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 2032, growing at a CAGR of 10.5% during the forecast period 2026-2032.

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.

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

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.

1) Data Introduction • The Air Properties Time Series Dataset is a tabular time series dataset collected for air conditioning (HVAC) environment monitoring. It includes key air quality indicators such as dew point temperature, temperature, humidity, air flow, and power consumption of supply and return air at each time interval.

2) Data Utilization (1) Characteristics of the Air Properties Time Series Dataset: • By integrating real-time air quality, temperature and humidity, dew point, and other environmental indicators, this dataset is well-suited for practical applications such as industrial cleanroom environment control and facility management.

(2) Applications of theAir Properties Time Series Dataset: • Development of AHU Operating Status Prediction Models: The dataset can be used to develop machine learning models that predict the operating status (ON/OFF) of air handling units (AHU) using various environmental variables and time information. • Air Conditioning Environment Monitoring and Anomaly Detection: By analyzing the time series patterns of key indicators such as temperature, humidity, and dew point, the dataset can be utilized for real-time environment monitoring and anomaly detection in air conditioning systems.

non-detrended

This dataset comprises 524 recordings of 6-dimensional time series data, capturing forces in three directions and torques in three directions during the assembly of small car model wheels. The data was collected using an equidistant sampling method with a sampling period of 0.004 seconds. Each time series represents the process of assembling one wheel, specifically the placement of a tire onto a rim, and includes a label indicating whether the assembly was successful (OK). The wheels were assembled in batches of four, and the recordings were obtained over six different days. The labels of recordings from two (days 3 and 4) of the six days are invalid as described in [1]. The labels presented in this data set are only binary (they do not describe the reason of the failure). The labels of recordings from days 5 and 6 are created by human while the other labels came from a convolutional neural network based computer vision classifier and can be inaccurate as described in section 5.4 of [1].

Dataset Structure:

• File: ForceTorqueTimeSeries.csv
  • Columns:
    • idx (1-524): Index of the recording corresponding to the assembly of one wheel.
    • label (true/false): Indicates whether the assembly was successful (TRUE = product is OK).
    • meas_id (1-6): Identifier for the day on which the recording was made (refer to Table 2.1 in [1]).
    • force_x: X-component of the force measured by the sensor mounted on the delta robot's end effector.
    • force_y: Y-component of the force.
    • force_z: Z-component of the force.
    • torque_x: X-component of the torque.
    • torque_y: Y-component of the torque.
    • torque_z: Z-component of the torque.

Additional Files:

• IMG_3351.MOV: A video demonstrating the assembly process for one batch of four wheels.
• F3-BP-2024-Trna-Ales-Ales Trna - 2024 - Anomaly detection in robotic assembly process using force and torque sensors.pdf: Bachelor thesis [1] detailing the dataset and preliminary experiments on fault detection.
• F3-BP-2024-Hanzlik-Vojtech-Anomaly_Detection_Bachelors_Thesis.pdf: Bachelor thesis [2] describing the data acquisition process.

References:

1. Trna, A. (2024). Anomaly detection in robotic assembly process using force and torque sensors [Bachelor’s thesis, Czech Technical University in Prague].
2. Hanzlik, V. (2024). Edge AI integration for anomaly detection in assembly using Delta robot [Bachelor’s thesis, Czech Technical University in Prague].

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.

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.

Time Series Analytics Market Outlook

According to our latest research, the global time series analytics market size stood at USD 6.9 billion in 2024, reflecting a robust expansion across diverse sectors. The market is set to grow at a CAGR of 15.2% from 2025 to 2033, reaching a forecasted value of USD 25.5 billion by 2033. The surge in demand for advanced analytics solutions, driven by the proliferation of IoT devices and the exponential growth of data, is a primary catalyst for this accelerated market expansion. The adoption of time series analytics is becoming increasingly essential for organizations seeking actionable insights from sequential data, particularly in industries with high-frequency data streams and real-time decision-making requirements.

The rapid digital transformation across industries is one of the most significant growth factors for the time series analytics market. Organizations are generating massive volumes of data from sensors, transactions, and customer interactions, necessitating sophisticated tools for analysis. Time series analytics solutions enable enterprises to uncover patterns, trends, and anomalies within temporal data, facilitating predictive maintenance, demand forecasting, and process optimization. This capability is particularly valuable in sectors such as manufacturing, where unplanned downtime can result in substantial losses, and in retail, where accurate demand forecasting directly impacts profitability. The integration of advanced machine learning and artificial intelligence algorithms into time series analytics platforms further enhances their predictive power, driving adoption among forward-thinking enterprises.

Another major driver is the increasing adoption of cloud-based analytics platforms, which offer scalability, flexibility, and cost-efficiency. Cloud deployment models allow organizations to process and analyze vast datasets without the need for significant upfront infrastructure investments. This democratization of analytics technology has led to a surge in adoption among small and medium-sized enterprises (SMEs) that previously lacked access to such advanced capabilities. Additionally, the emergence of edge computing and real-time analytics is enabling organizations to process time-sensitive data closer to the source, reducing latency and supporting use cases such as fraud detection in financial services and real-time health monitoring in healthcare. The synergy between cloud computing and time series analytics is expected to remain a key growth driver throughout the forecast period.

Regulatory compliance and risk management are also fueling the demand for time series analytics solutions. In highly regulated sectors like BFSI and healthcare, the ability to monitor, analyze, and report on data in real-time is critical for ensuring compliance with stringent regulations and for mitigating operational risks. Time series analytics platforms provide organizations with the tools needed to detect unusual patterns or anomalies that could indicate fraudulent activity, system failures, or compliance breaches. As regulatory frameworks evolve and become more complex, organizations are increasingly relying on advanced analytics to maintain compliance and safeguard their operations. This trend is expected to further amplify the growth trajectory of the time series analytics market in the coming years.

From a regional perspective, North America currently leads the global time series analytics market, accounting for over 35% of the total market share in 2024. The region’s dominance is attributed to the early adoption of advanced analytics technologies, a strong presence of key market players, and significant investments in digital infrastructure. Europe and Asia Pacific are also witnessing rapid growth, with Asia Pacific projected to exhibit the highest CAGR over the forecast period, driven by increasing digitalization initiatives and the proliferation of IoT devices in emerging economies. Latin America and the Middle East & Africa are gradually catching up, supported by growing awareness and investments in analytics capabilities. The global landscape is characterized by a dynamic interplay of technological innovation, regulatory factors, and evolving business needs, all of which are shaping the future of the time series analytics market.

Component Analysis

The time series analytics market is segmented by component into software and services, with each playing a pi

Time Series Anomaly Detection AI Market Outlook

According to our latest research, the global Time Series Anomaly Detection AI market size reached USD 1.92 billion in 2024, demonstrating robust momentum as organizations increasingly leverage AI-driven analytics to secure and optimize their operations. The market is expected to grow at a CAGR of 21.7% from 2025 to 2033, with the forecasted market size projected to reach USD 13.4 billion by 2033. This exceptional growth is underpinned by escalating demand for real-time anomaly detection solutions across diverse sectors, fueled by the proliferation of IoT devices, the need for proactive risk management, and the critical importance of operational efficiency in the digital era.

The primary growth driver for the Time Series Anomaly Detection AI market is the exponential increase in data generated by connected devices, sensors, and digital platforms across industries. Modern enterprises are inundated with time-stamped data streams, making manual monitoring and analysis impractical and error-prone. AI-based anomaly detection systems offer the ability to autonomously identify deviations, outliers, and potential threats in real time, enabling organizations to mitigate risks, prevent fraud, and maintain seamless operations. As data volumes continue to surge—particularly in sectors such as finance, healthcare, and manufacturing—AI-powered time series anomaly detection is rapidly becoming a cornerstone of enterprise analytics strategies, ensuring data integrity and timely decision-making.

Another significant factor propelling the market is the rising sophistication of cyber-attacks and the growing need for advanced security solutions. Anomaly detection AI plays a pivotal role in identifying unusual patterns or breaches that could indicate security incidents, insider threats, or fraudulent activities. In highly regulated industries like BFSI and healthcare, compliance mandates and the potential consequences of undetected anomalies further amplify the adoption of these solutions. Additionally, advancements in machine learning algorithms and the integration of deep learning techniques have significantly enhanced the accuracy and scalability of anomaly detection systems, making them more accessible to organizations of all sizes and technical maturities.

The market’s growth is also supported by the increasing adoption of cloud computing and the shift towards digital transformation initiatives. Cloud-based deployment models provide scalability, flexibility, and cost-effectiveness, allowing businesses to implement sophisticated AI-driven anomaly detection without substantial upfront investments in infrastructure. Furthermore, the democratization of AI through user-friendly platforms and APIs is empowering small and medium enterprises (SMEs) to harness the power of time series anomaly detection, leveling the playing field with larger competitors. This democratization, coupled with the rising focus on predictive maintenance, operational efficiency, and customer experience, is expected to sustain the market’s momentum over the forecast period.

From a regional perspective, North America currently dominates the global Time Series Anomaly Detection AI market, driven by the presence of leading technology providers, high digital adoption rates, and a strong emphasis on innovation. However, Asia Pacific is emerging as a rapidly growing market, fueled by large-scale digitalization efforts, burgeoning industrialization, and increasing investments in AI infrastructure. Europe also holds a significant share, particularly in sectors like manufacturing, energy, and financial services. The Middle East & Africa and Latin America regions are witnessing steady uptake, supported by growing awareness of AI’s potential and government-led digital transformation initiatives. Each region presents unique opportunities and challenges, shaping the competitive landscape and influencing market dynamics.

Component Analysis

The Time Series Anomaly Detection AI market is segmented by component into software, hardware, and services, each playing a distinct role in the overall ecosystem. Software remains the largest and fastest-growing segment, accounting for a substantial portion of market revenue in 2024. This growth is attributed to the continuous innovation in AI algorithms, the development of intuitive user interfaces, and the integration of anomaly detection capabilities into broader analytics platforms. Advan

This dataset is for the paper "First onset of unrest captured at Socompa: A Recent Geodetic Survey at Central Andean volcanoes in Northern Chile" which is published in GRL: https://doi.org/10.1029/2022GL102480.

InSAR Data:

The folder of InSAR_149A.rar stores the InSAR time series analysis dataset on ascending track 149.

The 'Imagedate' folder stores the empty *.rslc files to indicate the date of each SLCs.

Data_Asc.mat stores the main InSAR time series data, which includes the UTC time of the acquisition (for accurate time calculation), the length of perpendicular baselines (unit is meter), the number of days counting from the first epoch, the unwrapped time series data (ifg), the unwrapped time series data with GACOS correction (ifg_aps), the look angles (la, unit is rad), and lat&lon.

parms.mat stores the parameters used during the data processing by StaMPS.

semi_fit.mat stores the results of the semi-variogram fitting of each interferogram on time series. It provides two versions for the original dataset (semi) and the GACOS-corrected dataset (semi_aps). This file is mainly used to weight the data during the time series fitting.

runTSA.m, the main function to run the InSAR time series fitting. See more details in the Code part.

The folder of InSAR_156D.rar stores the same content as the InSAR_149A.rar but for descending track 156.

Code:

This folder contains the codes of the InSAR time series fitting for this dataset, and the GBIS software.

TSA_findref.m, this function is used to search the reference point of the InSAR data.

TSA_EQ_fit.m, is the main function to perform InSAR time series fitting.

rb_pixel_fit.m, is the robust way to fit the linear model.

TSA_EQ_pixel.m, is the function used to plot the results.

To perform the InSAR time series fitting, you need to put these four functions under your Matlab path, and then run the runTSA.m function in the data folder.

The GBIS folder stores the updated version of the GBIS software, which allows you to perform the pCDM, CDM, and pECM. The core functions of these models are provided by Dr. Mehdi Nikkhoo, and you could find them here: https://www.volcanodeformation.com/software

GBIS_Modelling_Results:

This folder stores the data of InSAR and GPS joint inversion for Socompa Uplift.

The folder Socompa stores the modelling results using the models of Okada(D), pECM(E), Mogi(M), pCDM(N), and Yang(Y), respectively.

GPS_data.txt stores the cumulative displacements and the uncertainties of the SOCM station in three directions.

Socompa.inp is the configuration file for GBIS running.

Vol_asc.mat and Vol_asc_ds.mat stores the original and the downsampled ascending data, while Vol_dsc.mat and Vol_dsc_ds.mat store those of descending.

Many thanks for using our dataset and please let me know if you have any further questions!

Time Series Databases Software Market size was valued at USD 359.37 USD Million in 2024 and is projected to reach USD 773.71 Million by 2031, growing at a CAGR of 10.06% from 2024 to 2031.

Time Series Databases Software Market Drivers

Growing Data Volume: The exponential growth of data generated by various sources, including IoT devices, financial transactions, and digital services, necessitates efficient management and analysis of time-stamped data. Time series databases are optimized for handling large volumes of time-stamped data, driving their adoption.

Rise of IoT and Connected Devices: The proliferation of IoT devices in industries such as manufacturing, healthcare, and smart cities generates massive amounts of time-series data. Time series databases are crucial for storing, querying, and analyzing this continuous stream of data efficiently.

Increasing Importance of Real-Time Analytics: Businesses require real-time insights to make informed decisions and maintain competitive advantage. Time series databases support real-time analytics by efficiently processing and analyzing time-stamped data, which is critical for applications like monitoring, forecasting, and anomaly detection.

Edge Time-Series Database Market Outlook

According to our latest research, the Global Edge Time-Series Database market size was valued at $1.2 billion in 2024 and is projected to reach $5.8 billion by 2033, expanding at a robust CAGR of 19.7% during the forecast period of 2025–2033. One of the major factors propelling the global expansion of the Edge Time-Series Database market is the exponential growth in the deployment of IoT devices across industries, which has led to an unprecedented surge in real-time data generation at the edge. This shift necessitates efficient, scalable, and low-latency database solutions, catalyzing investments and innovation in edge time-series database technologies worldwide.

Regional Outlook

North America currently holds the largest share of the global Edge Time-Series Database market, accounting for approximately 38% of the total market value in 2024. This dominance is attributed to the region's mature digital infrastructure, high adoption of Industrial Internet of Things (IIoT), and a strong ecosystem of technology providers and integrators. The presence of leading cloud service providers and a robust focus on smart manufacturing and automation have further accelerated the uptake of edge time-series databases. Additionally, proactive government policies supporting Industry 4.0 and digital transformation initiatives have cemented North America’s leadership position. As a result, enterprises in the region are increasingly leveraging edge analytics for predictive maintenance, real-time monitoring, and operational efficiency, thereby driving sustained market growth.

The Asia Pacific region is emerging as the fastest-growing market, with a projected CAGR exceeding 23% during 2025–2033. Rapid industrialization, urbanization, and the proliferation of smart city initiatives are key drivers fueling demand for edge-based time-series data management solutions. Countries such as China, Japan, South Korea, and India are witnessing significant investments in IoT infrastructure, 5G deployment, and digital healthcare, all of which require robust edge analytics platforms. The expanding manufacturing base, coupled with government incentives for digital transformation and smart energy management, is accelerating adoption. Furthermore, collaborations between global technology firms and local enterprises are enhancing the region's capability to deploy advanced edge database solutions, positioning Asia Pacific as a pivotal growth engine for the market.

In contrast, emerging economies in Latin America and the Middle East & Africa are experiencing gradual adoption of edge time-series databases, primarily driven by localized demand in energy management, transportation, and healthcare monitoring. However, challenges such as limited digital infrastructure, skills gaps, and inconsistent regulatory frameworks can impede market penetration. Despite these obstacles, there is growing recognition of the value that edge analytics brings in terms of operational efficiency and cost savings. As governments and private sectors in these regions increase investments in digitalization and smart infrastructure, the adoption curve is expected to accelerate, albeit at a slower pace compared to more mature markets.

Report Scope

This dataset presents time-series physiological and contextual data collected from a simulated college environment to support research in mental health monitoring using smart wearables. It reflects the everyday experiences of students in academic settings, capturing relevant health indicators at high frequency to help understand patterns related to stress and well-being.

Each record contains second-by-second readings from a wearable device, along with self-reported stress information and contextual tags like location and session duration. The dataset is ideal for studies on student health behavior, physiological monitoring, and educational well-being analytics.

⭐ Key Features Time-Synchronized Physiological Data Includes continuous readings of heart rate, skin temperature, electrodermal activity (EDA), and physical activity.

Stress Level Labels Provides a binary stress label (0 or 1) and a corresponding descriptive category (None or High).

Contextual Information Captures environmental context such as activity location (e.g., Class, Library, Dorm) and session duration.

High-Resolution Data Logged at 1-second intervals to reflect real-time physiological changes and behavioral shifts.

Anonymized and Synthetic Safely modeled data that mimics realistic physiological behavior, suitable for academic use without privacy concerns.

Structured Format Ready-to-use in CSV format for easy integration into data science workflows and health research projects.

Data and code to reproduce the results in Weix D., Cath T. Y., and Hering A. S. “Monitoring Covariance in Multivariate Time Series: Comparing Machine Learning and Statistical Approaches”. Under Review with Quality and Reliability Engineering International

This dataset provides high-frequency, time-stamped vital sign measurements for anonymized patients, including heart rate, blood pressure, respiratory rate, oxygen saturation, body temperature, and consciousness level. Designed for health monitoring and early warning system development, it supports longitudinal patient analysis and real-time clinical decision support. Device and location metadata enable context-aware analytics and integration with hospital systems.

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.

The agricultural WSN (wireless sensor network) has the characteristics of long operation cycle and wide coverage area. In order to cover as much area as possible, farms usually deploy multiple monitoring devices in different locations of the same area. Due to different types of equipment, monitoring data will vary greatly, and too many monitoring nodes also reduce the efficiency of the network. Although there have been some studies on data fusion algorithms, they have problems such as ignoring the dynamic changes of time series, weak anti-interference ability, and poor processing of data fluctuations. So in this study, a data fusion algorithm for optimal node tracking in agricultural wireless sensor networks is designed. By introducing the dynamic bending distance in the dynamic time warping algorithm to replace the absolute distance in the fuzzy association algorithm and combine the sensor’s own reliability and association degree as the weighted fusion weight, which improved the fuzzy association algorithm. Finally, another three algorithm were tested for multi-temperature sensor data fusion. Compare with the kalman filter, arithmetic mean and fuzzy association algorithm, the average value of the improved data fusion algorithm is 29.5703, which is close to the average value of the other three algorithms, indicating that the data distribution is more even. Its extremely bad value is 8.9767, which is 10.04%, 1.14% and 9.85% smaller than the other three algorithms, indicating that it is more robust when dealing with outliers. Its variance is 2.6438, which is 2.82%, 0.65% and 0.27% smaller than the other three algorithms, indicating that it is more stable and has less data volatility. The results show that the algorithm proposed in this study has higher fusion accuracy and better robustness, which can obtain the fusion value that truly feedbacks the agricultural environment conditions. It reduces production costs by reducing redundant monitoring devices, the energy consumption and improves the data collection efficiency in wireless sensor networks.

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.