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Forecasting Multivariate Time Series with the Theta Method [PDF]
Journal of Forecasting, 2015 AbstractIn this study building on earlier work on the properties and performance of the univariate Theta method for a unit root data‐generating process we: (a) derive new theoretical formulations for the application of the method on multivariate time series; (b) investigate the conditions for which the multivariate Theta method is expected to forecast ...
Dimitrios D. Thomakos +1 more
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Skip-RCNN: A Cost-Effective Multivariate Time Series Forecasting Model
IEEE Access, 2023 Multivariate time series (MTS) forecasting is a crucial aspect in many classification and regression tasks. In recent years, deep learning models have become the mainstream framework for MTS forecasting. Among these deep learning methods, the transformer
Haitao Song +6 more
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Hierarchical Joint Graph Learning and Multivariate Time Series Forecasting
IEEE Access, 2023 Multivariate time series is prevalent in many scientific and industrial domains. Modeling multivariate signals is challenging due to their long-range temporal dependencies and intricate interactions–both direct and indirect.
Juhyeon Kim +5 more
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Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs [PDF]
, 2022 Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and traffic prediction.
Chen, Siheng +11 more
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Temporal pattern attention for multivariate time series forecasting [PDF]
Machine Learning, 2019 Forecasting multivariate time series data, such as prediction of electricity consumption, solar power production, and polyphonic piano pieces, has numerous valuable applications. However, complex and non-linear interdependencies between time steps and series complicate the task. To obtain accurate prediction, it is crucial to model long-term dependency
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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Transformers in multivariate time series forecasting: a review [PDF]
ریاضی و جامعهLong-term forecasting of multivariate time series is a fundamental challenge in the field of machine learning, with critical applications in numerous domains such as energy, transportation, and financial markets.
Esmaeil Chitgar +2 more
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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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A Neural Networks Based Method for Multivariate Time-Series Forecasting
IEEE Access, 2021 In recent years, more and more deep neural network methods have been used in the forecasting research of multivariate time series. Comparing to the traditional methods such as autoregressive models, methods based on neural networks have achieved superior
Shaowei Li, He Huang, Wei Lu
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DTMamba : Dual Twin Mamba for Time Series Forecasting
Tsinghua Science and TechnologyLong-term Time Series Forecasting (LTSF) has always been an important task where models need to effectively capture hidden patterns in the time series in order to make accurate predictions about future states.
Zexue Wu +3 more
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