Time Series Analysis Software Market size was valued at USD 1.8 Billion in 2024 and is projected to reach USD 4.7 Billion by 2031, growing at a CAGR of 10.5% during the forecast period 2024-2031.

Global Time Series Analysis Software Market Drivers

Growing Data Volumes: The exponential growth in data generated across various industries necessitates advanced tools for analyzing time series data. Businesses need to extract actionable insights from large datasets to make informed decisions, driving the demand for time series analysis software.

Increasing Adoption of IoT and Connected Devices: The proliferation of Internet of Things (IoT) devices generates continuous streams of time-stamped data. Analyzing this data in real-time helps businesses optimize operations, predict maintenance needs, and enhance overall efficiency, fueling the demand for time series analysis tools.

Advancements in Machine Learning and AI: Integration of machine learning and artificial intelligence (AI) with time series analysis enhances predictive capabilities and automates the analysis process. These advancements enable more accurate forecasting and anomaly detection, attracting businesses to adopt sophisticated analysis software.

Need for Predictive Analytics: Businesses are increasingly focusing on predictive analytics to anticipate future trends and behaviors. Time series analysis is crucial for forecasting demand, financial performance, stock prices, and other metrics, driving the market growth.

Industry 4.0 and Automation: The push towards Industry 4.0 involves automating industrial processes and integrating smart technologies. Time series analysis software is essential for monitoring and optimizing manufacturing processes, predictive maintenance, and supply chain management in this context.

Financial Sector Growth: The financial industry extensively uses time series analysis for modeling stock prices, risk management, and economic forecasting. The growing complexity of financial markets and the need for real-time data analysis bolster the demand for specialized software.

Healthcare and Biomedical Applications: Time series analysis is increasingly used in healthcare for monitoring patient vitals, managing medical devices, and analyzing epidemiological data. The focus on personalized medicine and remote patient monitoring drives the adoption of these tools.

Climate and Environmental Monitoring: Governments and organizations use time series analysis to monitor climate change, weather patterns, and environmental data. The need for accurate predictions and real-time monitoring in environmental science boosts the market.

Regulatory Compliance and Risk Management: Industries such as finance, healthcare, and energy face stringent regulatory requirements. Time series analysis software helps in compliance by providing detailed monitoring and reporting capabilities, reducing risks associated with regulatory breaches.

Emergence of Big Data and Cloud Computing: The adoption of big data technologies and cloud computing facilitates the storage and analysis of large volumes of time series data. Cloud-based time series analysis software offers scalability, flexibility, and cost-efficiency, making it accessible to a broader range of businesses.

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.

SCALABLE TIME SERIES CHANGE DETECTION FOR BIOMASS MONITORING USING GAUSSIAN PROCESS

VARUN CHANDOLA* AND RANGA RAJU VATSAVAI*

Abstract. Biomass monitoring, specifically, detecting changes in the biomass or vegetation of a geographical region, is vital for studying the carbon cycle of the system and has significant implications in the context of understanding climate change and its impacts. Recently, several time series change detection methods have been proposed to identify land cover changes in temporal profiles (time series) of vegetation collected using remote sensing instruments. In this paper, we adapt Gaussian process regression to detect changes in such time series in an online fashion. While Gaussian process (GP) has been widely used as a kernel based learning method for regression and classification, their applicability to massive spatio-temporal data sets, such as remote sensing data, has been limited owing to the high computational costs involved. In our previous work we proposed an efficient Toeplitz matrix based solution for scalable GP parameter estimation. In this paper we apply these solutions to a GP based change detection algorithm. The proposed change detection algorithm requires a memory footprint which is linear in the length of the input time series and runs in time which is quadratic to the length of the input time series. Experimental results show that both serial and parallel implementations of our proposed method achieve significant speedups over the serial implementation. Finally, we demonstrate the effectiveness of the proposed change detection method in identifying changes in Normalized Difference Vegetation Index (NDVI) data.

This lesson was adapted from educational material written by Dr. Kateri Salk for her Fall 2019 Hydrologic Data Analysis course at Duke University. This is the first part of a two-part exercise focusing on time series analysis.

Introduction

Time series are a special class of dataset, where a response variable is tracked over time. The frequency of measurement and the timespan of the dataset can vary widely. At its most simple, a time series model includes an explanatory time component and a response variable. Mixed models can include additional explanatory variables (check out the nlme and lme4 R packages). We will be covering a few simple applications of time series analysis in these lessons.

Opportunities

Analysis of time series presents several opportunities. In aquatic sciences, some of the most common questions we can answer with time series modeling are:

• Has there been an increasing or decreasing trend in the response variable over time?

• Can we forecast conditions in the future?

  Challenges

Time series datasets come with several caveats, which need to be addressed in order to effectively model the system. A few common challenges that arise (and can occur together within a single dataset) are:

• Autocorrelation: Data points are not independent from one another (i.e., the measurement at a given time point is dependent on previous time point(s)).

• Data gaps: Data are not collected at regular intervals, necessitating interpolation between measurements. There are often gaps between monitoring periods. For many time series analyses, we need equally spaced points.

• Seasonality: Cyclic patterns in variables occur at regular intervals, impeding clear interpretation of a monotonic (unidirectional) trend. Ex. We can assume that summer temperatures are higher.

• Heteroscedasticity: The variance of the time series is not constant over time.

• Covariance: the covariance of the time series is not constant over time. Many of these models assume that the variance and covariance are similar over the time-->heteroschedasticity.

  Learning Objectives

After successfully completing this notebook, you will be able to:

1. Choose appropriate time series analyses for trend detection and forecasting

2. Discuss the influence of seasonality on time series analysis

3. Interpret and communicate results of time series analyses

CESNET-TimeSeries24: The dataset for network traffic forecasting and anomaly detection

The dataset called CESNET-TimeSeries24 was collected by long-term monitoring of selected statistical metrics for 40 weeks for each IP address on the ISP network CESNET3 (Czech Education and Science Network). The dataset encompasses network traffic from more than 275,000 active IP addresses, assigned to a wide variety of devices, including office computers, NATs, servers, WiFi routers, honeypots, and video-game consoles found in dormitories. Moreover, the dataset is also rich in network anomaly types since it contains all types of anomalies, ensuring a comprehensive evaluation of anomaly detection methods.

Last but not least, the CESNET-TimeSeries24 dataset provides traffic time series on institutional and IP subnet levels to cover all possible anomaly detection or forecasting scopes. Overall, the time series dataset was created from the 66 billion IP flows that contain 4 trillion packets that carry approximately 3.7 petabytes of data. The CESNET-TimeSeries24 dataset is a complex real-world dataset that will finally bring insights into the evaluation of forecasting models in real-world environments.

Please cite the usage of our dataset as:

Koumar, J., Hynek, K., Čejka, T. et al. CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting. Sci Data 12, 338 (2025). https://doi.org/10.1038/s41597-025-04603-x

@Article{cesnettimeseries24,
author={Koumar, Josef and Hynek, Karel and {\v{C}}ejka, Tom{\'a}{\v{s}} and {\v{S}}i{\v{s}}ka, Pavel},
title={CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting},
journal={Scientific Data},
year={2025},
month={Feb},
day={26},
volume={12},
number={1},
pages={338},
issn={2052-4463},
doi={10.1038/s41597-025-04603-x},
url={https://doi.org/10.1038/s41597-025-04603-x}
}

Time series

We create evenly spaced time series for each IP address by aggregating IP flow records into time series datapoints. The created datapoints represent the behavior of IP addresses within a defined time window of 10 minutes. The vector of time-series metrics v_{ip, i} describes the IP address ip in the i-th time window. Thus, IP flows for vector v_{ip, i} are captured in time windows starting at t_i and ending at t_{i+1}. The time series are built from these datapoints.

Datapoints created by the aggregation of IP flows contain the following time-series metrics:

• Simple volumetric metrics: the number of IP flows, the number of packets, and the transmitted data size (i.e. number of bytes)
• Unique volumetric metrics: the number of unique destination IP addresses, the number of unique destination Autonomous System Numbers (ASNs), and the number of unique destination transport layer ports. The aggregation of \textit{Unique volumetric metrics} is memory intensive since all unique values must be stored in an array. We used a server with 41 GB of RAM, which was enough for 10-minute aggregation on the ISP network.
• Ratios metrics: the ratio of UDP/TCP packets, the ratio of UDP/TCP transmitted data size, the direction ratio of packets, and the direction ratio of transmitted data size
• Average metrics: the average flow duration, and the average Time To Live (TTL)

Multiple time aggregation: The original datapoints in the dataset are aggregated by 10 minutes of network traffic. The size of the aggregation interval influences anomaly detection procedures, mainly the training speed of the detection model. However, the 10-minute intervals can be too short for longitudinal anomaly detection methods. Therefore, we added two more aggregation intervals to the datasets--1 hour and 1 day.

Time series of institutions: We identify 283 institutions inside the CESNET3 network. These time series aggregated per each institution ID provide a view of the institution's data.

Time series of institutional subnets: We identify 548 institution subnets inside the CESNET3 network. These time series aggregated per each institution ID provide a view of the institution subnet's data.

Data Records

The file hierarchy is described below:

cesnet-timeseries24/

|- institution_subnets/

| |- agg_10_minutes/

| |- agg_1_hour/

| |- agg_1_day/

| |- identifiers.csv

|- institutions/

| |- agg_10_minutes/

| |- agg_1_hour/

| |- agg_1_day/

| |- identifiers.csv

|- ip_addresses_full/

| |- agg_10_minutes/

| |- agg_1_hour/

| |- agg_1_day/

| |- identifiers.csv

|- ip_addresses_sample/

| |- agg_10_minutes/

| |- agg_1_hour/

| |- agg_1_day/

| |- identifiers.csv

|- times/

| |- times_10_minutes.csv

| |- times_1_hour.csv

| |- times_1_day.csv

|- ids_relationship.csv
|- weekends_and_holidays.csv

The following list describes time series data fields in CSV files:

• id_time: Unique identifier for each aggregation interval within the time series, used to segment the dataset into specific time periods for analysis.
• n_flows: Total number of flows observed in the aggregation interval, indicating the volume of distinct sessions or connections for the IP address.
• n_packets: Total number of packets transmitted during the aggregation interval, reflecting the packet-level traffic volume for the IP address.
• n_bytes: Total number of bytes transmitted during the aggregation interval, representing the data volume for the IP address.
• n_dest_ip: Number of unique destination IP addresses contacted by the IP address during the aggregation interval, showing the diversity of endpoints reached.
• n_dest_asn: Number of unique destination Autonomous System Numbers (ASNs) contacted by the IP address during the aggregation interval, indicating the diversity of networks reached.
• n_dest_port: Number of unique destination transport layer ports contacted by the IP address during the aggregation interval, representing the variety of services accessed.
• tcp_udp_ratio_packets: Ratio of packets sent using TCP versus UDP by the IP address during the aggregation interval, providing insight into the transport protocol usage pattern. This metric belongs to the interval <0, 1> where 1 is when all packets are sent over TCP, and 0 is when all packets are sent over UDP.
• tcp_udp_ratio_bytes: Ratio of bytes sent using TCP versus UDP by the IP address during the aggregation interval, highlighting the data volume distribution between protocols. This metric belongs to the interval <0, 1> with same rule as tcp_udp_ratio_packets.
• dir_ratio_packets: Ratio of packet directions (inbound versus outbound) for the IP address during the aggregation interval, indicating the balance of traffic flow directions. This metric belongs to the interval <0, 1>, where 1 is when all packets are sent in the outgoing direction from the monitored IP address, and 0 is when all packets are sent in the incoming direction to the monitored IP address.
• dir_ratio_bytes: Ratio of byte directions (inbound versus outbound) for the IP address during the aggregation interval, showing the data volume distribution in traffic flows. This metric belongs to the interval <0, 1> with the same rule as dir_ratio_packets.
• avg_duration: Average duration of IP flows for the IP address during the aggregation interval, measuring the typical session length.
• avg_ttl: Average Time To Live (TTL) of IP flows for the IP address during the aggregation interval, providing insight into the lifespan of packets.

Moreover, the time series created by re-aggregation contains following time series metrics instead of n_dest_ip, n_dest_asn, and n_dest_port:

• sum_n_dest_ip: Sum of numbers of unique destination IP addresses.
• avg_n_dest_ip: The average number of unique destination IP addresses.
• std_n_dest_ip: Standard deviation of numbers of unique destination IP addresses.
• sum_n_dest_asn: Sum of numbers of unique destination ASNs.
• avg_n_dest_asn: The average number of unique destination ASNs.
• std_n_dest_asn: Standard deviation of numbers of unique destination ASNs)
• sum_n_dest_port: Sum of numbers of unique destination transport layer ports.
• avg_n_dest_port: The average number of unique destination transport layer ports.
• std_n_dest_port: Standard deviation of numbers of unique destination transport layer

The data contains a zip-file with the following folders: a) data (agricultural parcels, filled and unfilled NDVI time series tables, feature extraction tables and prediction results) (csv, shp), b) model (random forest models for catch crop prediction) (rds), and c) R (R script files for Random Forest model training and prediction with RStudio) (r).

The algorithms and models developed for this study were implemented via virtual Docker containers into the timeStamp software prototype which allows for large-scale automatized catch crop analysis on the parcel-level (www.timestamp.lup-umwelt.de). timeStamp saves Sentinel-2 raster data as parcel-wise clipped image time series into a PostGIS database. All further processing steps were performed with the statistical computing language R (RStudio Team, 2020). For raster data manipulation within the PostGIS database and downloading NDVI time series, we used the packages rpostgis (Bucklin and Basille, 2019) and RPostgreSQL (Conway et al., 2017). For time series filling and predictors calculation, we used the packages zoo (Zeileis et al., 2020), hydroGOF (Zambrano-Bigiarini, 2020), tsoutliers (de Lacalle, 2019), and changepoint (Killick et al., 2016). For RF modelling, we used the package caret (Kuhn et al., 2020).

The original data for NDVI time series calculation is from the GFZ Time Series System for Sentinel-2 by the German Research Centre for Geosciences, 2020 (https://gitext.gfz-potsdam.de/gts2). The predictors for Random Forest modelling calculated from the NDVI time series are described in the article in the reference section.

For further information, we refer to the following article: Schulz, C.; Holtgrave, A.; Kleinschmit, B.: Large-scale winter catch crop monitoring with Sentinel-2 time series and machine learning–An alternative to on-site controls?, Computers and Electronics in Agriculture, Volume 186, 2021, 106173, ISSN 0168-1699, https://doi.org/10.1016/j.compag.2021.106173.

Time series of the October mean ozone vertical column above Antarctica.

The dataset behind those images was developed within the ESA Climate Change Initiative Programme (https://climate.esa.int/en/projects/ozone/) and is operationally distributed by the EU Copernicus Climate Change Service

Fish Monitoring Dataset from the Okavango Delta, collected by Okavango Research Institute. Data Licenses as per ORI and JRS Biodiversity data standards aggreement.

Context

This data set contains some basic statistics about user count and user growth as well as crash count for a real mobile app. The dataset contains a basic timeseries of 1 hour resolution for a period of one week.

Content

The data set contains columns for total concurrent user count, new users acquired in that period of time, number of sessions and crash count.

Acknowledgements

This data set would not be available without the Real User Monitoring capabilities of Dynatrace and its flexibility to export and expose this data for scientific experiments.

Inspiration

The data set was intended to play around with seasonality, trend and prediction of timeseries.

As per Cognitive Market Research's latest published report, the Global Time Series Databases Software market size will be $993.24 Million by 2028. Time Series Databases Software Industry's Compound Annual Growth Rate will be 18.36% from 2023 to 2030. Factors Affecting Time Series Databases Software market growth

Rise in automation in industry

Industrial sensors are a key part of factory automation and Industry 4.0. Motion, environmental, and vibration sensors are used to monitor the health of equipment, from linear or angular positioning, tilt sensing, leveling, shock, or fall detection. A Sensor is a device that identifies the progressions in electrical or physical or other quantities and in a way delivers a yield as an affirmation of progress in the quantity.

In simple terms, Industrial Automation Sensors are input devices that provide an output (signal) with respect to a specific physical quantity (input). In industrial automation, sensors play a vital part to make the products intellectual and exceptionally automatic. These permit one to detect, analyze, measure, and process a variety of transformations like alteration in position, length, height, exterior, and dislocation that occurs in the Industrial manufacturing sites. These sensors also play a pivotal role in predicting and preventing numerous potential proceedings, thus, catering to the requirements of many sensing applications. This sensor generally works on time series as the readings are taken after equal intervals of time.

The increase in the use of sensor to monitor the industrial activities and in production factories is fueling the growth of the time series database software market. Also manufacturing in pharmaceutical industry requires proper monitoring due to which there is increase in demand for sensors and time series database, this fuels the demand for time series database software market.

Increasing Demand of Data Driven Decision Making Fuels the Market Growth

Restraints for Time Series Databases Software Market

Network Security. (Access Detailed Analysis in the Full Report Version)

Opportunities for Time Series Databases Software Market

IoT and time series database software. (Access Detailed Analysis in the Full Report Version)

Factors Affecting the Time Series Databases Software Market

Time-series data is a sequence of data points collected over time intervals, giving us the ability to track changes over time. Time-series data can track changes over milliseconds, days, or even years. Time-series databases are designed to store data that changes with time. This can be any kind of data which was collected over time. It might be metrics collected from some systems - all trending systems are examples of the time-series data. Time Series Databases (TSDB) are designed to store and analyze event data, time series, or time-stamped data, often streamed from IoT devices, and enables graphing, monitoring and analyzing changes over time. Time series databases allow businesses to store time-stamped data.

A company may adopt a time series database if they need to monitor data in real time or if they are running applications that continuously produce data. Some examples of applications that product time series data include network or application performance monitoring (APM) software tools, sensor data from IoT devices, financial market data, and a number of security applications, among many others. Time series databases are optimized for storing this data so that it can be easily pulled and analyzed. Time series data is often used when running predictive analytics or machine learning algorithms, enabling users to understand historical data to help predict future outcomes. Some big data processing and distribution software may provide time series storage functionality. In some fields, time series may be called profiles, curves, traces or trends. Several early time series databases are associated with industrial applications which could efficiently store measured values from sensory equipment (also referred to as data historians), but now are used in support of a much wider range of applications.

Abstract copyright UK Data Service and data collection copyright owner.

The General Household Survey (GHS), ran from 1971-2011 (the UKDS holds data from 1972-2011). It was a continuous annual national survey of people living in private households, conducted by the Office for National Statistics (ONS). The main aim of the survey was to collect data on a range of core topics, covering household, family and individual information. This information was used by government departments and other organisations for planning, policy and monitoring purposes, and to present a picture of households, families and people in Great Britain. In 2008, the GHS became a module of the Integrated Household Survey (IHS). In recognition, the survey was renamed the General Lifestyle Survey (GLF). The GLF closed in January 2012. The 2011 GLF is therefore the last in the series. A limited number of questions previously run on the GLF were subsequently included in the Opinions and Lifestyle Survey (OPN).

Secure Access GHS/GLF
The UKDS holds standard access End User Licence (EUL) data for 1972-2006. A Secure Access version is available, covering the years 2000-2011 - see SN 6716 General Lifestyle Survey, 2000-2011: Secure Access.

History
The GHS was conducted annually until 2011, except for breaks in 1997-1998 when the survey was reviewed, and 1999-2000 when the survey was redeveloped. Further information may be found in the ONS document An overview of 40 years of data (General Lifestyle Survey Overview - a report on the 2011 General Lifestyle Survey) (PDF). Details of changes each year may be found in the individual study documentation.

EU-SILC
In 2005, the European Union (EU) made a legal obligation (EU-SILC) for member states to collect additional statistics on income and living conditions. In addition, the EU-SILC data cover poverty and social exclusion. These statistics are used to help plan and monitor European social policy by comparing poverty indicators and changes over time across the EU. The EU-SILC requirement was integrated into the GHS/GLF in 2005. After the closure of the GLF, EU-SILC was collected via the Family Resources Survey (FRS) until the UK left the EU in 2020.

Reformatted GHS data 1973-1982 - Surrey SPSS Files
SPSS files were created by the University of Surrey for all GHS years from 1973 to 1982 inclusive. The early files were restructured and the case changed from the household to the individual with all of the household information duplicated for each individual. The Surrey SPSS files contain all the original variables as well as some extra derived variables (a few variables were omitted from the data files for 1973-76). In 1973 only, the section on leisure was not included in the Surrey SPSS files. This has subsequently been made available, however, and is now held in a separate study, General Household Survey, 1973: Leisure Questions (SN 3982). Records for the original GHS 1973-1982 ASCII files have been removed from the UK Data Archive catalogue, but the data are still preserved and available upon request.

General Household Survey: Time Series Dataset, 1972-2004:
The General Household Survey Time Series Dataset 1972-2004 was created by ONS. The dataset allows for analysis of several topics covered in the GHS over time and for pseudo-cohort analysis. It was originally referred to as the GHS Pseudo-Cohort Dataset (GHSPCD). The user guide for the time series dataset was created jointly by ONS and the Economic and Social Data Service (ESDS) Government.

Main Topics:

The main GHS consisted of a household questionnaire, completed by the Household Reference Person (HRP), and an individual questionnaire, completed by all adults aged 16 and over resident in the household. A number of different trailers each year covering extra topics were included in later (post-review) surveys in the series from 2000.

• The household questionnaire covered the following topics: household information, accommodation type, housing tenure/costs, and consumer durables including vehicle ownership.
• The individual questionnaire included data from the household dataset, and additional sections on migration/citizenship/national identity/ethnicity, employment, pensions, education, health, child care, smoking, drinking, family information, financial situation, and income.
  

The General Household Survey: Time Series Dataset, 1972-2004 comprises 71 variables and 827,624 cases. The variables cover topics included in the main GHS over time, such as household composition, socio-economic status, qualifications and education, economic activity, health consultations, chronic and debilitative illness, smoking behaviour and household and individual demographics.

This resource contains an example script for using the software package pyhydroqc. pyhydroqc was developed to identify and correct anomalous values in time series data collected by in situ aquatic sensors. For more information, see the code repository: https://github.com/AmberSJones/pyhydroqc and the documentation: https://ambersjones.github.io/pyhydroqc/. The package may be installed from the Python Package Index.

This script applies the functions to data from a single site in the Logan River Observatory, which is included in the repository. The data collected in the Logan River Observatory are sourced at http://lrodata.usu.edu/tsa/ or on HydroShare: https://www.hydroshare.org/search/?q=logan%20river%20observatory.

Anomaly detection methods include ARIMA (AutoRegressive Integrated Moving Average) and LSTM (Long Short Term Memory). These are time series regression methods that detect anomalies by comparing model estimates to sensor observations and labeling points as anomalous when they exceed a threshold. There are multiple possible approaches for applying LSTM for anomaly detection/correction. - Vanilla LSTM: uses past values of a single variable to estimate the next value of that variable. - Multivariate Vanilla LSTM: uses past values of multiple variables to estimate the next value for all variables. - Bidirectional LSTM: uses past and future values of a single variable to estimate a value for that variable at the time step of interest. - Multivariate Bidirectional LSTM: uses past and future values of multiple variables to estimate a value for all variables at the time step of interest.

The correction approach uses piecewise ARIMA models. Each group of consecutive anomalous points is considered as a unit to be corrected. Separate ARIMA models are developed for valid points preceding and following the anomalous group. Model estimates are blended to achieve a correction.

The anomaly detection and correction workflow involves the following steps: 1. Retrieving data 2. Applying rules-based detection to screen data and apply initial corrections 3. Identifying and correcting sensor drift and calibration (if applicable) 4. Developing a model (i.e., ARIMA or LSTM) 5. Applying model to make time series predictions 6. Determining a threshold and detecting anomalies by comparing sensor observations to modeled results 7. Widening the window over which an anomaly is identified 8. Aggregating detections resulting from multiple models 9. Making corrections for anomalous events

Instructions to run the notebook through the CUAHSI JupyterHub: 1. Click "Open with..." at the top of the resource and select the CUAHSI JupyterHub. You may need to sign into CUAHSI JupyterHub using your HydroShare credentials. 2. Select 'Python 3.8 - Scientific' as the server and click Start. 2. From your JupyterHub directory, click on the ExampleNotebook.ipynb file. 3. Execute each cell in the code by clicking the Run button.

A validation assessment of Land Cover Monitoring, Assessment, and Projection Version 1 annual land cover products (1985–2017) for the Conterminous United States was conducted with an independently collected reference data set. Reference data land cover attributes were assigned by trained interpreters for each year of the time series (1984–2018) to a reference sample of 24,971 randomly-selected Landsat resolution (30m x 30m) pixels. The LCMAP and reference dataset labels for each pixel location are displayed here for each year, 1985–2017.

The data archive InTheMED_WP2_DS_GWLevelAnnualTimeSeries is part of Task 2.2 “Review and collect the available groundwater quantity and quality data sets in the MED region” and contains the groundwater level time series in the InTheMED study countries, Greece, Portugal, Spain, Tunisia, Turkey, Italy, and also France.

The size and share of the market is categorized based on Application (Relational Databases, NoSQL Databases, Specialized Time Series Databases) and Product (Time-Based Data Storage, Analytics, Monitoring Systems, IoT Applications) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

This dataset contains time series measurements of temperature and salinity at the GAK1 site at the mouth of Resurrection Bay near Seward, AK from December 1999 through October 2002. Instrument packages were deployed at 6 depth levels.

You are not authorized to view this dataset. You may email the responsible party OEAW to request access.

A validation assessment of Land Cover Monitoring, Assessment, and Projection Collection 1.0 annual land cover products (2000–2019) for Hawaii was conducted with an independently collected reference dataset. Reference data land cover attributes were assigned by trained interpreters for each year of the time series (2000–2019) to a reference sample of 600 Landsat resolution (30m x 30m) pixels. The LCMAP and reference dataset labels for each pixel location are displayed here for each year, 2000–2019.

Abstract Prognostics solutions for mission critical systems require a comprehensive methodology for proactively detecting and isolating failures, recommending and guiding condition-based maintenance actions, and estimating in real time the remaining useful life of critical components and associated subsystems. A major challenge has been to extend the benefits of prognostics to include computer servers and other electronic components. The key enabler for prognostics capabilities is monitoring time series signals relating to the health of executing components and subsystems. Time series signals are processed in real time using pattern recognition for proactive anomaly detection and for remaining useful life estimation. Examples will be presented of the use of pattern recognition techniques for early detection of a number of mechanisms that are known to cause failures in electronic systems, including: environmental issues; software aging; degraded or failed sensors; degradation of hardware components; degradation of mechanical, electronic, and optical interconnects. Prognostics pattern classification is helping to substantially increase component reliability margins and system availability goals while reducing costly sources of "no trouble found" events that have become a significant warranty-cost issue. Bios Aleksey Urmanov is a research scientist at Sun Microsystems. He earned his doctoral degree in Nuclear Engineering at the University of Tennessee in 2002. Dr. Urmanov's research activities are centered around his interest in pattern recognition, statistical learning theory and ill-posed problems in engineering. His most recent activities at Sun focus on developing health monitoring and prognostics methods for EP-enabled computer servers. He is a founder and an Editor of the Journal of Pattern Recognition Research. Anton Bougaev holds a M.S. and a Ph.D. degrees in Nuclear Engineering from Purdue University. Before joining Sun Microsystems Inc. in 2007, he was a lecturer in Nuclear Engineering Department and a member of Applied Intelligent Systems Laboratory (AISL), of Purdue University, West Lafayette, USA. Dr. Bougaev is a founder and the Editor-in-Chief of the Journal of Pattern Recognition Research. His current focus is in reliability physics with emphasis on complex system analysis and the physics of failures which are based on the data driven pattern recognition techniques.

This dataset includes real-world time-series statistics from network traffic on real commercial LTE networks in Greece. The purpose of this dataset is to capture the QoS/QoE of three COTS UEs interacting with three edge applications. Specifically, the following features are included: Throughput and Jitter for each UE-Application and Channel Quality Indicator (CQI) for each UE. The interactions were generated from a realistic network behavior in an office by developing multiple network traffic scenarios. These scenarios are based on real network patterns observed at a specific time interval during the day (early morning from 10:00 AM to 11:00 AM) on users in our office facilities in Volos, Greece. The mobility of users is considered as well, since we developed attenuation scenarios from real commercial networks in Volos, Greece. These attenuation scenarios emulate cars traveling a specific city route with speeds that vary from 40 to 60 km/h. These car scenarios were used to collect 182500 CQI data from 73 cars capturing a large spectrum of the route's traffic. To attenuate the signal in order to emulate the realistic mobility scenarios, we utilized Programmable Attenuators that were connected directly to the RAN. The CQI dataset is publicly available here. Traffic monitoring, as well as traffic analysis and CQI, is captured/calculated in almost real-time from our custom Data Analytics Function (NWDAF) named Core & Ran Analytics Function (CRAF). CRAF utilizes the PyShark to live capture the traffic and FlexRAN controller to obtain RAN Statistics such as the CQI. Then it stores all the data in a MySQL database. These data were exported as a .csv file for easy data analysis and preprocessing.