ModernTCN: A Modern Pure Convolution Structure for General Time Series Analysis
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Keywords: Time Series Analysis, Deep Learning
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TL;DR: We take a seldom-explored way in time series community to successfully bring convolution back to time series analysis. Our pure convolution structure achieves consistent state-of-the-art in five mainstream time series analysis tasks.
Abstract: Recently, Transformer-based and MLP-based models have emerged rapidly and
won dominance in time series analysis. In contrast, convolution is losing steam
in time series tasks nowadays for inferior performance. This paper studies the
open question of how to better use convolution in time series analysis and makes
efforts to bring convolution back to the arena of time series analysis. To this end,
we modernize the traditional TCN and conduct time series related modifications
to make it more suitable for time series tasks. As the outcome, we propose
ModernTCN and successfully solve this open question through a seldom-explored
way in time series community. As a pure convolution structure, ModernTCN still
achieves the consistent state-of-the-art performance on five mainstream time series
analysis tasks while maintaining the efficiency advantage of convolution-based
models, therefore providing a better balance of efficiency and performance than
state-of-the-art Transformer-based and MLP-based models. Our study further
reveals that, compared with previous convolution-based models, our ModernTCN
has much larger effective receptive fields (ERFs), therefore can better unleash the
potential of convolution in time series analysis. Code is available at this repository:
https://github.com/luodhhh/ModernTCN.
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Primary Area: representation learning for computer vision, audio, language, and other modalities
Submission Number: 5228
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