Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem — (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations. Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual disk access for only less than 5% of the observations. To the best of our knowledge, this is the first flexible MTS search algorithm capable of subsequence search on any subset of variables. Moreover, MTS subsequence search has never been attempted on datasets of the size we have used in this paper.

Modal Service data and Safety & Security (S&S) public transit time series data delineated by transit/agency/mode/year/month. Includes all Full Reporters--transit agencies operating modes with more than 30 vehicles in maximum service--to the National Transit Database (NTD). This dataset will be updated monthly.

The monthly ridership data is released one month after the month in which the service is provided. Records with null monthly service data reflect late reporting.

The S&S statistics provided include both Major and Non-Major Events where applicable. Events occurring in the past three months are excluded from the corresponding monthly ridership rows in this dataset while they undergo validation. This dataset is the only NTD publication in which all Major and Non-Major S&S data are presented without any adjustment for historical continuity.

Rosalia Times Series Database

The BOKU (University of Natural Resources and Life Sciences Vienna) university demonstration forest Rosalia with an area of 950 ha has been used for research and education since 1875. In 2013 – upon an initiative of a group of researchers in various disciplines – it was decided to extend the so far mainly forestry oriented activities by implementing a hydrological experimental research watershed. The overall objective is to collect data that support the study of transport processes in the system of soil, water, plants and atmosphere. More specifically, emphasis is on bridging the gap between point related measurements and effective values and parameters required for modelling flow and transport processes in watersheds.

2 Objectives

The main objectives for the research watershed are

• to collect data that support the study of transport processes in the system of soil, water, plants and atmosphere
• emphasis is on bridging the gap between point related measurements and effective values and parameters for modelling watersheds of various sizes
• to generate comprehensive reference information for research projects on future management and climate change impacts

Operation is planned for a period of at least 10 years using only internal resources of the university, to avoid potential interruptions due to project-based short-term availability of personal and financial resources.

The objective of this article is to present the research watershed, the data collected and to make these data accessible to the research community.

The U.S. Geological Survey (USGS) Oceanographic Time-Series Measurements Database contains oceanographic observations made as part of studies designed to increase understanding of sediment transport processes and associated ocean dynamics. This report describes the instrumentation and platforms used to make the measurements; the methods used to process and apply quality-control criteria and archive the data; and the data storage format. The report also includes instructions on how to access the data from the online database at https://stellwagen.er.usgs.gov/.

Movements in the volume of production for the UK production industries: manufacturing, mining and quarrying, energy supply, and water and waste management. Figures are seasonally adjusted.

The NREL PVDAQ is a large-scale time-series database containing system metadata and performance data from a variety of experimental PV sites and commercial public PV sites. The datasets are used to perform on-going performance and degradation analysis. Some of the sets can exhibit common elements that effect PV performance (e.g. soiling). The dataset consists of a series of files devoted to each of the systems and an associated set of metadata information that explains details about the system hardware and the site geo-location. Some system datasets also include environmental sensors that cover irradiance, temperatures, wind speeds, and precipitation at the site.

The dataset included with this article contains three files describing and defining the sample and variables for VAT impact, and Excel file 1 consists of all raw and filtered data for the variables for the panel data sample. Excel file 2 depicts time-series and cross-sectional data for nonfinancial firms listed on the Saudi market for the second and third quarters of 2019 and the third and fourth quarters of 2020. Excel file 3 presents the raw material of variables used in measuring the company's profitability of the panel data sample

The Ocean Data Inventory database is an inventory of all of the oceanographic time series data held by the Ocean Science Division at the Bedford Institute of Oceanography. The data archive includes about 5800 current meter and acoustic doppler time series, 4500 coastal temperature time series from thermographs, as well as a small number (200) of tide gauges. Many of the current meters also have temperature and salinity sensors. The area for which there are data is roughly defined as the North Atlantic and Arctic from 30° - 82° N, although there are some minor amounts of data from other parts of the world. The time period is from 1960 to present. The database is updated on a regular basis.

The 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 Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) data set. The grids were created by using rates of population change between decades from the coarser resolution History Database of the Global Environment (HYDE) database to back-cast the GRUMPv1 population density grids. Mismatches between the spatial extent of the HYDE calculated rates and GRUMPv1 population data were resolved via infilling rate cells based on a focal mean of values. Finally, the grids were adjusted so that the population totals for each country equaled the UN World Population Prospects (2008 Revision) estimates for that country for the respective year (1970, 1980, 1990, and 2000). These data do not represent census observations for the years prior to 2000, and therefore can at best be thought of as estimations of the populations in given locations. The population grids are consistent internally within the time series, but are not recommended for use in creating longer time series with any other population grids, including GRUMPv1, Gridded Population of the World, Version 4 (GPWv4), or non-SEDAC developed population grids. These population grids served as an input to SEDAC's Global Estimated Net Migration Grids by Decade: 1970-2000 data set.

Midyear population estimates and projections for all countries and areas of the world with a population of 5,000 or more // Source: U.S. Census Bureau, Population Division, International Programs Center// Note: Total population available from 1950 to 2100 for 227 countries and areas. Other demographic variables available from base year to 2100. Base year varies by country and therefore data are not available for all years for all countries. For the United States, total population available from 1950-2060, and other demographic variables available from 1980-2060. See methodology at https://www.census.gov/programs-surveys/international-programs/about/idb.html

Comprehensive database of time series covering measures of inflation data for the UK including CPIH, CPI and RPI.

Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem — (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations. Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual disk access for only less than 5% of the observations. To the best of our knowledge, this is the first flexible MTS search algorithm capable of subsequence search on any subset of variables. Moreover, MTS subsequence search has never been attempted on datasets of the size we have used in this paper.

The Uniform Crime Reports National Time-Series Data, 1967-1980, include detailed criminal offense and clearance information submitted monthly by over 3,000 consistently reporting law enforcement agencies in the United States. These data, provided in annually pooled cross-sections, were processed at the Center for Applied Social Research, Northeastern University, Boston, Massachusetts to produce easily accessible and highly reliable time-series data on officially reported crime. Originally provided by the Federal Bureau of Investigation (FBI), these data exclude Uniform Crime Report (UCR) data from infrequently reporting law enforcement agencies. In general, only those agencies that submitted ten or more monthly reports in every year during 1967 through 1980 are included in this collection. The data include detailed breakdowns of offenses and clearances taken from disaggregated UCR Return A tapes. Of particular interest are weapon-specific robbery and assault variables, types of rape, burglary, larceny, and motor vehicle theft, and clearances by arrest (or other exceptional means) of adults and juveniles for each offense sub-type. Both monthly and annual counts of these are available. Finally, as an aid to the user, each agency is identified by its FBI "ORI Code" as well as a sequential case number produced and documented by ICPSR in the codebook's appendix. Cases also may be identified by geographic region, state, SMSA, county, population size and group, and frequency of reporting.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since the first reported coronavirus case in Washington State on Jan. 21, 2020, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

Holocene climate reconstructions are useful for understanding the diverse features and spatial heterogeneity of past and future climate change. Here we present a database of western North American Holocene paleoclimate records. The database gathers paleoclimate time series from 184 terrestrial and marine sites, including 381 individual proxy records. The records span at least 4000 of the last 12 000 years (median duration of 10 725 years) and have been screened for resolution, chronologic control, and climate sensitivity. Records were included that reflect temperature, hydroclimate, or circulation features. The database is shared in the machine readable Linked Paleo Data (LiPD) format and includes geochronologic data for generating site-level time-uncertain ensembles. This publicly accessible and curated collection of proxy paleoclimate records will have wide research applications, including, for example, investigations of the primary features of ocean–atmospheric circulation along the eastern margin of the North Pacific and the latitudinal response of climate to orbital changes.

The size and share of the market is categorized based on Type (Relational Database Server, Time Series Database Server, Object Oriented Database Server, Navigational Database Server) and Application (Education, Financial Services, Healthcare, Government, Life Sciences, Manufacturing, Retail, Utilities, Others) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

Water characteristics of Yaquina Bay and Hatfield Marine Science Center's in-building seawater system, measured every six minutes since 1988. Tide height data is collected at the HMSC/OSU boat dock. Water temperature and salinity data is collected at the HMSC/OSU pumphouse dock.

The Economic Indicator Database Search provides easy to access time series data for the 12 Economic Indicators produced by the Census Bureau. Data users (business analysts, the press, economic researchers) can use the database for quicker access to the data they need for modeling, trend analyses, and other research.

The National Database for Clinical Trials Related to Mental Illness (NDCT) is an extensible informatics platform for relevant data at all levels of biological and behavioral organization (molecules, genes, neural tissue, behavioral, social and environmental interactions) and for all data types (text, numeric, image, time series, etc.) related to clinical trials funded by the National Institute of Mental Health. Sharing data, associated tools, methodologies and results, rather than just summaries or interpretations, accelerates research progress. Community-wide sharing requires common data definitions and standards, as well as comprehensive and coherent informatics approaches for the sharing of de-identified human subject research data. Built on the National Database for Autism Research (NDAR) informatics platform, NDCT provides a comprehensive data sharing platform for NIMH grantees supporting clinical trials.