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- W4378473497 abstract "To enhance predicting performance while minimizing computational demands, this paper introduces a joint time-frequency domain Transformer (JTFT) for multivariate forecasting. The method exploits the sparsity of time series in the frequency domain using a small number of learnable frequencies to extract temporal dependencies effectively. Alongside the frequency domain representation, a fixed number of the most recent data points are directly encoded in the time domain, bolstering the learning of local relationships and mitigating the adverse effects of non-stationarity. JTFT achieves linear complexity since the length of the internal representation remains independent of the input sequence length. Additionally, a low-rank attention layer is proposed to efficiently capture cross-dimensional dependencies and prevent performance degradation due to the entanglement of temporal and channel-wise modeling. Experiments conducted on six real-world datasets demonstrate that JTFT outperforms state-of-the-art methods." @default.
- W4378473497 created "2023-05-27" @default.
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- W4378473497 date "2023-05-23" @default.
- W4378473497 modified "2023-09-28" @default.
- W4378473497 title "A Joint Time-frequency Domain Transformer for Multivariate Time Series Forecasting" @default.
- W4378473497 doi "https://doi.org/10.48550/arxiv.2305.14649" @default.
- W4378473497 hasPublicationYear "2023" @default.
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