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PeriodPatch: A frequency-aware modular framework with patch-based embedding and periodic bias for multivariate time

Hongxing Peng1, Shuxia Jiang1, Jianji Ren1

  • 1School of Software, Henan Polytechnic University, Jiaozuo, China.

Neural Networks : the Official Journal of the International Neural Network Society
|April 22, 2026
PubMed
Summary
This summary is machine-generated.

PeriodPatch enhances multivariate time series forecasting by integrating frequency and structure awareness into patch-based methods. This novel approach improves accuracy and resilience in both short-term and long-term predictions.

Keywords:
Attention mechanismFrequency-aware modelingMultivariate time series forecastingPatch tokenizationPeriodic embedding

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Science

Background:

  • Multivariate time series forecasting faces challenges from complex temporal dynamics, inter-variable relationships, and periodic patterns.
  • Existing patch-based methods often struggle to capture frequency-domain information and structural variations.
  • There is a need for advanced models that can effectively handle these complexities for improved forecasting accuracy.

Purpose of the Study:

  • To introduce PeriodPatch, a novel period-aware patch-based forecasting method.
  • To enhance the capture of frequency-domain patterns and structural variety in time series data.
  • To improve the accuracy and robustness of multivariate time series forecasting.

Main Methods:

  • PeriodPatch employs intra-patch convolution and a decomposition module for trend and residual extraction.
  • A Reference Signal Generator (RSGenerator) utilizes Fourier transform to extract prominent frequency components.
  • Controlled Periodic Embedding and cross-attention mechanisms integrate frequency-aligned positional information and focus on periodic patterns.

Main Results:

  • PeriodPatch demonstrates superior accuracy and resilience across 11 benchmark datasets and four prediction periods.
  • Significant reductions in Mean Squared Error (MSE) and Mean Absolute Error (MAE) were observed for both long-term (13.1% MSE, 11.7% MAE) and short-term (25% MSE, 20% MAE) predictions compared to PatchTST.
  • PeriodPatch outperformed other leading methods including DLinear, Informer, Autoformer, FEDformer, and iTransformer.

Conclusions:

  • PeriodPatch offers a robust, interpretable, and generalizable solution for multivariate time series forecasting.
  • The method effectively identifies and leverages periodic structures and inter-variable dependencies.
  • PeriodPatch represents a significant advancement in handling complex temporal dynamics for forecasting applications.