VAR Model Based Clustering Method for Multivariate Time Series Data

2019 ◽  
Vol 237 (6) ◽  
pp. 754-765 ◽  
Author(s):  
S. Deb
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Series Data ◽  
Var Model ◽  
Clustering Method ◽  
Model Based Clustering ◽  
Model Based

scholarly journals Dynamic Modelling of Sharia-Based Corporate, Islamic Index and Exchange Rate: VAR Model Application

2020 ◽  
Vol 6 (2) ◽  
pp. 195
Author(s):  
Hasrun Afandi Umpusinga ◽  
Atika Riasari ◽  
Fajrin Satria Dwi Kesumah
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Dynamic Modelling ◽  
Series Data ◽  
Var Model ◽  
Suitable Model ◽  
Data Set ◽  
Dynamic Relationship

Indonesia is one of largest users of sharia-based compliant recently which bring into many concerns how the sharia stocks listing in the most valuable sharia stocks index in Indonesia perform and correlate with other variables, particularly exchange rates. The study aims to analysis the causal relationship and to forecast the performances of sharia-based stocks and its Islamic index in Indonesia along with the volatility of exchange rate. Vector Autoregressive (VAR) model is applied as the method to analyse the multivariate time series as it is believed as the suitable model in predicting such time-series data in the scope of multivariate variables. The finding suggests VAR(1) model is the fitted model as such to both analyse its dynamic relationship and forecast the data set for the next 24 weeks. While the prediction shows the JII has an increasing data, both ANTM and EXR are predicted to have a stable volatility. In addition, granger causality defines variables to have effect in its respective variables, and IRF describes the shocks in one variable cause another variable is relatively difficult in reaching its zero condition in short-term period.

scholarly journals Maximum Pseudolikelihood Estimation for Model-Based Clustering of Time Series Data

2017 ◽  
Vol 29 (4) ◽  
pp. 990-1020 ◽  
Author(s):  
Hien D. Nguyen ◽  
Geoffrey J. McLachlan ◽  
Pierre Orban ◽  
Pierre Bellec ◽  
Andrew L. Janke
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Expectation Maximization Algorithm ◽  
Resting State Fmri ◽  
Series Data ◽  
Ml Estimation ◽  
Model Based Clustering ◽  
Model Based ◽  
Ml Estimator ◽  
Maximum Pseudolikelihood

Mixture of autoregressions (MoAR) models provide a model-based approach to the clustering of time series data. The maximum likelihood (ML) estimation of MoAR models requires evaluating products of large numbers of densities of normal random variables. In practical scenarios, these products converge to zero as the length of the time series increases, and thus the ML estimation of MoAR models becomes infeasible without the use of numerical tricks. We propose a maximum pseudolikelihood (MPL) estimation approach as an alternative to the use of numerical tricks. The MPL estimator is proved to be consistent and can be computed with an EM (expectation-maximization) algorithm. Simulations are used to assess the performance of the MPL estimator against that of the ML estimator in cases where the latter was able to be calculated. An application to the clustering of time series data arising from a resting state fMRI experiment is presented as a demonstration of the methodology.

scholarly journals Toeplitz Inverse Covariance-based Clustering of Multivariate Time Series Data

2018 ◽  
Author(s):  
David Hallac ◽  
Sagar Vare ◽  
Stephen Boyd ◽  
Jure Leskovec
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Graphical Representation ◽  
State Of The Art ◽  
Multivariate Time Series ◽  
Series Data ◽  
Temporal Data ◽  
Scalable Algorithm ◽  
Model Based Clustering ◽  
Markov Random

Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters. However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series. Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC). Each cluster in the TICC method is defined by a correlation network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster. Based on this graphical representation, TICC simultaneously segments and clusters the time series data. We solve the TICC problem through a scalable algorithm that is able to efficiently solve for tens of millions of observations. We validate our approach by comparing TICC to several state-of-the-art baselines in a series of synthetic experiments, and we then demonstrate on an automobile dataset how TICC can be used to learn interpretable clusters in real-world scenarios.

scholarly journals Change Point Enhanced Anomaly Detection for IoT Time Series Data

Water ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document