Spatial-Temporal Convolutional Transformer Network for Multivariate Time Series Forecasting [PDF]
Sensors, 2022 Multivariate time series forecasting has long been a research hotspot because of its wide range of application scenarios. However, the dynamics and multiple patterns of spatiotemporal dependencies make this problem challenging.
Lei Huang +3 more
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Multiview Spatial-Temporal Meta-Learning for Multivariate Time Series Forecasting [PDF]
SensorsMultivariate time series modeling has been essential in sensor-based data mining tasks. However, capturing complex dynamics caused by intra-variable (temporal) and inter-variable (spatial) relationships while simultaneously taking into account evolving ...
Liang Zhang +3 more
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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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VCformer: Variable-Centric Multi-Scale Transformer for Multivariate Time Series Forecasting [PDF]
SensorsMultivariate time series forecasting is crucial for numerous practical applications ranging from financial markets to climate monitoring. Traditional multivariate time series forecasting methods primarily adopt a time-centric modeling paradigm, applying ...
Junyu Zhu +6 more
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Consistency regularization for few shot multivariate time series forecasting [PDF]
Scientific ReportsMultivariate time series forecasting aims to accurately predict future trends by capturing and analyzing various features of the time series. Adequate training data are crucial for ensuring the model’s generalizability.
Yumei She +5 more
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4D hypercomplex-valued neural network in multivariate time series forecasting [PDF]
Scientific ReportsThe goal of this paper is to test three classes of neural network (NN) architectures based on four-dimensional (4D) hypercomplex algebras for multivariate time series forecasting.
Radosław Kycia, Agnieszka Niemczynowicz
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FCP-Former: Enhancing Long-Term Multivariate Time Series Forecasting with Frequency Compensation [PDF]
SensorsLong-term multivariate time series forecasting is crucial for real-world applications, including energy consumption, traffic flow, healthcare, and finance.
Ming Li +5 more
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Improving long-term multivariate time series forecasting with a seasonal-trend decomposition-based 2-dimensional temporal convolution dense network [PDF]
Scientific ReportsImproving the accuracy of long-term multivariate time series forecasting is important for practical applications. Various Transformer-based solutions emerging for time series forecasting.
Jianhua Hao, Fangai Liu
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A novel extreme adaptive GRU for multivariate time series forecasting [PDF]
Scientific ReportsMultivariate time series forecasting is a critical problem in many real-world scenarios. Recent advances in deep learning have significantly enhanced the ability to tackle such problems.
Yifan Zhang +3 more
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