Efficient Time Series Processing for Transformers and State-Space Models through Token Merging
Keywords: Token Merging, Efficient Time Series Processing, Transformers, State-Space Models
TL;DR: We introduce local merging, a domain-specific causal token merging algorithm, to boost the efficiency of time series models by up to 54 times while maintaining prediction quality.
Abstract: Transformer architectures have shown promising results in time series processing. However, despite recent advances in subquadratic attention mechanisms or state-space models, processing very long sequences still imposes significant computational requirements. Token merging, which involves replacing multiple tokens with a single one calculated as their linear combination, has shown to considerably improve the throughput of vision transformer architectures while maintaining accuracy. In this work, we go beyond computer vision and perform the first investigations of token merging in time series analysis on both time series transformers and state-space models. We further introduce local merging, a domain-specific token merging algorithm that selectively combines tokens within a local neighborhood, achieving two major benefits: a) Local merging can adjust its the computational complexity from quadratic to linear based on the neighborhood size to effectively scale token merging to long sequences; b) Local merging is the first causal merging scheme enabling token merging in transformer decoders. Our comprehensive empirical evaluation demonstrates that token merging offers substantial computational benefits with minimal impact on accuracy across various models and datasets. On the recently proposed Chronos foundation model, we achieve accelerations up to 5400% with only minor accuracy degradations.
Primary Area: learning on time series and dynamical systems
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Submission Number: 907
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