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• W4313505511 abstract "Multivariate time series prediction models perform the required operation on a specific window length of a given input. However, capturing complex and nonlinear interdependencies in each temporal window remains challenging. The typical attention mechanisms assign a weight for a variable at the same time or the features of each previous time step to capture spatio-temporal correlations. However, it fails to directly extract each time step’s relevant features that affect future values to learn the spatio-temporal pattern from a global perspective. To this end, a temporal window attention-based window-dependent long short-term memory network (TWA-WDLSTM) is proposed to enhance the temporal dependencies, which exploits the encoder–decoder framework. In the encoder, we design a temporal window attention mechanism to select relevant exogenous series in a temporal window. Furthermore, we introduce a window-dependent long short-term memory network (WDLSTM) to encode the input sequences in a temporal window into a feature representation and capture very long term dependencies. In the decoder, we use WDLSTM to generate the prediction values. We applied our model to four real-world datasets in comparison to a variety of state-of-the-art models. The experimental results suggest that TWA-WDLSTM can outperform comparison models. In addition, the temporal window attention mechanism has good interpretability. We can observe which variable contributes to the future value." @default.
• W4313505511 created "2023-01-06" @default.
• W4313505511 creator A5043603162 @default.
• W4313505511 creator A5072710254 @default.
• W4313505511 date "2022-12-21" @default.
• W4313505511 modified "2023-10-18" @default.
• W4313505511 title "A Temporal Window Attention-Based Window-Dependent Long Short-Term Memory Network for Multivariate Time Series Prediction" @default.
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• W4313505511 doi "https://doi.org/10.3390/e25010010" @default.
• W4313505511 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36673150" @default.
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