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Multivariate Time Series Information Bottleneck
Entropy, 2023 Time series (TS) and multiple time series (MTS) predictions have historically paved the way for distinct families of deep learning models. The temporal dimension, distinguished by its evolutionary sequential aspect, is usually modeled by decomposition ...
Denis Ullmann +2 more
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Multivariate Time Series Similarity Searching [PDF]
The Scientific World Journal, 2014 Multivariate time series (MTS) datasets are very common in various financial, multimedia, and hydrological fields. In this paper, a dimension-combination method is proposed to search similar sequences for MTS.
Jimin Wang +4 more
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Multivariate epidemic count time series model.
PLoS ONE, 2023 An infectious disease spreads not only over a single population or community but also across multiple and heterogeneous communities. Moreover, its transmissibility varies over time because of various factors such as seasonality and epidemic control ...
Shinsuke Koyama
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Forecasting time series with multivariate copulas [PDF]
Dependence Modeling, 2015 Abstract In this paper we present a forecasting method for time series using copula-based models for multivariate time series. We study how the performance of the predictions evolves when changing the strength of the different possible dependencies, as well as the structure of the dependence.
Simard Clarence, Rémillard Bruno
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Pre-trained multi-scale RWKV-GCN for multivariate time series forecasting [PDF]
Scientific ReportsMultivariate time series forecasting faces two key challenges: capturing intra-series temporal dependencies and inter-series spatial dependencies. However, heterogeneous cross-scale correlations and noise from unrelated series may obscure temporal ...
Jianhua Hao, Fangai Liu, Weiwei Zhang
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A multiscale model for multivariate time series forecasting [PDF]
Scientific ReportsTransformer based models for time-series forecasting have shown promising performance and during the past few years different Transformer variants have been proposed in time-series forecasting domain.
Vahid Naghashi +2 more
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Multivariate Time Series Anomaly Detection Based on Inverted Transformer with Multivariate Memory Gate [PDF]
EntropyIn the industrial IoT, it is vital to detect anomalies in multivariate time series, yet it faces numerous challenges, including highly imbalanced datasets, complex and high-dimensional data, and large disparities across variables.
Yuan Ma +5 more
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Multivariate analysis in vector time series [PDF]
, 2000 This paper reviews the applications of classical multivariate techniques for discrimination, clustering and dimension reduction for time series data. It is shown that the discrimination problem can be seen as a model selection problem.
Galeano, Pedro, Peña, Daniel
core +7 more sources
FreFilterTST: a dynamic channel graph sparsification approach to multivariate time series anomaly detection with frequency-domain restoration [PDF]
Scientific ReportsThe scope of time series anomaly detection is increasingly shifting from univariate to multivariate contexts, as a growing number of real-world problems can no longer be adequately addressed by analyzing individual variables in isolation.
Yi Wang, Jian Jie Zhang, Ming Yang Zhang
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Eigen-entropy based time series signatures to support multivariate time series classification [PDF]
Scientific ReportsMost current algorithms for multivariate time series classification tend to overlook the correlations between time series of different variables. In this research, we propose a framework that leverages Eigen-entropy along with a cumulative moving window ...
Abhidnya Patharkar +6 more
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