Academic

Dynamic Multi-period Experts for Online Time Series Forecasting

arXiv:2603.09062v1 Announce Type: new Abstract: Online Time Series Forecasting (OTSF) requires models to continuously adapt to concept drift. However, existing methods often treat concept drift as a monolithic phenomenon. To address this limitation, we first redefine concept drift by categorizing it into two distinct types: Recurring Drift, where previously seen patterns reappear, and Emergent Drift, where entirely new patterns emerge. We then propose DynaME (Dynamic Multi-period Experts), a novel hybrid framework designed to effectively address this dual nature of drift. For Recurring Drift, DynaME employs a committee of specialized experts that are dynamically fitted to the most relevant historical periodic patterns at each time step. For Emergent Drift, the framework detects high-uncertainty scenarios and shifts reliance to a stable, general expert. Extensive experiments on several benchmark datasets and backbones demonstrate that DynaME effectively adapts to both concept drifts an

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Seungha Hong, Sukang Chae, Suyeon Kim, Sanghwan Jang, Hwanjo Yu · · 1 min read · 9 views
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arXiv:2603.09062v1 Announce Type: new Abstract: Online Time Series Forecasting (OTSF) requires models to continuously adapt to concept drift. However, existing methods often treat concept drift as a monolithic phenomenon. To address this limitation, we first redefine concept drift by categorizing it into two distinct types: Recurring Drift, where previously seen patterns reappear, and Emergent Drift, where entirely new patterns emerge. We then propose DynaME (Dynamic Multi-period Experts), a novel hybrid framework designed to effectively address this dual nature of drift. For Recurring Drift, DynaME employs a committee of specialized experts that are dynamically fitted to the most relevant historical periodic patterns at each time step. For Emergent Drift, the framework detects high-uncertainty scenarios and shifts reliance to a stable, general expert. Extensive experiments on several benchmark datasets and backbones demonstrate that DynaME effectively adapts to both concept drifts and significantly outperforms existing baselines.

Executive Summary

This study proposes a novel hybrid framework, DynaME, designed to address the dual nature of concept drift in Online Time Series Forecasting (OTSF). The framework categorizes concept drift into two distinct types: Recurring Drift and Emergent Drift. DynaME employs a committee of specialized experts for Recurring Drift and a stable, general expert for Emergent Drift. Experimental results demonstrate that DynaME effectively adapts to both concept drifts and outperforms existing baselines. This study contributes to the development of more robust OTSF models by addressing the limitations of existing methods. The proposed framework has potential applications in various domains, including finance and healthcare, where timely and accurate forecasting is crucial.

Key Points

  • ▸ DynaME categorizes concept drift into two distinct types: Recurring Drift and Emergent Drift
  • ▸ The framework employs a committee of specialized experts for Recurring Drift and a stable, general expert for Emergent Drift
  • ▸ Experimental results demonstrate that DynaME effectively adapts to both concept drifts and outperforms existing baselines

Merits

Strength in Addressing Concept Drift

DynaME's ability to differentiate between Recurring Drift and Emergent Drift allows for a more nuanced approach to addressing concept drift, which is a significant limitation of existing methods.

Improved Performance

The proposed framework demonstrates improved performance over existing baselines, making it a promising solution for OTSF applications.

Demerits

Limited Evaluation Datasets

The study relies on a limited set of benchmark datasets, which may not fully capture the complexity and variability of real-world OTSF applications.

Lack of Theoretical Analysis

The study focuses primarily on experimental results, with limited theoretical analysis of the proposed framework, which may limit its broader applicability.

Expert Commentary

The study proposes a novel framework for addressing concept drift in OTSF, which has the potential to improve the accuracy and reliability of OTSF models. However, the limited evaluation datasets and lack of theoretical analysis may limit the framework's broader applicability. Nevertheless, the study's findings contribute to the development of more robust OTSF models and have potential implications for policy decisions related to data-driven decision-making. Furthermore, the study raises important questions about explainability in AI, particularly in terms of understanding the decision-making process of complex models.

Recommendations

  • ✓ Future studies should focus on expanding the evaluation datasets to better capture the complexity and variability of real-world OTSF applications.
  • ✓ Theoretical analysis of the proposed framework should be conducted to better understand its underlying mechanisms and potential limitations.

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