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<p>The era of the information explosion prompted the accumulation of a tremendous amount of time-series data. Multivariate Time Series (MTS) data involve multiple variables evolving over time, presenting a more intricate structure than univariate counterparts. Traditional statistical methods, while valuable, may face limitations in capturing the intricate dependencies and patterns inherent in such data. The advent of deep learning has opened new avenues for tackling the challenges posed by multivariate time series analysis. Deep learning techniques, with their ability to automatically learn hierarchical representations and intricate temporal dependencies, offer a promising approach to unraveling the complexities within these dynamic datasets. The state-of-the-art algorithms, <i>viz.,</i> Recurrent Neural Networks (RNNs) and their variants, such as Long Short Term Memory (LSTM), have achieved a decent performance in dealing with some of the challenges such as identifying and understanding seasonal components, temporal dependence within time series and between the time series, anomaly identification, data scarcity, and missing values <i>etc</i>. This paper aims to explore the landscape of multivariate time series analysis, explicitly focusing on leveraging deep learning methodologies, identifying challenges, and discussing some of the potential deep learning models and presents future research directions that serve as a reference for MTS analysis problems such as MTS anomaly detection, model selection, time series alignment, and dimensionality reduction.</p>

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Deep learning models in multivariate time series analysis: comprehensive analysis, challenges, research opportunities and future prospects

  • 期刊论文

  • 发表日期：

    2026年7月30日

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  • M. K. Saravana,
  • M. S. Roopa,
  • J. S. Arunalatha,
  • K. R. Venugopal

摘要

The era of the information explosion prompted the accumulation of a tremendous amount of time-series data. Multivariate Time Series (MTS) data involve multiple variables evolving over time, presenting a more intricate structure than univariate counterparts. Traditional statistical methods, while valuable, may face limitations in capturing the intricate dependencies and patterns inherent in such data. The advent of deep learning has opened new avenues for tackling the challenges posed by multivariate time series analysis. Deep learning techniques, with their ability to automatically learn hierarchical representations and intricate temporal dependencies, offer a promising approach to unraveling the complexities within these dynamic datasets. The state-of-the-art algorithms, viz., Recurrent Neural Networks (RNNs) and their variants, such as Long Short Term Memory (LSTM), have achieved a decent performance in dealing with some of the challenges such as identifying and understanding seasonal components, temporal dependence within time series and between the time series, anomaly identification, data scarcity, and missing values etc. This paper aims to explore the landscape of multivariate time series analysis, explicitly focusing on leveraging deep learning methodologies, identifying challenges, and discussing some of the potential deep learning models and presents future research directions that serve as a reference for MTS analysis problems such as MTS anomaly detection, model selection, time series alignment, and dimensionality reduction.