Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2023 Multivariate Time Series (MTS) analysis is crucial to understanding and managing complex systems, such as traffic and energy systems, and a variety of approaches to MTS forecasting have been proposed recently.
Zezhi Shao +11 more
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Temporal pattern attention for multivariate time series forecasting [PDF]
Machine-mediated learning, 2018 Forecasting of multivariate time series data, for instance the prediction of electricity consumption, solar power production, and polyphonic piano pieces, has numerous valuable applications.
Shun-Yao Shih, Fan-Keng Sun, Hung-yi Lee
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Multivariate time series prediction of high dimensional data based on deep reinforcement learning [PDF]
E3S Web of Conferences, 2021 In order to improve the prediction accuracy of high-dimensional data time series, a high-dimensional data multivariate time series prediction method based on deep reinforcement learning is proposed. The deep reinforcement learning method is used to solve
Ji Xin +5 more
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The Capacity and Robustness Trade-Off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2023 Multivariate time series data comprises various channels of variables. The multivariate forecasting models need to capture the relationship between the channels to accurately predict future values.
Lu Han, Han-Jia Ye, De-chuan Zhan
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Multivariate Time Series Deep Spatiotemporal Forecasting with Graph Neural Network
Applied Sciences, 2022 Multivariate time series forecasting has long been a subject of great concern. For example, there are many valuable applications in forecasting electricity consumption, solar power generation, traffic congestion, finance, and so on.
Zichao He, Chunna Zhao, Yaqun Huang
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Multivariate dynamic kernels for financial time series forecasting [PDF]
, 2016 The final publication is available at http://link.springer.com/chapter/10.1007/978-3-319-44781-0_40We propose a forecasting procedure based on multivariate dynamic kernels, with the capability of integrating information measured at different frequencies ...
AJ Smola +6 more
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Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting [PDF]
Knowledge Discovery and Data Mining, 2022 Recent studies have shown great promise in applying graph neural networks for multivariate time series forecasting, where the interactions of time series are described as a graph structure and the variables are represented as the graph nodes.
Junchen Ye +6 more
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Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting-Full Version [PDF]
International Joint Conference on Artificial Intelligence, 2022 A variety of real-world applications rely on far future information to make decisions, thus calling for efficient and accurate long sequence multivariate time series forecasting.
Razvan-Gabriel Cirstea +5 more
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Multi-step CNN forecasting for COVID-19 multivariate time-series
IJAIN (International Journal of Advances in Intelligent Informatics), 2023 The new coronavirus (COVID-19) has spread to over 200 countries, with over 36 million confirmed cases as of October 10, 2020. As a result, numerous machine learning models capable of forecasting the epidemic worldwide have been produced.
Haviluddin Haviluddin, Rayner Alfred
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Multivariate Time Series Forecasting With Dynamic Graph Neural ODEs [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2022 Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and traffic prediction.
Ming Jin +5 more
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