Causality Distance Measures for Multivariate Time Series with Applications

In this work, we focus on the development of new distance measure algorithms, namely, the Causality Within Groups (CAWG), the Generalized Causality Within Groups (GCAWG) and the Causality Between Groups (CABG), all of which are based on the well-known Granger causality. The proposed distances together with the associated algorithms are suitable for multivariate statistical data analysis including unsupervised classification (clustering) purposes for the analysis of multivariate time series data with emphasis on financial and economic data where causal relationships are frequently present. For exploring the appropriateness of the proposed methodology, we implement, for illustrative purposes, the proposed algorithms to hierarchical clustering for the classification of 19 EU countries based on seven variables related to health resources in healthcare systems.

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Unsupervised Classification of Multivariate Time Series Data for the Identification of Sea Regimes

Topics in Theoretical and Applied Statistics ◽

10.1007/978-3-319-27274-0_6 ◽

2016 ◽

pp. 61-71

Author(s):

Mauro Bencivenga ◽

Francesco Lagona ◽

Antonello Maruotti ◽

Gabriele Nardone ◽

Marco Picone

Keyword(s):

Time Series ◽

Time Series Data ◽

Multivariate Time Series ◽

Unsupervised Classification ◽

Series Data

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A Versatile Approach to Classification of Multivariate Time Series Data

Proceedings of the 2015 Federated Conference on Computer Science and Information Systems ◽

10.15439/2015f419 ◽

2015 ◽

Cited By ~ 5

Author(s):

Adam Zagorecki

Keyword(s):

Time Series ◽

Time Series Data ◽

Multivariate Time Series ◽

Series Data

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Explainable AI Framework for Multivariate Hydrochemical Time Series

Machine Learning and Knowledge Extraction ◽

10.3390/make3010009 ◽

2021 ◽

Vol 3 (1) ◽

pp. 170-204

Author(s):

Michael C. Thrun ◽

Alfred Ultsch ◽

Lutz Breuer

Keyword(s):

Water Quality ◽

Time Series ◽

Time Series Data ◽

Distance Measure ◽

Multivariate Time Series ◽

Environmental Parameters ◽

Series Data ◽

Topographic Map ◽

Domain Experts ◽

Explainable Ai

The understanding of water quality and its underlying processes is important for the protection of aquatic environments. With the rare opportunity of access to a domain expert, an explainable AI (XAI) framework is proposed that is applicable to multivariate time series. The XAI provides explanations that are interpretable by domain experts. In three steps, it combines a data-driven choice of a distance measure with supervised decision trees guided by projection-based clustering. The multivariate time series consists of water quality measurements, including nitrate, electrical conductivity, and twelve other environmental parameters. The relationships between water quality and the environmental parameters are investigated by identifying similar days within a cluster and dissimilar days between clusters. The framework, called DDS-XAI, does not depend on prior knowledge about data structure, and its explanations are tendentially contrastive. The relationships in the data can be visualized by a topographic map representing high-dimensional structures. Two state of the art XAIs called eUD3.5 and iterative mistake minimization (IMM) were unable to provide meaningful and relevant explanations from the three multivariate time series data. The DDS-XAI framework can be swiftly applied to new data. Open-source code in R for all steps of the XAI framework is provided and the steps are structured application-oriented.

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Explainable AI Framework for Multivariate Hydrochemical Time Series

10.20944/preprints202011.0451.v1 ◽

2020 ◽

Author(s):

Michael Thrun ◽

Alfred Ultsch ◽

Lutz Breuer

Keyword(s):

Water Quality ◽

Time Series ◽

Time Series Data ◽

Distance Measure ◽

Multivariate Time Series ◽

Environmental Parameters ◽

Series Data ◽

Domain Expert ◽

Topographic Map ◽

Explainable Ai

The understanding of water quality and its underlying processes is important for the protection of aquatic environments enabling the rare opportunity of access to a domain expert. Hence, an explainable AI (XAI) framework is proposed that is applicable to multivariate time series resulting in explanations that are interpretable by a domain expert. The XAI combines in three steps a data-driven choice of a distance measure with explainable cluster analysis through supervised decision trees. The multivariate time series consists of water quality measurements, including nitrate, electrical conductivity, and twelve other environmental parameters. The relationships between water quality and the environmental parameters are investigated by identifying similar days within a cluster and dissimilar days between clusters. The XAI does not depend on prior knowledge about data structure, and its explanations are tendentially contrastive. The relationships in the data can be visualized by a topographic map representing high-dimensional structures. Two comparable decision-based XAIs were unable to provide meaningful and relevant explanations from the multivariate time series data. Open-source code in R for the three steps of the XAI framework is provided.

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Convex Hull Convolutive Non-Negative Matrix Factorization for Uncovering Temporal Patterns in Multivariate Time-Series Data

10.21437/interspeech.2016-571 ◽

2016 ◽

Cited By ~ 5

Author(s):

Colin Vaz ◽

Asterios Toutios ◽

Shrikanth S. Narayanan

Keyword(s):

Time Series ◽

Convex Hull ◽

Matrix Factorization ◽

Time Series Data ◽

Multivariate Time Series ◽

Temporal Patterns ◽

Series Data ◽

Non Negative Matrix Factorization

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Modeling Low-risk Actions from Multivariate Time Series Data Using Distributional Reinforcement Learning

2020 11th International Conference on Awareness Science and Technology (iCAST) ◽

10.1109/icast51195.2020.9319476 ◽

2020 ◽

Author(s):

Yosuke Sato ◽

Jianwei Zhang

Keyword(s):

Time Series ◽

Reinforcement Learning ◽

Time Series Data ◽

Multivariate Time Series ◽

Low Risk ◽

Series Data

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Time–frequency time–space LSTM for robust classification of physiological signals

Scientific Reports ◽

10.1038/s41598-021-86432-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Tuan D. Pham

Keyword(s):

Time Series ◽

Network Architecture ◽

Time Series Data ◽

Short Term Memory ◽

Physiological Signals ◽

Series Data ◽

Time Frequency ◽

Time Space ◽

Deep Recurrent Neural Network

AbstractAutomated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time–frequency and time–space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.

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High-pass filtering artifacts in multivariate classification of neural time series data

Journal of Neuroscience Methods ◽

10.1016/j.jneumeth.2021.109080 ◽

2021 ◽

Vol 352 ◽

pp. 109080

Author(s):

Joram van Driel ◽

Christian N.L. Olivers ◽

Johannes J. Fahrenfort

Keyword(s):

Time Series ◽

Time Series Data ◽

Series Data ◽

Multivariate Classification ◽

High Pass

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Change Point Enhanced Anomaly Detection for IoT Time Series Data

Water ◽

10.3390/w13121633 ◽

2021 ◽

Vol 13 (12) ◽

pp. 1633

Author(s):

Elena-Simona Apostol ◽

Ciprian-Octavian Truică ◽

Florin Pop ◽

Christian Esposito

Keyword(s):

Time Series ◽

Anomaly Detection ◽

Change Point ◽

Time Series Data ◽

Multivariate Time Series ◽

Change Point Detection ◽

Change Points ◽

Series Data ◽

Prediction And Forecasting ◽

Point Detection

Due to the exponential growth of the Internet of Things networks and the massive amount of time series data collected from these networks, it is essential to apply efficient methods for Big Data analysis in order to extract meaningful information and statistics. Anomaly detection is an important part of time series analysis, improving the quality of further analysis, such as prediction and forecasting. Thus, detecting sudden change points with normal behavior and using them to discriminate between abnormal behavior, i.e., outliers, is a crucial step used to minimize the false positive rate and to build accurate machine learning models for prediction and forecasting. In this paper, we propose a rule-based decision system that enhances anomaly detection in multivariate time series using change point detection. Our architecture uses a pipeline that automatically manages to detect real anomalies and remove the false positives introduced by change points. We employ both traditional and deep learning unsupervised algorithms, in total, five anomaly detection and five change point detection algorithms. Additionally, we propose a new confidence metric based on the support for a time series point to be an anomaly and the support for the same point to be a change point. In our experiments, we use a large real-world dataset containing multivariate time series about water consumption collected from smart meters. As an evaluation metric, we use Mean Absolute Error (MAE). The low MAE values show that the algorithms accurately determine anomalies and change points. The experimental results strengthen our assumption that anomaly detection can be improved by determining and removing change points as well as validates the correctness of our proposed rules in real-world scenarios. Furthermore, the proposed rule-based decision support systems enable users to make informed decisions regarding the status of the water distribution network and perform effectively predictive and proactive maintenance.

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Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building

Future Internet ◽

10.3390/fi13030067 ◽

2021 ◽

Vol 13 (3) ◽

pp. 67

Author(s):

Eric Hitimana ◽

Gaurav Bajpai ◽

Richard Musabe ◽

Louis Sibomana ◽

Jayavel Kayalvizhi

Keyword(s):

Machine Learning ◽

Time Series ◽

Deep Learning ◽

Time Series Data ◽

Multivariate Time Series ◽

Machine Learning Algorithms ◽

Series Data ◽

Support Vector ◽

Human Beings ◽

Feed Forward Network

Many countries worldwide face challenges in controlling building incidence prevention measures for fire disasters. The most critical issues are the localization, identification, detection of the room occupant. Internet of Things (IoT) along with machine learning proved the increase of the smartness of the building by providing real-time data acquisition using sensors and actuators for prediction mechanisms. This paper proposes the implementation of an IoT framework to capture indoor environmental parameters for occupancy multivariate time-series data. The application of the Long Short Term Memory (LSTM) Deep Learning algorithm is used to infer the knowledge of the presence of human beings. An experiment is conducted in an office room using multivariate time-series as predictors in the regression forecasting problem. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. The information collected was applied to the LSTM algorithm and compared with other machine learning algorithms. The compared algorithms are Support Vector Machine, Naïve Bayes Network, and Multilayer Perceptron Feed-Forward Network. The outcomes based on the parametric calibrations demonstrate that LSTM performs better in the context of the proposed application.

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