Academic

Harmonic Dataset Distillation for Time Series Forecasting

arXiv:2603.03760v1 Announce Type: new Abstract: Time Series forecasting (TSF) in the modern era faces significant computational and storage cost challenges due to the massive scale of real-world data. Dataset Distillation (DD), a paradigm that synthesizes a small, compact dataset to achieve training performance comparable to that of the original dataset, has emerged as a promising solution. However, conventional DD methods are not tailored for time series and suffer from architectural overfitting and limited scalability. To address these issues, we propose Harmonic Dataset Distillation for Time Series Forecasting (HDT). HDT decomposes the time series into its sinusoidal basis through the FFT and aligns the core periodic structure by Harmonic Matching. Since this process operates in the frequency domain, all updates during distillation are applied globally without disrupting temporal dependencies of time series. Extensive experiments demonstrate that HDT achieves strong cross-architect

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Seungha Hong, Sanghwan Jang, Wonbin Kweon, Suyeon Kim, Gyuseok Lee, Hwanjo Yu · · 1 min read · 11 views
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arXiv:2603.03760v1 Announce Type: new Abstract: Time Series forecasting (TSF) in the modern era faces significant computational and storage cost challenges due to the massive scale of real-world data. Dataset Distillation (DD), a paradigm that synthesizes a small, compact dataset to achieve training performance comparable to that of the original dataset, has emerged as a promising solution. However, conventional DD methods are not tailored for time series and suffer from architectural overfitting and limited scalability. To address these issues, we propose Harmonic Dataset Distillation for Time Series Forecasting (HDT). HDT decomposes the time series into its sinusoidal basis through the FFT and aligns the core periodic structure by Harmonic Matching. Since this process operates in the frequency domain, all updates during distillation are applied globally without disrupting temporal dependencies of time series. Extensive experiments demonstrate that HDT achieves strong cross-architecture generalization and scalability, validating its practicality for large-scale, real-world applications.

Executive Summary

The article proposes Harmonic Dataset Distillation for Time Series Forecasting (HDT), a novel approach to address the challenges of time series forecasting. HDT leverages the Fourier transform to decompose time series into its sinusoidal basis and aligns the core periodic structure through Harmonic Matching. This process enables global updates during distillation without disrupting temporal dependencies, resulting in strong cross-architecture generalization and scalability. The method has been validated through extensive experiments, demonstrating its practicality for large-scale, real-world applications.

Key Points

  • ▸ Harmonic Dataset Distillation for Time Series Forecasting (HDT) is proposed as a solution to address computational and storage cost challenges
  • ▸ HDT utilizes the Fourier transform to decompose time series into its sinusoidal basis
  • ▸ Harmonic Matching is used to align the core periodic structure, enabling global updates during distillation

Merits

Efficient Dataset Distillation

HDT achieves strong cross-architecture generalization and scalability, making it a promising solution for large-scale time series forecasting applications

Demerits

Limited Theoretical Analysis

The article primarily focuses on empirical evaluations, with limited theoretical analysis of the HDT method, which may raise questions about its robustness and generalizability

Expert Commentary

The proposed HDT method demonstrates significant potential for improving the efficiency and accuracy of time series forecasting models. By leveraging the Fourier transform and Harmonic Matching, HDT can effectively capture the underlying periodic structure of time series data, enabling more accurate predictions. However, further research is needed to theoretically analyze the method's robustness and generalizability, as well as to explore its applications in various domains.

Recommendations

  • ✓ Further theoretical analysis of the HDT method to establish its robustness and generalizability
  • ✓ Exploration of HDT's applications in various time series forecasting domains, such as finance and weather forecasting

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