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A multiscale model for multivariate time series forecasting.

Vahid Naghashi1, Mounir Boukadoum1, Abdoulaye Banire Diallo2

  • 1Computer Science, Université du Québec à Montréal, Montreal, Canada.

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Summary
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This study introduces MultiPatchFormer, a novel Transformer model for time-series forecasting. It improves accuracy by analyzing data at multiple scales and considering inter-series correlations, outperforming existing methods.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Time-Series Analysis

Background:

  • Transformer models show promise for time-series forecasting.
  • Existing methods often use a single scale, limiting granularity and inter-series correlation capture.
  • This can lead to inaccurate forecasts.

Purpose of the Study:

  • To propose a Transformer-based model addressing limitations of single-scale and ignored inter-series correlations.
  • To enhance time-series forecasting accuracy and generalizability.

Main Methods:

  • Developed MultiPatchFormer, integrating multi-scale patch-wise temporal modeling and channel-wise representation.
  • Input time-series divided into patches of varying resolutions for multi-scale temporal correlation.
  • Channel-wise encoder captures intricate interactions among input series.
  • Multi-step linear decoder reduces overfitting and noise.

Main Results:

  • MultiPatchFormer achieved state-of-the-art results on seven real-world datasets.
  • Outperformed current baseline models in error metrics.
  • Demonstrated stronger generalizability.

Conclusions:

  • The proposed MultiPatchFormer effectively captures multi-scale temporal patterns and inter-series correlations.
  • The model offers improved accuracy and generalizability for time-series forecasting.
  • Addresses key limitations in existing Transformer-based forecasting approaches.