Infomaxformer: Maximum Entropy Transformer for Long Time-Series Forecasting Problem

• URL: http://arxiv.org/abs/2301.01772v1
• Date: Wed, 4 Jan 2023 14:08:21 GMT
• Title: Infomaxformer: Maximum Entropy Transformer for Long Time-Series Forecasting Problem
• Authors: Peiwang Tang and Xianchao Zhang
• Abstract summary: The Transformer architecture yields state-of-the-art results in many tasks such as natural language processing (NLP) and computer vision (CV) With this advanced capability, however, the quadratic time complexity and high memory usage prevents the Transformer from dealing with long time-series forecasting problem. We propose a method that combines the encoder-decoder architecture with seasonal-trend decomposition to capture more specific seasonal parts.
• Score: 6.497816402045097
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Transformer architecture yields state-of-the-art results in many tasks such as natural language processing (NLP) and computer vision (CV), since the ability to efficiently capture the precise long-range dependency coupling between input sequences. With this advanced capability, however, the quadratic time complexity and high memory usage prevents the Transformer from dealing with long time-series forecasting problem (LTFP). To address these difficulties: (i) we revisit the learned attention patterns of the vanilla self-attention, redesigned the calculation method of self-attention based the Maximum Entropy Principle. (ii) we propose a new method to sparse the self-attention, which can prevent the loss of more important self-attention scores due to random sampling.(iii) We propose Keys/Values Distilling method motivated that a large amount of feature in the original self-attention map is redundant, which can further reduce the time and spatial complexity and make it possible to input longer time-series. Finally, we propose a method that combines the encoder-decoder architecture with seasonal-trend decomposition, i.e., using the encoder-decoder architecture to capture more specific seasonal parts. A large number of experiments on several large-scale datasets show that our Infomaxformer is obviously superior to the existing methods. We expect this to open up a new solution for Transformer to solve LTFP, and exploring the ability of the Transformer architecture to capture much longer temporal dependencies.

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