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A multiscale model for multivariate time series forecasting [PDF]

Scientific Reports
Transformer 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, Mounir Boukadoum, Abdoulaye Banire Diallo   +2 more
doaj   +4 more sources

Explicit Future Pattern-Enhanced Multivariate Time Series Forecasting [PDF]

Entropy
Multivariate time series forecasting requires models to infer future values and how the temporal structure evolves beyond the observation boundary. A central challenge is to define this evolution as an intermediate prediction and connect it to value ...
Yaokang Li, Yunan Wei, Jing Cai, Wenbin Cheng, Yiting Lin   +4 more
doaj   +2 more sources

MixNet: A scale-adaptive method for multivariate time series forecasting. [PDF]

PLoS ONE
Time series forecasting is a critical task with widespread applications in industrial domains and daily life, including weather prediction, long-term energy consumption planning, and marketing analysis.
Xinhan Wang, Bowen Zhao
doaj   +3 more sources

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, Feng Mao, Kai Zhang, Zhiheng Li   +3 more
doaj   +2 more sources

Multiview Spatial-Temporal Meta-Learning for Multivariate Time Series Forecasting [PDF]

Sensors
Multivariate 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, Jianping Zhu, Bo Jin, Xiaopeng Wei   +3 more
doaj   +2 more sources

Pre-trained multi-scale RWKV-GCN for multivariate time series forecasting [PDF]

Scientific Reports
Multivariate 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
doaj   +2 more sources

VCformer: Variable-Centric Multi-Scale Transformer for Multivariate Time Series Forecasting [PDF]

Sensors
Multivariate 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, Enguang Zuo, Xinyu Bi, Chen Chen, Cheng Chen, Ziwei Yan, Xiaoyi Lv   +6 more
doaj   +2 more sources

4D hypercomplex-valued neural network in multivariate time series forecasting [PDF]

Scientific Reports
The 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
doaj   +2 more sources

Consistency regularization for few shot multivariate time series forecasting [PDF]

Scientific Reports
Multivariate 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, Yi Hong, Shikai Shen, Bin Yang, Liyi Zhang, Jianxiao Wang   +5 more
doaj   +2 more sources

FCP-Former: Enhancing Long-Term Multivariate Time Series Forecasting with Frequency Compensation [PDF]

Sensors
Long-term multivariate time series forecasting is crucial for real-world applications, including energy consumption, traffic flow, healthcare, and finance.
Ming Li, Muyu Yang, Shaolong Chen, Huangyongxiang Li, Gaosong Xing, Shuting Li   +5 more
doaj   +2 more sources