mlsanityData to 7 Oct 2026

Enhancing Transformer Efficiency for Multivariate Time Series Classification

arXiv 2203.14472 ↗First posted 28 Mar 2022cs.LG cs.AIcited by 4

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Abstract (arXiv)

Most current multivariate time series (MTS) classification algorithms focus on improving the predictive accuracy. However, for large-scale (either high-dimensional or long-sequential) time series (TS) datasets, there is an additional consideration: to design an efficient network architecture to reduce computational costs such as training time and memory footprint. In this work we propose a methodology based on module-wise pruning and Pareto analysis to investigate the relationship between model efficiency and accuracy, as well as its complexity. Comprehensive experiments on benchmark MTS datasets illustrate the effectiveness of our method.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 24 of its 44 references that are in the map. First posted on arXiv 28 Mar 2022.

Builds on

24 of 44 references.

References · 24

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12 more
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Cited by year

Citing papers per year: 2023: 2; 2024: 2.

4 papers.

  1. 2023TRAM: Benchmarking Temporal Reasoning for Large Language Modelscited by 39
  2. 2023Gemini in Reasoning: Unveiling Commonsense in Multimodal Large Language Modelscited by 15
  3. 2024RUPBench: Benchmarking Reasoning Under Perturbations for Robustness Evaluation in Large Language Modelscited by 13
  4. 2024Causal and Local Correlations Based Network for Multivariate Time Series Classificationcited by 0