Papers › Take an Irregular Route: Enhance the Decoder of Time-Series Forecasting Transformer

Take an Irregular Route: Enhance the Decoder of Time-Series Forecasting Transformer

10 Dec 2023arXiv:2312.05792archive 2025-07-28

Li Shen, Yuning Wei, Yangzhu Wang, Hongguang Li

With the development of Internet of Things (IoT) systems, precise long-term forecasting method is requisite for decision makers to evaluate current statuses and formulate future policies. Currently, Transformer and MLP are two paradigms for deep time-series forecasting and the former one is more prevailing in virtue of its exquisite attention mechanism and encoder-decoder architecture. However, data scientists seem to be more willing to dive into the research of encoder, leaving decoder unconcerned. Some researchers even adopt linear projections in lieu of the decoder to reduce the complexity. We argue that both extracting the features of input sequence and seeking the relations of input and prediction sequence, which are respective functions of encoder and decoder, are of paramount significance. Motivated from the success of FPN in CV field, we propose FPPformer to utilize bottom-up and top-down architectures respectively in encoder and decoder to build the full and rational hierarchy. The cutting-edge patch-wise attention is exploited and further developed with the combination, whose format is also different in encoder and decoder, of revamped element-wise attention in this work. Extensive experiments with six state-of-the-art baselines on twelve benchmarks verify the promising performances of FPPformer and the importance of elaborately devising decoder in time-series forecasting Transformer. The source code is released in https://github.com/OrigamiSL/FPPformer.

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Decoder Time Series Time Series Forecasting

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1x1 Convolution Absolute Position Encodings Adam Attention BPE Convolution Dense Connections Dropout FPN Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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