscillating correlated behaviors. Also, we verify that simulated chaotic and stochastic time series can be distinguished based on whether these curves are open or closed. We further test this technique in experimental scenarios related to chaotic laser intensity, stock price, sunspot, and geomagnetic dynamics, confirming its usefulness. Finally, we prove that these curves enhance the automatic classification of time series with long-range correlations and interbeat intervals of healthy subjects and patients with heart disease.
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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
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Climate, carbon cycling, and deep-ocean ecosystems.
Smith, K L; Ruhl, H A; Bett, B J; Billett, D S M; Lampitt, R S; Kaufmann, R S
2009-11-17
Climate variation affects surface ocean processes and the production of organic carbon, which ultimately comprises the primary food supply to the deep-sea ecosystems that occupy approximately 60% of the Earth's surface. Warming trends in atmospheric and upper ocean temperatures, attributed to anthropogenic influence, have occurred over the past four decades. Changes in upper ocean temperature influence stratification and can affect the availability of nutrients for phytoplankton production. Global warming has been predicted to intensify stratification and reduce vertical mixing. Research also suggests that such reduced mixing will enhance variability in primary production and carbon export flux to the deep sea. The dependence of deep-sea communities on surface water production has raised important questions about how climate change will affect carbon cycling and deep-ocean ecosystem function. Recently, unprecedented time-series studies conducted over the past two decades in the North Pacific and the North Atlantic at >4,000-m depth have revealed unexpectedly large changes in deep-ocean ecosystems significantly correlated to climate-driven changes in the surface ocean that can impact the global carbon cycle. Climate-driven variation affects oceanic communities from surface waters to the much-overlooked deep sea and will have impacts on the global carbon cycle. Data from these two widely separated areas of the deep ocean provide compelling evidence that changes in climate can readily influence deep-sea processes. However, the limited geographic coverage of these existing time-series studies stresses the importance of developing a more global effort to monitor deep-sea ecosystems under modern conditions of rapidly changing climate.
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Observing climate change trends in ocean biogeochemistry: when and where.
Henson, Stephanie A; Beaulieu, Claudie; Lampitt, Richard
2016-04-01
Understanding the influence of anthropogenic forcing on the marine biosphere is a high priority. Climate change-driven trends need to be accurately assessed and detected in a timely manner. As part of the effort towards detection of long-term trends, a network of ocean observatories and time series stations provide high quality data for a number of key parameters, such as pH, oxygen concentration or primary production (PP). Here, we use an ensemble of global coupled climate models to assess the temporal and spatial scales over which observations of eight biogeochemically relevant variables must be made to robustly detect a long-term trend. We find that, as a global average, continuous time series are required for between 14 (pH) and 32 (PP) years to distinguish a climate change trend from natural variability. Regional differences are extensive, with low latitudes and the Arctic generally needing shorter time series (ocean surface. Our results present a quantitative framework for assessing the adequacy of current and future ocean observing networks for detection and monitoring of climate change-driven responses in the marine ecosystem. © 2016 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.
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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.
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Introduction to time series analysis and forecasting
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.
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Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance.
Liu, Yongli; Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao
2018-01-01
Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy.
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Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance
Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao
2018-01-01
Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy. PMID:29795600
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The foundations of modern time series analysis
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.
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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.
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Dakos, Vasilis; Carpenter, Stephen R.; Brock, William A.; Ellison, Aaron M.; Guttal, Vishwesha; Ives, Anthony R.; Kéfi, Sonia; Livina, Valerie; Seekell, David A.; van Nes, Egbert H.; Scheffer, Marten
2012-01-01
Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical transitions to a contrasting state can happen. Because critical transitions can occur unexpectedly and are difficult to manage, there is a need for methods that can be used to identify when a critical transition is approaching. Recent theory shows that we can identify the proximity of a system to a critical transition using a variety of so-called ‘early warning signals’, and successful empirical examples suggest a potential for practical applicability. However, while the range of proposed methods for predicting critical transitions is rapidly expanding, opinions on their practical use differ widely, and there is no comparative study that tests the limitations of the different methods to identify approaching critical transitions using time-series data. Here, we summarize a range of currently available early warning methods and apply them to two simulated time series that are typical of systems undergoing a critical transition. In addition to a methodological guide, our work offers a practical toolbox that may be used in a wide range of fields to help detect early warning signals of critical transitions in time series data. PMID:22815897
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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.
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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...
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Time-series prediction and applications a machine intelligence approach
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...
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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.
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Stochastic nature of series of waiting times
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 1/2
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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...
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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.
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Diurnal variability of surface fluxes at an oceanic station in the Bay of Bengal
Digital Repository Service at National Institute of Oceanography (India)
Sarma, Y.V.B.; Rao, D.P.
Diurnal variability of the surface fluxes and ocean heat content was studied using the time-series data on marine surface meteorological parameters and upper ocean temperature collected at an oceanic station in the Bay of Bengal during 1st to 8th...
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A novel weight determination method for time series data aggregation
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.
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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.
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Foundations of Sequence-to-Sequence Modeling for Time Series
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...
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Recurrent Neural Network Applications for Astronomical Time Series
Protopapas, Pavlos
2017-06-01
The benefits of good predictive models in astronomy lie in early event prediction systems and effective resource allocation. Current time series methods applicable to regular time series have not evolved to generalize for irregular time series. In this talk, I will describe two Recurrent Neural Network methods, Long Short-Term Memory (LSTM) and Echo State Networks (ESNs) for predicting irregular time series. Feature engineering along with a non-linear modeling proved to be an effective predictor. For noisy time series, the prediction is improved by training the network on error realizations using the error estimates from astronomical light curves. In addition to this, we propose a new neural network architecture to remove correlation from the residuals in order to improve prediction and compensate for the noisy data. Finally, I show how to set hyperparameters for a stable and performant solution correctly. In this work, we circumvent this obstacle by optimizing ESN hyperparameters using Bayesian optimization with Gaussian Process priors. This automates the tuning procedure, enabling users to employ the power of RNN without needing an in-depth understanding of the tuning procedure.
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Transition Icons for Time-Series Visualization and Exploratory Analysis.
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.
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Multifractal analysis of visibility graph-based Ito-related connectivity time series.
Czechowski, Zbigniew; Lovallo, Michele; Telesca, Luciano
2016-02-01
In this study, we investigate multifractal properties of connectivity time series resulting from the visibility graph applied to normally distributed time series generated by the Ito equations with multiplicative power-law noise. We show that multifractality of the connectivity time series (i.e., the series of numbers of links outgoing any node) increases with the exponent of the power-law noise. The multifractality of the connectivity time series could be due to the width of connectivity degree distribution that can be related to the exit time of the associated Ito time series. Furthermore, the connectivity time series are characterized by persistence, although the original Ito time series are random; this is due to the procedure of visibility graph that, connecting the values of the time series, generates persistence but destroys most of the nonlinear correlations. Moreover, the visibility graph is sensitive for detecting wide "depressions" in input time series.
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Mathematical foundations of time series analysis a concise introduction
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.
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Time series analysis in the social sciences the fundamentals
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
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Data imputation analysis for Cosmic Rays time series
Fernandes, R. C.; Lucio, P. S.; Fernandez, J. H.
2017-05-01
The occurrence of missing data concerning Galactic Cosmic Rays time series (GCR) is inevitable since loss of data is due to mechanical and human failure or technical problems and different periods of operation of GCR stations. The aim of this study was to perform multiple dataset imputation in order to depict the observational dataset. The study has used the monthly time series of GCR Climax (CLMX) and Roma (ROME) from 1960 to 2004 to simulate scenarios of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% and 90% of missing data compared to observed ROME series, with 50 replicates. Then, the CLMX station as a proxy for allocation of these scenarios was used. Three different methods for monthly dataset imputation were selected: AMÉLIA II - runs the bootstrap Expectation Maximization algorithm, MICE - runs an algorithm via Multivariate Imputation by Chained Equations and MTSDI - an Expectation Maximization algorithm-based method for imputation of missing values in multivariate normal time series. The synthetic time series compared with the observed ROME series has also been evaluated using several skill measures as such as RMSE, NRMSE, Agreement Index, R, R2, F-test and t-test. The results showed that for CLMX and ROME, the R2 and R statistics were equal to 0.98 and 0.96, respectively. It was observed that increases in the number of gaps generate loss of quality of the time series. Data imputation was more efficient with MTSDI method, with negligible errors and best skill coefficients. The results suggest a limit of about 60% of missing data for imputation, for monthly averages, no more than this. It is noteworthy that CLMX, ROME and KIEL stations present no missing data in the target period. This methodology allowed reconstructing 43 time series.
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Algorithm for Compressing Time-Series Data
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").
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National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0123694 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...
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National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0126498 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...
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National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0138191 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...
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National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0141106 contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA...
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Modeling of Volatility with Non-linear Time Series Model
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.
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Layered Ensemble Architecture for Time Series Forecasting.
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.
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Philip, Brendan T.; Denny, Alden R.; Solomon, Evan A.; Kelley, Deborah S.
2016-03-01
An estimated 500-2500 gigatons of methane carbon is sequestered in gas hydrate at continental margins and some of these deposits are associated with overlying methane seeps. To constrain the impact that seeps have on methane concentrations in overlying ocean waters and to characterize the bubble plumes that transport methane vertically into the ocean, water samples and time-series acoustic images were collected above Southern Hydrate Ridge (SHR), a well-studied hydrate-bearing seep site ˜90 km west of Newport, Oregon. These data were coregistered with robotic vehicle observations to determine the origin of the seeps, the plume rise heights above the seafloor, and the temporal variability in bubble emissions. Results show that the locations of seep activity and bubble release remained unchanged over the 3 year time-series investigation, however, the magnitude of gas release was highly variable on hourly time scales. Bubble plumes were detected to depths of 320-620 m below sea level (mbsl), in several cases exceeding the upper limit of hydrate stability by ˜190 m. For the first time, sustained gas release was imaged at the Pinnacle site and in-between the Pinnacle and the Summit area of venting, indicating that the subseafloor transport of fluid and gas is not restricted to the Summit at SHR, requiring a revision of fluid-flow models. Dissolved methane concentrations above background levels from 100 to 300 mbsl are consistent with long-term seep gas transport into the upper water column, which may lead to the build-up of seep-derived carbon in regional subsurface waters and to increases in associated biological activity.
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Use of Real Time Satellite Infrared and Ocean Color to Produce Ocean Products
Roffer, M. A.; Muller-Karger, F. E.; Westhaver, D.; Gawlikowski, G.; Upton, M.; Hall, C.
2014-12-01
Real-time data products derived from infrared and ocean color satellites are useful for several types of users around the world. Highly relevant applications include recreational and commercial fisheries, commercial towing vessel and other maritime and navigation operations, and other scientific and applied marine research. Uses of the data include developing sampling strategies for research programs, tracking of water masses and ocean fronts, optimizing ship routes, evaluating water quality conditions (coastal, estuarine, oceanic), and developing fisheries and essential fish habitat indices. Important considerations for users are data access and delivery mechanisms, and data formats. At this time, the data are being generated in formats increasingly available on mobile computing platforms, and are delivered through popular interfaces including social media (Facebook, Linkedin, Twitter and others), Google Earth and other online Geographical Information Systems, or are simply distributed via subscription by email. We review 30 years of applications and describe how we develop customized products and delivery mechanisms working directly with users. We review benefits and issues of access to government databases (NOAA, NASA, ESA), standard data products, and the conversion to tailored products for our users. We discuss advantages of different product formats and of the platforms used to display and to manipulate the data.
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A window-based time series feature extraction method.
Katircioglu-Öztürk, Deniz; Güvenir, H Altay; Ravens, Ursula; Baykal, Nazife
2017-10-01
This study proposes a robust similarity score-based time series feature extraction method that is termed as Window-based Time series Feature ExtraCtion (WTC). Specifically, WTC generates domain-interpretable results and involves significantly low computational complexity thereby rendering itself useful for densely sampled and populated time series datasets. In this study, WTC is applied to a proprietary action potential (AP) time series dataset on human cardiomyocytes and three precordial leads from a publicly available electrocardiogram (ECG) dataset. This is followed by comparing WTC in terms of predictive accuracy and computational complexity with shapelet transform and fast shapelet transform (which constitutes an accelerated variant of the shapelet transform). The results indicate that WTC achieves a slightly higher classification performance with significantly lower execution time when compared to its shapelet-based alternatives. With respect to its interpretable features, WTC has a potential to enable medical experts to explore definitive common trends in novel datasets. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Smith, Kenneth L; Ruhl, Henry A; Kahru, Mati; Huffard, Christine L; Sherman, Alana D
2013-12-03
The deep ocean, covering a vast expanse of the globe, relies almost exclusively on a food supply originating from primary production in surface waters. With well-documented warming of oceanic surface waters and conflicting reports of increasing and decreasing primary production trends, questions persist about how such changes impact deep ocean communities. A 24-y time-series study of sinking particulate organic carbon (food) supply and its utilization by the benthic community was conducted in the abyssal northeast Pacific (~4,000-m depth). Here we show that previous findings of food deficits are now punctuated by large episodic surpluses of particulate organic carbon reaching the sea floor, which meet utilization. Changing surface ocean conditions are translated to the deep ocean, where decadal peaks in supply, remineralization, and sequestration of organic carbon have broad implications for global carbon budget projections.
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National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0124303 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...
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National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0165030 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...
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National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0165088 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...
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National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0125087 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...
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National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0165089 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...
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National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0165031 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...
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National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0165363 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...
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National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0137412 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...
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National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0125267 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...
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Razavi, Saman; Vogel, Richard
2018-02-01
Prewhitening, the process of eliminating or reducing short-term stochastic persistence to enable detection of deterministic change, has been extensively applied to time series analysis of a range of geophysical variables. Despite the controversy around its utility, methodologies for prewhitening time series continue to be a critical feature of a variety of analyses including: trend detection of hydroclimatic variables and reconstruction of climate and/or hydrology through proxy records such as tree rings. With a focus on the latter, this paper presents a generalized approach to exploring the impact of a wide range of stochastic structures of short- and long-term persistence on the variability of hydroclimatic time series. Through this approach, we examine the impact of prewhitening on the inferred variability of time series across time scales. We document how a focus on prewhitened, residual time series can be misleading, as it can drastically distort (or remove) the structure of variability across time scales. Through examples with actual data, we show how such loss of information in prewhitened time series of tree rings (so-called "residual chronologies") can lead to the underestimation of extreme conditions in climate and hydrology, particularly droughts, reconstructed for centuries preceding the historical period.
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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.
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DEFF Research Database (Denmark)
Frigaard, Peter
2001-01-01
Book review: I. R. Young, Elsevier Ocean Engineering Series, Vol 2. Elsevier Science, Oxford, UK, 1999, 306 pages, hardbound, ISBN 0-08-043317-0, Dfl. 275,00 (US$ 139.50)......Book review: I. R. Young, Elsevier Ocean Engineering Series, Vol 2. Elsevier Science, Oxford, UK, 1999, 306 pages, hardbound, ISBN 0-08-043317-0, Dfl. 275,00 (US$ 139.50)...
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DTW-APPROACH FOR UNCORRELATED MULTIVARIATE TIME SERIES IMPUTATION
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...
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National Oceanic and Atmospheric Administration, Department of Commerce — Jason-3 is the fourth mission in U.S.-European series of satellite missions that measure the height of the ocean surface. Scheduled to launch in 2015, the mission...
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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.
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Trend time-series modeling and forecasting with neural networks.
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.
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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...
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Non-parametric characterization of long-term rainfall time series
Tiwari, Harinarayan; Pandey, Brij Kishor
2018-03-01
The statistical study of rainfall time series is one of the approaches for efficient hydrological system design. Identifying, and characterizing long-term rainfall time series could aid in improving hydrological systems forecasting. In the present study, eventual statistics was applied for the long-term (1851-2006) rainfall time series under seven meteorological regions of India. Linear trend analysis was carried out using Mann-Kendall test for the observed rainfall series. The observed trend using the above-mentioned approach has been ascertained using the innovative trend analysis method. Innovative trend analysis has been found to be a strong tool to detect the general trend of rainfall time series. Sequential Mann-Kendall test has also been carried out to examine nonlinear trends of the series. The partial sum of cumulative deviation test is also found to be suitable to detect the nonlinear trend. Innovative trend analysis, sequential Mann-Kendall test and partial cumulative deviation test have potential to detect the general as well as nonlinear trend for the rainfall time series. Annual rainfall analysis suggests that the maximum changes in mean rainfall is 11.53% for West Peninsular India, whereas the maximum fall in mean rainfall is 7.8% for the North Mountainous Indian region. The innovative trend analysis method is also capable of finding the number of change point available in the time series. Additionally, we have performed von Neumann ratio test and cumulative deviation test to estimate the departure from homogeneity. Singular spectrum analysis has been applied in this study to evaluate the order of departure from homogeneity in the rainfall time series. Monsoon season (JS) of North Mountainous India and West Peninsular India zones has higher departure from homogeneity and singular spectrum analysis shows the results to be in coherence with the same.
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Directory of Open Access Journals (Sweden)
S. Wu
2017-10-01
Full Text Available The ocean carbon cycle has a significant influence on global climate, and is commonly evaluated using time-series satellite-derived CO2 flux data. Location-aware and globe-based visualization is an important technique for analyzing and presenting the evolution of climate change. To achieve realistic simulation of the spatiotemporal dynamics of ocean carbon, a cloud-driven digital earth platform is developed to support the interactive analysis and display of multi-geospatial data, and an original visualization method based on our digital earth is proposed to demonstrate the spatiotemporal variations of carbon sinks and sources using time-series satellite data. Specifically, a volume rendering technique using half-angle slicing and particle system is implemented to dynamically display the released or absorbed CO2 gas. To enable location-aware visualization within the virtual globe, we present a 3D particlemapping algorithm to render particle-slicing textures onto geospace. In addition, a GPU-based interpolation framework using CUDA during real-time rendering is designed to obtain smooth effects in both spatial and temporal dimensions. To demonstrate the capabilities of the proposed method, a series of satellite data is applied to simulate the air-sea carbon cycle in the China Sea. The results show that the suggested strategies provide realistic simulation effects and acceptable interactive performance on the digital earth.
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Time Series Decomposition into Oscillation Components and Phase Estimation.
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.
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Sanders, Abram F. J.; Verstraeten, Willem W.; Kooreman, Maurits L.; van Leth, Thomas C.; Beringer, Jason; Joiner, Joanna
2016-01-01
A global, monthly averaged time series of Sun-induced Fluorescence (SiF), spanning January 2007 to June 2015, was derived from Metop-A Global Ozone Monitoring Experiment 2 (GOME-2) spectral measurements. Far-red SiF was retrieved using the filling-in of deep solar Fraunhofer lines and atmospheric absorption bands based on the general methodology described by Joiner et al, AMT, 2013. A Principal Component (PC) analysis of spectra over non-vegetated areas was performed to describe the effects of atmospheric absorption. Our implementation (SiF KNMI) is an independent algorithm and differs from the latest implementation of Joiner et al, AMT, 2013 (SiF NASA, v26), because we used desert reference areas for determining PCs (as opposed to cloudy ocean and some desert) and a wider fit window that covers water vapour and oxygen absorption bands (as opposed to only Fraunhofer lines). As a consequence, more PCs were needed (35 as opposed to 12). The two time series (SiF KNMI and SiF NASA, v26) correlate well (overall R of 0.78) except for tropical rain forests. Sensitivity experiments suggest the strong impact of the water vapour absorption band on retrieved SiF values. Furthermore, we evaluated the SiF time series with Gross Primary Productivity (GPP) derived from twelve flux towers in Australia. Correlations for individual towers range from 0.37 to 0.84. They are particularly high for managed biome types. In the de-seasonalized Australian SiF time series, the break of the Millennium Drought during local summer of 2010/2011 is clearly observed.
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Introduction to time series analysis and forecasting
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
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Multi-Scale Dissemination of Time Series Data
DEFF Research Database (Denmark)
Guo, Qingsong; Zhou, Yongluan; Su, Li
2013-01-01
In this paper, we consider the problem of continuous dissemination of time series data, such as sensor measurements, to a large number of subscribers. These subscribers fall into multiple subscription levels, where each subscription level is specified by the bandwidth constraint of a subscriber......, which is an abstract indicator for both the physical limits and the amount of data that the subscriber would like to handle. To handle this problem, we propose a system framework for multi-scale time series data dissemination that employs a typical tree-based dissemination network and existing time...
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RADON CONCENTRATION TIME SERIES MODELING AND APPLICATION DISCUSSION.
Stránský, V; Thinová, L
2017-11-01
In the year 2010 a continual radon measurement was established at Mladeč Caves in the Czech Republic using a continual radon monitor RADIM3A. In order to model radon time series in the years 2010-15, the Box-Jenkins Methodology, often used in econometrics, was applied. Because of the behavior of radon concentrations (RCs), a seasonal integrated, autoregressive moving averages model with exogenous variables (SARIMAX) has been chosen to model the measured time series. This model uses the time series seasonality, previously acquired values and delayed atmospheric parameters, to forecast RC. The developed model for RC time series is called regARIMA(5,1,3). Model residuals could be retrospectively compared with seismic evidence of local or global earthquakes, which occurred during the RCs measurement. This technique enables us to asses if continuously measured RC could serve an earthquake precursor. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
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Similarity estimators for irregular and age uncertain time series
Rehfeld, K.; Kurths, J.
2013-09-01
Paleoclimate time series are often irregularly sampled and age uncertain, which is an important technical challenge to overcome for successful reconstruction of past climate variability and dynamics. Visual comparison and interpolation-based linear correlation approaches have been used to infer dependencies from such proxy time series. While the first is subjective, not measurable and not suitable for the comparison of many datasets at a time, the latter introduces interpolation bias, and both face difficulties if the underlying dependencies are nonlinear. In this paper we investigate similarity estimators that could be suitable for the quantitative investigation of dependencies in irregular and age uncertain time series. We compare the Gaussian-kernel based cross correlation (gXCF, Rehfeld et al., 2011) and mutual information (gMI, Rehfeld et al., 2013) against their interpolation-based counterparts and the new event synchronization function (ESF). We test the efficiency of the methods in estimating coupling strength and coupling lag numerically, using ensembles of synthetic stalagmites with short, autocorrelated, linear and nonlinearly coupled proxy time series, and in the application to real stalagmite time series. In the linear test case coupling strength increases are identified consistently for all estimators, while in the nonlinear test case the correlation-based approaches fail. The lag at which the time series are coupled is identified correctly as the maximum of the similarity functions in around 60-55% (in the linear case) to 53-42% (for the nonlinear processes) of the cases when the dating of the synthetic stalagmite is perfectly precise. If the age uncertainty increases beyond 5% of the time series length, however, the true coupling lag is not identified more often than the others for which the similarity function was estimated. Age uncertainty contributes up to half of the uncertainty in the similarity estimation process. Time series irregularity
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Similarity estimators for irregular and age-uncertain time series
Rehfeld, K.; Kurths, J.
2014-01-01
Paleoclimate time series are often irregularly sampled and age uncertain, which is an important technical challenge to overcome for successful reconstruction of past climate variability and dynamics. Visual comparison and interpolation-based linear correlation approaches have been used to infer dependencies from such proxy time series. While the first is subjective, not measurable and not suitable for the comparison of many data sets at a time, the latter introduces interpolation bias, and both face difficulties if the underlying dependencies are nonlinear. In this paper we investigate similarity estimators that could be suitable for the quantitative investigation of dependencies in irregular and age-uncertain time series. We compare the Gaussian-kernel-based cross-correlation (gXCF, Rehfeld et al., 2011) and mutual information (gMI, Rehfeld et al., 2013) against their interpolation-based counterparts and the new event synchronization function (ESF). We test the efficiency of the methods in estimating coupling strength and coupling lag numerically, using ensembles of synthetic stalagmites with short, autocorrelated, linear and nonlinearly coupled proxy time series, and in the application to real stalagmite time series. In the linear test case, coupling strength increases are identified consistently for all estimators, while in the nonlinear test case the correlation-based approaches fail. The lag at which the time series are coupled is identified correctly as the maximum of the similarity functions in around 60-55% (in the linear case) to 53-42% (for the nonlinear processes) of the cases when the dating of the synthetic stalagmite is perfectly precise. If the age uncertainty increases beyond 5% of the time series length, however, the true coupling lag is not identified more often than the others for which the similarity function was estimated. Age uncertainty contributes up to half of the uncertainty in the similarity estimation process. Time series irregularity
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National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0142630 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...
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National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0119414 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...
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National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0132045 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...
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National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0165011 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...
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National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0124186 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...
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National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0130691 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...
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National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164081 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...
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National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164446 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...
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National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0165012 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...
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National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0155295 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...
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National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0121982 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...
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Robust Forecasting of Non-Stationary Time Series
Croux, C.; Fried, R.; Gijbels, I.; Mahieu, K.
2010-01-01
This paper proposes a robust forecasting method for non-stationary time series. The time series is modelled using non-parametric heteroscedastic regression, and fitted by a localized MM-estimator, combining high robustness and large efficiency. The proposed method is shown to produce reliable forecasts in the presence of outliers, non-linearity, and heteroscedasticity. In the absence of outliers, the forecasts are only slightly less precise than those based on a localized Least Squares estima...
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Time Series Econometrics for the 21st Century
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)…
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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 ...
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Ocean Color and Earth Science Data Records
Maritorena, S.
2014-12-01
The development of consistent, high quality time series of biogeochemical products from a single ocean color sensor is a difficult task that involves many aspects related to pre- and post-launch instrument calibration and characterization, stability monitoring and the removal of the contribution of the atmosphere which represents most of the signal measured at the sensor. It is even more challenging to build Climate Data Records (CDRs) or Earth Science Data Records (ESDRs) from multiple sensors as design, technology and methodologies (bands, spectral/spatial resolution, Cal/Val, algorithms) differ from sensor to sensor. NASA MEaSUREs, ESA Climate Change Initiative (CCI) and IOCCG Virtual Constellation are some of the underway efforts that investigate or produce ocean color CDRs or ESDRs from the recent and current global missions (SeaWiFS, MODIS, MERIS). These studies look at key aspects of the development of unified data records from multiple sensors, e.g. the concatenation of the "best" individual records vs. the merging of multiple records or band homogenization vs. spectral diversity. The pros and cons of the different approaches are closely dependent upon the overall science purpose of the data record and its temporal resolution. While monthly data are generally adequate for biogeochemical modeling or to assess decadal trends, higher temporal resolution data records are required to look into changes in phenology or the dynamics of phytoplankton blooms. Similarly, short temporal resolution (daily to weekly) time series may benefit more from being built through the merging of data from multiple sensors while a simple concatenation of data from individual sensors might be better suited for longer temporal resolution (e.g. monthly time series). Several Ocean Color ESDRs were developed as part of the NASA MEaSUREs project. Some of these time series are built by merging the reflectance data from SeaWiFS, MODIS-Aqua and Envisat-MERIS in a semi-analytical ocean color
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Dash, Y.; Mishra, S. K.; Panigrahi, B. K.
2017-12-01
Prediction of northeast/post monsoon rainfall which occur during October, November and December (OND) over Indian peninsula is a challenging task due to the dynamic nature of uncertain chaotic climate. It is imperative to elucidate this issue by examining performance of different machine leaning (ML) approaches. The prime objective of this research is to compare between a) statistical prediction using historical rainfall observations and global atmosphere-ocean predictors like Sea Surface Temperature (SST) and Sea Level Pressure (SLP) and b) empirical prediction based on a time series analysis of past rainfall data without using any other predictors. Initially, ML techniques have been applied on SST and SLP data (1948-2014) obtained from NCEP/NCAR reanalysis monthly mean provided by the NOAA ESRL PSD. Later, this study investigated the applicability of ML methods using OND rainfall time series for 1948-2014 and forecasted up to 2018. The predicted values of aforementioned methods were verified using observed time series data collected from Indian Institute of Tropical Meteorology and the result revealed good performance of ML algorithms with minimal error scores. Thus, it is found that both statistical and empirical methods are useful for long range climatic projections.
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Time Series Analysis of Insar Data: Methods and Trends
Osmanoglu, Batuhan; Sunar, Filiz; Wdowinski, Shimon; Cano-Cabral, Enrique
2015-01-01
Time series analysis of InSAR data has emerged as an important tool for monitoring and measuring the displacement of the Earth's surface. Changes in the Earth's surface can result from a wide range of phenomena such as earthquakes, volcanoes, landslides, variations in ground water levels, and changes in wetland water levels. Time series analysis is applied to interferometric phase measurements, which wrap around when the observed motion is larger than one-half of the radar wavelength. Thus, the spatio-temporal ''unwrapping" of phase observations is necessary to obtain physically meaningful results. Several different algorithms have been developed for time series analysis of InSAR data to solve for this ambiguity. These algorithms may employ different models for time series analysis, but they all generate a first-order deformation rate, which can be compared to each other. However, there is no single algorithm that can provide optimal results in all cases. Since time series analyses of InSAR data are used in a variety of applications with different characteristics, each algorithm possesses inherently unique strengths and weaknesses. In this review article, following a brief overview of InSAR technology, we discuss several algorithms developed for time series analysis of InSAR data using an example set of results for measuring subsidence rates in Mexico City.
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Anthropogenic CO2 in the oceans estimated using transit time distributions
International Nuclear Information System (INIS)
Waugh, D.W.; McNeil, B.I.
2006-01-01
The distribution of anthropogenic carbon (Cant) in the oceans is estimated using the transit time distribution (TTD) method applied to global measurements of chlorofluorocarbon-12 (CFC12). Unlike most other inference methods, the TTD method does not assume a single ventilation time and avoids the large uncertainty incurred by attempts to correct for the large natural carbon background in dissolved inorganic carbon measurements. The highest concentrations and deepest penetration of anthropogenic carbon are found in the North Atlantic and Southern Oceans. The estimated total inventory in 1994 is 134 Pg-C. To evaluate uncertainties the TTD method is applied to output from an ocean general circulation model (OGCM) and compared the results to the directly simulated Cant. Outside of the Southern Ocean the predicted Cant closely matches the directly simulated distribution, but in the Southern Ocean the TTD concentrations are biased high due to the assumption of 'constant disequilibrium'. The net result is a TTD overestimate of the global inventory by about 20%. Accounting for this bias and other centred uncertainties, an inventory range of 94-121 Pg-C is obtained. This agrees with the inventory of Sabine et al., who applied the DeltaC* method to the same data. There are, however, significant differences in the spatial distributions: The TTD estimates are smaller than DeltaC* in the upper ocean and larger at depth, consistent with biases expected in DeltaC* given its assumption of a single parcel ventilation time
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Interpretation of a compositional time series
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
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Capturing Structure Implicitly from Time-Series having Limited Data
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...
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Behling, Robert; Milewski, Robert; Chabrillat, Sabine
2018-06-01
This paper proposes the remote sensing time series approach WLMO (Water-Land MOnitor) to monitor spatiotemporal shoreline changes. The approach uses a hierarchical classification system based on temporal MNDWI-trajectories with the goal to accommodate typical uncertainties in remote sensing shoreline extraction techniques such as existence of clouds and geometric mismatches between images. Applied to a dense Landsat time series between 1984 and 2014 for the two Namibian coastal lagoons at Walvis Bay and Sandwich Harbour the WLMO was able to identify detailed accretion and erosion progressions at the sand spits forming these lagoons. For both lagoons a northward expansion of the sand spits of up to 1000 m was identified, which corresponds well with the prevailing northwards directed ocean current and wind processes that are responsible for the material transport along the shore. At Walvis Bay we could also show that in the 30 years of analysis the sand spit's width has decreased by more than a half from 750 m in 1984-360 m in 2014. This ongoing cross-shore erosion process is a severe risk for future sand spit breaching, which would expose parts of the lagoon and the city to the open ocean. One of the major advantages of WLMO is the opportunity to analyze detailed spatiotemporal shoreline changes. Thus, it could be shown that the observed long-term accretion and erosion processes underwent great variations over time and cannot a priori be assumed as linear processes. Such detailed spatiotemporal process patterns are a prerequisite to improve the understanding of the processes forming the Namibian shorelines. Moreover, the approach has also the potential to be used in other coastal areas, because the focus on MNDWI-trajectories allows the transfer to many multispectral satellite sensors (e.g. Sentinel-2, ASTER) available worldwide.
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Self-affinity in the dengue fever time series
Azevedo, S. M.; Saba, H.; Miranda, J. G. V.; Filho, A. S. Nascimento; Moret, M. A.
2016-06-01
Dengue is a complex public health problem that is common in tropical and subtropical regions. This disease has risen substantially in the last three decades, and the physical symptoms depict the self-affine behavior of the occurrences of reported dengue cases in Bahia, Brazil. This study uses detrended fluctuation analysis (DFA) to verify the scale behavior in a time series of dengue cases and to evaluate the long-range correlations that are characterized by the power law α exponent for different cities in Bahia, Brazil. The scaling exponent (α) presents different long-range correlations, i.e. uncorrelated, anti-persistent, persistent and diffusive behaviors. The long-range correlations highlight the complex behavior of the time series of this disease. The findings show that there are two distinct types of scale behavior. In the first behavior, the time series presents a persistent α exponent for a one-month period. For large periods, the time series signal approaches subdiffusive behavior. The hypothesis of the long-range correlations in the time series of the occurrences of reported dengue cases was validated. The observed self-affinity is useful as a forecasting tool for future periods through extrapolation of the α exponent behavior. This complex system has a higher predictability in a relatively short time (approximately one month), and it suggests a new tool in epidemiological control strategies. However, predictions for large periods using DFA are hidden by the subdiffusive behavior.
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On the plurality of times: disunified time and the A-series | Nefdt ...
African Journals Online (AJOL)
Then, I attempt to show that disunified time is a problem for a semantics based on the A-series since A-truthmakers are hard to come by in a universe of temporally disconnected time-series. Finally, I provide a novel argument showing that presentists should be particularly fearful of such a universe. South African Journal of ...
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Time-series modeling of long-term weight self-monitoring data.
Helander, Elina; Pavel, Misha; Jimison, Holly; Korhonen, Ilkka
2015-08-01
Long-term self-monitoring of weight is beneficial for weight maintenance, especially after weight loss. Connected weight scales accumulate time series information over long term and hence enable time series analysis of the data. The analysis can reveal individual patterns, provide more sensitive detection of significant weight trends, and enable more accurate and timely prediction of weight outcomes. However, long term self-weighing data has several challenges which complicate the analysis. Especially, irregular sampling, missing data, and existence of periodic (e.g. diurnal and weekly) patterns are common. In this study, we apply time series modeling approach on daily weight time series from two individuals and describe information that can be extracted from this kind of data. We study the properties of weight time series data, missing data and its link to individuals behavior, periodic patterns and weight series segmentation. Being able to understand behavior through weight data and give relevant feedback is desired to lead to positive intervention on health behaviors.
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Mariani, Luigi; Zavatti, Franco
2017-09-01
The spectral periods in North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO) and El Nino Southern Oscillation (ENSO) were analyzed and has been verified how they imprint a time series of European temperature anomalies (ETA), two European temperature time series and some phenological series (dates of cherry flowering and grapevine harvest). Such work had as reference scenario the linear causal chain MCTP (Macroscale Circulation→Temperature→Phenology of crops) that links oceanic and atmospheric circulation to surface air temperature which in its turn determines the earliness of appearance of phenological phases of plants. Results show that in the three segments of the MCTP causal chain are present cycles with the following central period in years (the % of the 12 analyzed time series interested by these cycles are in brackets): 65 (58%), 24 (58%), 20.5 (58%), 13.5 (50%), 11.5 (58%), 7.7 (75%), 5.5 (58%), 4.1 (58%), 3 (50%), 2.4 (67%). A comparison with short term spectral peaks of the four El Niño regions (nino1+2, nino3, nino3.4 and nino4) show that 10 of the 12 series are imprinted by periods around 2.3-2.4yr while 50-58% of the series are imprinted by El Niño periods of 4-4.2, 3.8-3.9, 3-3.1years. The analysis highlights the links among physical and biological variables of the climate system at scales that range from macro to microscale whose knowledge is crucial to reach a suitable understanding of the ecosystem behavior. The spectral analysis was also applied to a time series of spring - summer precipitation in order to evaluate the presence of peaks common with other 12 selected series with result substantially negative which brings us to rule out the existence of a linear causal chain MCPP (Macroscale Circulation→Precipitation→Phenology). Copyright © 2017 Elsevier B.V. All rights reserved.
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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 ...
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Miura, T.; Kato, A.; Wang, J.; Vargas, M.; Lindquist, M.
2015-12-01
Satellite vegetation index (VI) time series data serve as an important means to monitor and characterize seasonal changes of terrestrial vegetation and their interannual variability. It is, therefore, critical to ensure quality of such VI products and one method of validating VI product quality is cross-comparison with in situ flux tower measurements. In this study, we evaluated the quality of VI time series derived from Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (NPP) spacecraft by cross-comparison with in situ radiation flux measurements at select flux tower sites over North America and Europe. VIIRS is a new polar-orbiting satellite sensor series, slated to replace National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer in the afternoon overpass and to continue the highly-calibrated data streams initiated with Moderate Resolution Imaging Spectrometer of National Aeronautics and Space Administration's Earth Observing System. The selected sites covered a wide range of biomes, including croplands, grasslands, evergreen needle forest, woody savanna, and open shrublands. The two VIIRS indices of the Top-of-Atmosphere (TOA) Normalized Difference Vegetation Index (NDVI) and the atmospherically-corrected, Top-of-Canopy (TOC) Enhanced Vegetation Index (EVI) (daily, 375 m spatial resolution) were compared against the TOC NDVI and a two-band version of EVI (EVI2) calculated from tower radiation flux measurements, respectively. VIIRS and Tower VI time series showed comparable seasonal profiles across biomes with statistically significant correlations (> 0.60; p-value 0.95), with mean differences of 2.3 days and 5.0 days for the NDVI and the EVI, respectively. These results indicate that VIIRS VI time series can capture seasonal evolution of vegetated land surface as good as in situ radiometric measurements. Future studies that address biophysical or physiological interpretations
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Characterization of time series via Rényi complexity-entropy curves
Jauregui, M.; Zunino, L.; Lenzi, E. K.; Mendes, R. S.; Ribeiro, H. V.
2018-05-01
One of the most useful tools for distinguishing between chaotic and stochastic time series is the so-called complexity-entropy causality plane. This diagram involves two complexity measures: the Shannon entropy and the statistical complexity. Recently, this idea has been generalized by considering the Tsallis monoparametric generalization of the Shannon entropy, yielding complexity-entropy curves. These curves have proven to enhance the discrimination among different time series related to stochastic and chaotic processes of numerical and experimental nature. Here we further explore these complexity-entropy curves in the context of the Rényi entropy, which is another monoparametric generalization of the Shannon entropy. By combining the Rényi entropy with the proper generalization of the statistical complexity, we associate a parametric curve (the Rényi complexity-entropy curve) with a given time series. We explore this approach in a series of numerical and experimental applications, demonstrating the usefulness of this new technique for time series analysis. We show that the Rényi complexity-entropy curves enable the differentiation among time series of chaotic, stochastic, and periodic nature. In particular, time series of stochastic nature are associated with curves displaying positive curvature in a neighborhood of their initial points, whereas curves related to chaotic phenomena have a negative curvature; finally, periodic time series are represented by vertical straight lines.
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National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0129419 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...
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National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0120616 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...
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National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0150824 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...
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National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164155 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...
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National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0113519 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...
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National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164082 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...
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National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164786 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...
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National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0150823 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...
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National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164319 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...
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National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0116135 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...
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National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0138304 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...
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National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0152488 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...
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National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0164785 contains raw underway meteorological, navigational, optical, physical, profile and time series data logged by the Scientific Computer System...
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Quantifying Selection with Pool-Seq Time Series Data.
Taus, Thomas; Futschik, Andreas; Schlötterer, Christian
2017-11-01
Allele frequency time series data constitute a powerful resource for unraveling mechanisms of adaptation, because the temporal dimension captures important information about evolutionary forces. In particular, Evolve and Resequence (E&R), the whole-genome sequencing of replicated experimentally evolving populations, is becoming increasingly popular. Based on computer simulations several studies proposed experimental parameters to optimize the identification of the selection targets. No such recommendations are available for the underlying parameters selection strength and dominance. Here, we introduce a highly accurate method to estimate selection parameters from replicated time series data, which is fast enough to be applied on a genome scale. Using this new method, we evaluate how experimental parameters can be optimized to obtain the most reliable estimates for selection parameters. We show that the effective population size (Ne) and the number of replicates have the largest impact. Because the number of time points and sequencing coverage had only a minor effect, we suggest that time series analysis is feasible without major increase in sequencing costs. We anticipate that time series analysis will become routine in E&R studies. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
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Transformation-cost time-series method for analyzing irregularly sampled data.
Ozken, Ibrahim; Eroglu, Deniz; Stemler, Thomas; Marwan, Norbert; Bagci, G Baris; Kurths, Jürgen
2015-06-01
Irregular sampling of data sets is one of the challenges often encountered in time-series analysis, since traditional methods cannot be applied and the frequently used interpolation approach can corrupt the data and bias the subsequence analysis. Here we present the TrAnsformation-Cost Time-Series (TACTS) method, which allows us to analyze irregularly sampled data sets without degenerating the quality of the data set. Instead of using interpolation we consider time-series segments and determine how close they are to each other by determining the cost needed to transform one segment into the following one. Using a limited set of operations-with associated costs-to transform the time series segments, we determine a new time series, that is our transformation-cost time series. This cost time series is regularly sampled and can be analyzed using standard methods. While our main interest is the analysis of paleoclimate data, we develop our method using numerical examples like the logistic map and the Rössler oscillator. The numerical data allows us to test the stability of our method against noise and for different irregular samplings. In addition we provide guidance on how to choose the associated costs based on the time series at hand. The usefulness of the TACTS method is demonstrated using speleothem data from the Secret Cave in Borneo that is a good proxy for paleoclimatic variability in the monsoon activity around the maritime continent.
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Transformation-cost time-series method for analyzing irregularly sampled data
Ozken, Ibrahim; Eroglu, Deniz; Stemler, Thomas; Marwan, Norbert; Bagci, G. Baris; Kurths, Jürgen
2015-06-01
Irregular sampling of data sets is one of the challenges often encountered in time-series analysis, since traditional methods cannot be applied and the frequently used interpolation approach can corrupt the data and bias the subsequence analysis. Here we present the TrAnsformation-Cost Time-Series (TACTS) method, which allows us to analyze irregularly sampled data sets without degenerating the quality of the data set. Instead of using interpolation we consider time-series segments and determine how close they are to each other by determining the cost needed to transform one segment into the following one. Using a limited set of operations—with associated costs—to transform the time series segments, we determine a new time series, that is our transformation-cost time series. This cost time series is regularly sampled and can be analyzed using standard methods. While our main interest is the analysis of paleoclimate data, we develop our method using numerical examples like the logistic map and the Rössler oscillator. The numerical data allows us to test the stability of our method against noise and for different irregular samplings. In addition we provide guidance on how to choose the associated costs based on the time series at hand. The usefulness of the TACTS method is demonstrated using speleothem data from the Secret Cave in Borneo that is a good proxy for paleoclimatic variability in the monsoon activity around the maritime continent.
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A multidisciplinary database for geophysical time series management
Montalto, P.; Aliotta, M.; Cassisi, C.; Prestifilippo, M.; Cannata, A.
2013-12-01
The variables collected by a sensor network constitute a heterogeneous data source that needs to be properly organized in order to be used in research and geophysical monitoring. With the time series term we refer to a set of observations of a given phenomenon acquired sequentially in time. When the time intervals are equally spaced one speaks of period or sampling frequency. Our work describes in detail a possible methodology for storage and management of time series using a specific data structure. We designed a framework, hereinafter called TSDSystem (Time Series Database System), in order to acquire time series from different data sources and standardize them within a relational database. The operation of standardization provides the ability to perform operations, such as query and visualization, of many measures synchronizing them using a common time scale. The proposed architecture follows a multiple layer paradigm (Loaders layer, Database layer and Business Logic layer). Each layer is specialized in performing particular operations for the reorganization and archiving of data from different sources such as ASCII, Excel, ODBC (Open DataBase Connectivity), file accessible from the Internet (web pages, XML). In particular, the loader layer performs a security check of the working status of each running software through an heartbeat system, in order to automate the discovery of acquisition issues and other warning conditions. Although our system has to manage huge amounts of data, performance is guaranteed by using a smart partitioning table strategy, that keeps balanced the percentage of data stored in each database table. TSDSystem also contains modules for the visualization of acquired data, that provide the possibility to query different time series on a specified time range, or follow the realtime signal acquisition, according to a data access policy from the users.
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National Oceanic and Atmospheric Administration, Department of Commerce — This archival package consists of time series of physical and biogeochemical data from 8 locations (8 tubes) in Pool 100 as well as temperature time series from Pool...
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Modeling financial time series with S-plus
Zivot, Eric
2003-01-01
The field of financial econometrics has exploded over the last decade This book represents an integration of theory, methods, and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics This is the first book to show the power of S-PLUS for the analysis of time series data It is written for researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts Eric Zivot is an associate professor and Gary Waterman Distinguished Scholar in the Economics Department at the University of Washington, and is co-director of the nascent Professional Master's Program in Computational Finance He regularly teaches courses on econometric theory, financial econometrics and time series econometrics, and is the recipient of the He...
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Detection of anthropogenic climate change in satellite records of ocean chlorophyll and productivity
Directory of Open Access Journals (Sweden)
S. A. Henson
2010-02-01
Full Text Available Global climate change is predicted to alter the ocean's biological productivity. But how will we recognise the impacts of climate change on ocean productivity? The most comprehensive information available on its global distribution comes from satellite ocean colour data. Now that over ten years of satellite-derived chlorophyll and productivity data have accumulated, can we begin to detect and attribute climate change-driven trends in productivity? Here we compare recent trends in satellite ocean colour data to longer-term time series from three biogeochemical models (GFDL, IPSL and NCAR. We find that detection of climate change-driven trends in the satellite data is confounded by the relatively short time series and large interannual and decadal variability in productivity. Thus, recent observed changes in chlorophyll, primary production and the size of the oligotrophic gyres cannot be unequivocally attributed to the impact of global climate change. Instead, our analyses suggest that a time series of ~40 years length is needed to distinguish a global warming trend from natural variability. In some regions, notably equatorial regions, detection times are predicted to be shorter (~20–30 years. Analysis of modelled chlorophyll and primary production from 2001–2100 suggests that, on average, the climate change-driven trend will not be unambiguously separable from decadal variability until ~2055. Because the magnitude of natural variability in chlorophyll and primary production is larger than, or similar to, the global warming trend, a consistent, decades-long data record must be established if the impact of climate change on ocean productivity is to be definitively detected.
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Application of Time Series Analysis in Determination of Lag Time in Jahanbin Basin
Directory of Open Access Journals (Sweden)
Seied Yahya Mirzaee
2005-11-01
One of the important issues that have significant role in study of hydrology of basin is determination of lag time. Lag time has significant role in hydrological studies. Quantity of rainfall related lag time depends on several factors, such as permeability, vegetation cover, catchments slope, rainfall intensity, storm duration and type of rain. Determination of lag time is important parameter in many projects such as dam design and also water resource studies. Lag time of basin could be calculated using various methods. One of these methods is time series analysis of spectral density. The analysis is based on fouries series. The time series is approximated with Sinuous and Cosines functions. In this method harmonically significant quantities with individual frequencies are presented. Spectral density under multiple time series could be used to obtain basin lag time for annual runoff and short-term rainfall fluctuation. A long lag time could be due to snowmelt as well as melting ice due to rainfalls in freezing days. In this research the lag time of Jahanbin basin has been determined using spectral density method. The catchments is subjected to both rainfall and snowfall. For short term rainfall fluctuation with a return period 2, 3, 4 months, the lag times were found 0.18, 0.5 and 0.083 month, respectively.
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Modeling Time Series Data for Supervised Learning
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…
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Empirical method to measure stochasticity and multifractality in nonlinear time series
Lin, Chih-Hao; Chang, Chia-Seng; Li, Sai-Ping
2013-12-01
An empirical algorithm is used here to study the stochastic and multifractal nature of nonlinear time series. A parameter can be defined to quantitatively measure the deviation of the time series from a Wiener process so that the stochasticity of different time series can be compared. The local volatility of the time series under study can be constructed using this algorithm, and the multifractal structure of the time series can be analyzed by using this local volatility. As an example, we employ this method to analyze financial time series from different stock markets. The result shows that while developed markets evolve very much like an Ito process, the emergent markets are far from efficient. Differences about the multifractal structures and leverage effects between developed and emergent markets are discussed. The algorithm used here can be applied in a similar fashion to study time series of other complex systems.
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Martin, T.; Reintges, A.; Park, W.; Latif, M.
2014-12-01
Many current coupled global climate models simulate open ocean deep convection in the Southern Ocean as a recurring event with time scales ranging from a few years to centennial (de Lavergne et al., 2014, Nat. Clim. Ch.). The only observation of such event, however, was the occurrence of the Weddell Polynya in the mid-1970s, an open water area of 350 000 km2 within the Antarctic sea ice in three consecutive winters. Both the wide range of modeled frequency of occurrence and the absence of deep convection in the Weddell Sea highlights the lack of understanding concerning the phenomenon. Nevertheless, simulations indicate that atmospheric and oceanic responses to the cessation of deep convection in the Southern Ocean include a strengthening of the low-level atmospheric circulation over the Southern Ocean (increasing SAM index) and a reduction in the export of Antarctic Bottom Water (AABW), potentially masking the regional effects of global warming (Latif et al., 2013, J. Clim.; Martin et al., 2014, Deep Sea Res. II). It is thus of great importance to enhance our understanding of Southern Ocean deep convection and clarify the associated time scales. In two multi-millennial simulations with the Kiel Climate Model (KCM, ECHAM5 T31 atmosphere & NEMO-LIM2 ~2˚ ocean) we showed that the deep convection is driven by strong oceanic warming at mid-depth periodically overriding the stabilizing effects of precipitation and ice melt (Martin et al., 2013, Clim. Dyn.). Sea ice thickness also affects location and duration of the deep convection. A new control simulation, in which, amongst others, the atmosphere grid resolution is changed to T42 (~2.8˚), yields a faster deep convection flip-flop with a period of 80-100 years and a weaker but still significant global climate response similar to CMIP5 simulations. While model physics seem to affect the time scale and intensity of the phenomenon, the driving mechanism is a rather robust feature. Finally, we compare the atmospheric and
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Biosilicification Drives a Decline of Dissolved Si in the Oceans through Geologic Time
Directory of Open Access Journals (Sweden)
Daniel J. Conley
2017-12-01
Full Text Available Biosilicification has driven variation in the global Si cycle over geologic time. The evolution of different eukaryotic lineages that convert dissolved Si (DSi into mineralized structures (higher plants, siliceous sponges, radiolarians, and diatoms has driven a secular decrease in DSi in the global ocean leading to the low DSi concentrations seen today. Recent studies, however, have questioned the timing previously proposed for the DSi decreases and the concentration changes through deep time, which would have major implications for the cycling of carbon and other key nutrients in the ocean. Here, we combine relevant genomic data with geological data and present new hypotheses regarding the impact of the evolution of biosilicifying organisms on the DSi inventory of the oceans throughout deep time. Although there is no fossil evidence for true silica biomineralization until the late Precambrian, the timing of the evolution of silica transporter genes suggests that bacterial silicon-related metabolism has been present in the oceans since the Archean with eukaryotic silicon metabolism already occurring in the Neoproterozoic. We hypothesize that biological processes have influenced oceanic DSi concentrations since the beginning of oxygenic photosynthesis.
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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...
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Clinical time series prediction: Toward a hierarchical dynamical system framework.
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.
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Clinical time series prediction: towards a hierarchical dynamical system framework
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
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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
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Characterizing time series: when Granger causality triggers complex networks
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.
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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)
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Reading, Michael J.; Santos, Isaac R.; Maher, Damien T.; Jeffrey, Luke C.; Tait, Douglas R.
2017-07-01
The oceans are a major source of the potent greenhouse gas nitrous oxide (N2O) to the atmosphere. However, little information is available on how estuaries and the coastal ocean may contribute to N2O budgets, and on the drivers of N2O in aquatic environments. This study utilised five time series stations along the freshwater to marine continuum in a sub-tropical estuary in Australia (Coffs Creek, Australia). Each time series station captured N2O, radon (222Rn, a natural submarine groundwater discharge tracer), dissolved nitrogen, and dissolved organic carbon (DOC) concentrations for a minimum of 25 h. The use of automated time series observations enabled spatial and tidal-scale variability of N2O to be captured. Groundwater was highly enriched in N2O (up to 306 nM) compared to the receiving surface water. Dissolved N2O supersaturation as high as 386% (27.4 nM) was observed in the upstream freshwater and brackish water areas which represented only a small (∼13%) proportion of the total estuary area. A large area of N2O undersaturation (as low as 53% or 3.9 nM) was observed in the mangrove-dominated lower estuary. This undersaturated area likely resulted from N2O consumption due to nitrate/nitrite (NOx) limitation in mangrove sediments subject to shallow porewater exchange. Overall, the estuary was a minor source of N2O to the atmosphere as the lower mangrove-dominated estuary sink of N2O counteracted groundwater-dominated source of N2O in the upper estuary. Average area-weighted N2O fluxes at the water-air interface approached zero (0.2-0.7 μmol m-2 d-1, depending on piston velocity model used), and were much lower than nitrogen-rich Northern Hemisphere estuaries that are considered large sources of N2O to the atmosphere. This study revealed a temporally and spatially diverse estuary, with areas of N2O production and consumption related to oxygen and total dissolved nitrogen availability, submarine groundwater discharge, and uptake within mangroves.
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Multivariate time series analysis with R and financial applications
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
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Measurements of spatial population synchrony: influence of time series transformations.
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.
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Hill, Emma M.; Ponte, Rui M.; Davis, James L.
2007-01-01
Comparison of monthly mean tide-gauge time series to corresponding model time series based on a static inverted barometer (IB) for pressure-driven fluctuations and a ocean general circulation model (OM) reveals that the combined model successfully reproduces seasonal and interannual changes in relative sea level at many stations. Removal of the OM and IB from the tide-gauge record produces residual time series with a mean global variance reduction of 53%. The OM is mis-scaled for certain regions, and 68% of the residual time series contain a significant seasonal variability after removal of the OM and IB from the tide-gauge data. Including OM admittance parameters and seasonal coefficients in a regression model for each station, with IB also removed, produces residual time series with mean global variance reduction of 71%. Examination of the regional improvement in variance caused by scaling the OM, including seasonal terms, or both, indicates weakness in the model at predicting sea-level variation for constricted ocean regions. The model is particularly effective at reproducing sea-level variation for stations in North America, Europe, and Japan. The RMS residual for many stations in these areas is 25-35 mm. The production of "cleaner" tide-gauge time series, with oceanographic variability removed, is important for future analysis of nonsecular and regionally differing sea-level variations. Understanding the ocean model's strengths and weaknesses will allow for future improvements of the model.
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International Comprehensive Ocean Atmosphere Data Set (ICOADS) in Near-Real Time (NRT)
National Oceanic and Atmospheric Administration, Department of Commerce — The International Comprehensive Ocean-Atmosphere Data Set (ICOADS) Near-Real-Time (NRT) product is an extension of the official ICOADS dataset with preliminary...
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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...
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Stochastic time series analysis of hydrology data for water resources
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.
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Neural network versus classical time series forecasting models
Nor, Maria Elena; Safuan, Hamizah Mohd; Shab, Noorzehan Fazahiyah Md; Asrul, Mohd; Abdullah, Affendi; Mohamad, Nurul Asmaa Izzati; Lee, Muhammad Hisyam
2017-05-01
Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network and classical time series forecasting method namely seasonal autoregressive integrated moving average models was being compared by utilizing gold price data. Moreover, the effect of different data preprocessing on the forecast performance of neural network being examined. The forecast accuracy was evaluated using mean absolute deviation, root mean square error and mean absolute percentage error. It was found that ANN produced the most accurate forecast when Box-Cox transformation was used as data preprocessing.
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Nonlinear time series analysis of the human electrocardiogram
International Nuclear Information System (INIS)
Perc, Matjaz
2005-01-01
We analyse the human electrocardiogram with simple nonlinear time series analysis methods that are appropriate for graduate as well as undergraduate courses. In particular, attention is devoted to the notions of determinism and stationarity in physiological data. We emphasize that methods of nonlinear time series analysis can be successfully applied only if the studied data set originates from a deterministic stationary system. After positively establishing the presence of determinism and stationarity in the studied electrocardiogram, we calculate the maximal Lyapunov exponent, thus providing interesting insights into the dynamics of the human heart. Moreover, to facilitate interest and enable the integration of nonlinear time series analysis methods into the curriculum at an early stage of the educational process, we also provide user-friendly programs for each implemented method
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Wang, Jin; Sun, Xiangping; Nahavandi, Saeid; Kouzani, Abbas; Wu, Yuchuan; She, Mary
2014-11-01
Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
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National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0122396 contains raw underway meteorological, navigational, optical, physical, profile, time series and trawl data logged by the Scientific Computer...
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National Oceanic and Atmospheric Administration, Department of Commerce — NCEI Accession 0150695 contains raw underway meteorological, navigational, optical, physical and time series data logged by the Scientific Computer System (SCS)...
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Hidden Markov Models for Time Series An Introduction Using R
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.
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Constructing ordinal partition transition networks from multivariate time series.
Zhang, Jiayang; Zhou, Jie; Tang, Ming; Guo, Heng; Small, Michael; Zou, Yong
2017-08-10
A growing number of algorithms have been proposed to map a scalar time series into ordinal partition transition networks. However, most observable phenomena in the empirical sciences are of a multivariate nature. We construct ordinal partition transition networks for multivariate time series. This approach yields weighted directed networks representing the pattern transition properties of time series in velocity space, which hence provides dynamic insights of the underling system. Furthermore, we propose a measure of entropy to characterize ordinal partition transition dynamics, which is sensitive to capturing the possible local geometric changes of phase space trajectories. We demonstrate the applicability of pattern transition networks to capture phase coherence to non-coherence transitions, and to characterize paths to phase synchronizations. Therefore, we conclude that the ordinal partition transition network approach provides complementary insight to the traditional symbolic analysis of nonlinear multivariate time series.
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Permutation entropy of finite-length white-noise time series.
Little, Douglas J; Kane, Deb M
2016-08-01
Permutation entropy (PE) is commonly used to discriminate complex structure from white noise in a time series. While the PE of white noise is well understood in the long time-series limit, analysis in the general case is currently lacking. Here the expectation value and variance of white-noise PE are derived as functions of the number of ordinal pattern trials, N, and the embedding dimension, D. It is demonstrated that the probability distribution of the white-noise PE converges to a χ^{2} distribution with D!-1 degrees of freedom as N becomes large. It is further demonstrated that the PE variance for an arbitrary time series can be estimated as the variance of a related metric, the Kullback-Leibler entropy (KLE), allowing the qualitative N≫D! condition to be recast as a quantitative estimate of the N required to achieve a desired PE calculation precision. Application of this theory to statistical inference is demonstrated in the case of an experimentally obtained noise series, where the probability of obtaining the observed PE value was calculated assuming a white-noise time series. Standard statistical inference can be used to draw conclusions whether the white-noise null hypothesis can be accepted or rejected. This methodology can be applied to other null hypotheses, such as discriminating whether two time series are generated from different complex system states.
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Multiresolution analysis of Bursa Malaysia KLCI time series
Ismail, Mohd Tahir; Dghais, Amel Abdoullah Ahmed
2017-05-01
In general, a time series is simply a sequence of numbers collected at regular intervals over a period. Financial time series data processing is concerned with the theory and practice of processing asset price over time, such as currency, commodity data, and stock market data. The primary aim of this study is to understand the fundamental characteristics of selected financial time series by using the time as well as the frequency domain analysis. After that prediction can be executed for the desired system for in sample forecasting. In this study, multiresolution analysis which the assist of discrete wavelet transforms (DWT) and maximal overlap discrete wavelet transform (MODWT) will be used to pinpoint special characteristics of Bursa Malaysia KLCI (Kuala Lumpur Composite Index) daily closing prices and return values. In addition, further case study discussions include the modeling of Bursa Malaysia KLCI using linear ARIMA with wavelets to address how multiresolution approach improves fitting and forecasting results.