Results 131 to 140 of about 223,849 (180)

STF-DKANMixer: Tri-component decomposition with KAN-MLP hybrid architecture for time series forecasting. [PDF]

PLoS One
Wei J, Guo R, Wang Y.
europepmc   +1 more source

A new automatic forecasting method based on explainable deep dendritic artificial neural network. [PDF]

Sci Rep
Bas E, Egrioglu E.
europepmc   +1 more source

Redefining multi-target weather forecasting with a novel deep learning model: Hierarchical temporal convolutional long short-term memory with attention (HTC-LSTM-Attn) in Bangladesh. [PDF]

PLoS One
Kabir MA, Chakma C.
europepmc   +1 more source

A Channel-Independent Anchor Graph-Regularized Broad Learning System for Industrial Soft Sensors. [PDF]

Entropy (Basel)
Zhang Z, Yang M, Xie C, Xu Z, Yin P.
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FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective

Neural Information Processing Systems, 2023
Multivariate time series (MTS) forecasting has shown great importance in numerous industries. Current state-of-the-art graph neural network (GNN)-based forecasting methods usually require both graph networks (e.g., GCN) and temporal networks (e.g., LSTM)
Kun Yi, Qi Zhang, Wei Fan, Hui He, Liang Hu, Pengyang Wang, Ning An, Longbin Cao, Zhendong Niu   +8 more
semanticscholar   +1 more source

MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting

AAAI Conference on Artificial Intelligence, 2023
Multivariate time series forecasting poses an ongoing challenge across various disciplines. Time series data often exhibit diverse intra-series and inter-series correlations, contributing to intricate and interwoven dependencies that have been the focus ...
Wanlin Cai, Yuxuan Liang, Xianggen Liu, Jianshuai Feng, Yuankai Wu   +4 more
semanticscholar   +1 more source

MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixing

IEEE International Joint Conference on Neural Network, 2023
Multivariate time series forecasting has been widely used in various practical scenarios. Recently, Transformer-based models have shown significant potential in forecasting tasks due to the capture of long-range dependencies.
Zhe Li, Zhongwen Rao, Lujia Pan, Zenglin Xu   +3 more
semanticscholar   +1 more source