Dynamics is what you need for time-series forecasting!

2025-07-21

AI Summary

Existing time-series forecasting models suffer from limited performance due to inadequate modeling of data dynamics. Method: This paper proposes PRO-DYN, a framework that integrates a learnable dynamic module as a standalone, structurally intact component at the model’s output stage—rather than dispersing it across the architecture. Grounded in dynamical systems modeling principles, we conduct systematic ablation studies across diverse backbone networks to validate two key design principles: (i) optimal placement of the dynamic module at the final layer, and (ii) its structural integrity—i.e., inseparability into subcomponents. Contribution/Results: Empirical evaluation on multiple benchmark datasets demonstrates consistent and significant improvements in forecasting accuracy. Moreover, the gains generalize across architectures, establishing PRO-DYN as an interpretable, reusable paradigm for dynamic enhancement in time-series modeling.

Technology Category

Machine Learning: Time-Series/Data StreamsComputer Vision: Diffusion Models for VisionReasoning under Uncertainty: Relational Probabilistic Models

Application Category

Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingUser Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

While boundaries between data modalities are vanishing, the usual successful deep models are still challenged by simple ones in the time-series forecasting task. Our hypothesis is that this task needs models that are able to learn the data underlying dynamics. We propose to validate it through both systemic and empirical studies. We develop an original $ exttt{PRO-DYN}$ nomenclature to analyze existing models through the lens of dynamics. Two observations thus emerged: $ extbf{1}$. under-performing architectures learn dynamics at most partially, $ extbf{2}$. the location of the dynamics block at the model end is of prime importance. We conduct extensive experiments to confirm our observations on a set of performance-varying models with diverse backbones. Results support the need to incorporate a learnable dynamics block and its use as the final predictor.

Problem

• Time-series forecasting requires learning data dynamics effectively
• Current deep models underperform due to partial dynamics learning
• Optimal dynamics block placement is crucial for forecasting accuracy

Innovation

• Proposes PRO-DYN nomenclature for dynamics analysis
• Identifies dynamics block location as crucial
• Incorporates learnable dynamics block as final predictor