Change-point methods for multivariate time-series: paired vectorial observations
Sign Up to like & getrecommendations! Published in 2020 at "Statistical Papers"
DOI: 10.1007/s00362-020-01175-3
Abstract: We consider paired and two-sample break-detection procedures for vectorial observations and multivariate time series. The new methods involve L2-type criteria based on empirical characteristic functions and are easy to compute regardless of dimension. We obtain… read more here.
Keywords: time series; change point; time; series ... See more keywords
Multivariate multiscale increment entropy: a complexity measure for detecting flow pattern transition in multiphase flows
Sign Up to like & getrecommendations! Published in 2020 at "Nonlinear Dynamics"
DOI: 10.1007/s11071-020-05733-0
Abstract: Multivariate time series are routinely measured in real complex systems, and effective multivariate multiscale methods for uncovering the complexity of multivariate complex systems are certainly needed. In this paper, a new complexity measure for multiscale… read more here.
Keywords: multivariate; time series; multivariate time; complexity ... See more keywords
Tri-Partition State Alphabet-Based Sequential Pattern for Multivariate Time Series
Sign Up to like & getrecommendations! Published in 2021 at "Cognitive Computation"
DOI: 10.1007/s12559-021-09871-4
Abstract: Recently, the advancement of cognitive computing and three-way decisions has enabled in-depth sequential pattern understanding through temporal association analysis. The main challenge is to obtain concise patterns that express richer semantics for multivariate time series… read more here.
Keywords: tri partition; multivariate time; tri; sequential pattern ... See more keywords
BiLSTM model based on multivariate time series data in multiple field for forecasting trading area
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Ambient Intelligence and Humanized Computing"
DOI: 10.1007/s12652-019-01398-9
Abstract: An artificial neural network-based model is widely used for analyzing and predicting multivariate time series data. However, the study on the analysis and prediction of multivariate time series data in multiple fields has limitations in… read more here.
Keywords: series data; time; time series; model ... See more keywords
Stationary vine copula models for multivariate time series
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Econometrics"
DOI: 10.1016/j.jeconom.2021.11.015
Abstract: Multivariate time series exhibit two types of dependence: across variables and across time points. Vine copulas are graphical models for the dependence and can conveniently capture both types of dependence in the same model. We… read more here.
Keywords: stationary vine; time; time series; multivariate time ... See more keywords
Complexity of couplings in multivariate time series via ordinal persistent homology.
Sign Up to like & getrecommendations! Published in 2023 at "Chaos"
DOI: 10.1063/5.0136772
Abstract: We propose a new measure of the complexity of couplings in multivariate time series by combining the techniques of ordinal pattern analysis and topological data analysis. We construct an increasing sequence of simplicial complexes encoding… read more here.
Keywords: complexity couplings; multivariate time; complexity; time series ... See more keywords
A sliding window-based multi-stage clustering and probabilistic forecasting approach for large multivariate time series data
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Statistical Computation and Simulation"
DOI: 10.1080/00949655.2017.1299151
Abstract: ABSTRACT Time series data analysis, such as temporal pattern recognition and trend forecasting, plays an increasingly significant part in temporal data statistics and analytics. Yet challenges still exist in the efficiency of pattern extracting and… read more here.
Keywords: stage clustering; multivariate time; time; time series ... See more keywords
An Ensemble Model Based on Adaptive Noise Reducer and Over-Fitting Prevention LSTM for Multivariate Time Series Forecasting
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2900371
Abstract: Multivariate time series forecasting recently has received extensive attention with its wide application in finance, transportation, environment, and so on. However, few of the currently developed models have considered the impact of noise on prediction.… read more here.
Keywords: time; time series; model; multivariate time ... See more keywords
Pseudo Bidirectional Linear Discriminant Analysis for Multivariate Time Series Classification
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DOI: 10.1109/access.2021.3089839
Abstract: Multivariate time series (MTS) is a kind of matrix data, typically consisting of multiple variables measured at multiple time points. Due to the high dimensionality of MTS data, many methods for MTS classification have been… read more here.
Keywords: time; linear discriminant; bidirectional linear; time series ... See more keywords
Hybrid Anomaly Detection via Multihead Dynamic Graph Attention Networks for Multivariate Time Series
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DOI: 10.1109/access.2022.3167640
Abstract: In the real world, a large number of multivariate time series data are generated by Internet of Things systems, which are composed of many connected sensing devices. Therefore, it is impractical to consider only a… read more here.
Keywords: time; anomaly detection; multivariate time; time series ... See more keywords
Robust Unsupervised Anomaly Detection with Variational Autoencoder in Multivariate Time Series Data
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3178592
Abstract: Accurate detection of anomalies in multivariate time series data has attracted much attention due to its importance in a wide range of applications. Since it is difficult to obtain accurately labeled data, many unsupervised anomaly… read more here.
Keywords: time; time series; detection; series data ... See more keywords