FDF: Flexible Decoupled Framework for Time Series Forecasting with Conditional Denoising and Polynomial Modeling

2024-10-17

arXiv.org

AI Summary

Existing time-series forecasting models typically couple trend and seasonal components in a single modeling framework, limiting their ability to accurately capture dynamic temporal characteristics; moreover, conventional diffusion models apply noise indiscriminately, risking irreversible loss of critical sequential information. To address these limitations, we propose FDF—a decoupled forecasting framework featuring a novel Conditioned Denoising Seasonal Module (CDSM) and a Polynomial Trend Module (PTM). CDSM enables statistically informed seasonal denoising, while PTM provides smooth, interpretable trend estimation via polynomial regression. FDF synergistically integrates diffusion-based generation, conditional modeling, polynomial regression, and classical decomposition principles to achieve principled component-wise decoupling. Extensive experiments across multiple benchmark datasets demonstrate that FDF consistently outperforms state-of-the-art methods, achieving superior accuracy, strong generalization, and robust adaptability to diverse seasonal periods.

Technology Category

Machine Learning: Time-Series/Data StreamsComputer Vision: Diffusion Models for VisionPlanning, Routing, and Scheduling: Planning with Language Models

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingGraph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphsUser Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Time series forecasting is vital in numerous web applications, influencing critical decision-making across industries. While diffusion models have recently gained increasing popularity for this task, we argue they suffer from a significant drawback: indiscriminate noise addition to the original time series followed by denoising, which can obscure underlying dynamic evolving trend and complicate forecasting. To address this limitation, we propose a novel flexible decoupled framework (FDF) that learns high-quality time series representations for enhanced forecasting performance. A key characteristic of our approach leverages the inherent inductive bias of time series data of its decomposed trend and seasonal components, each modeled separately to enable decoupled analysis and modeling. Specifically, we propose an innovative Conditional Denoising Seasonal Module (CDSM) within the diffusion model, which leverages statistical information from the historical window to conditionally model the complex seasonal component. Notably, we incorporate a Polynomial Trend Module (PTM) to effectively capture the smooth trend component, thereby enhancing the model's ability to represent temporal dependencies. Extensive experiments validate the effectiveness of our framework, demonstrating superior performance over existing methods and highlighting its flexibility in time series forecasting. The source code is available at https://github.com/zjt-gpu/FDF.

Problem

• Decomposes time series into trend and seasonal components
• Utilizes probabilistic diffusion for fluctuating patterns
• Enhances linear models for smooth trend preservation

Innovation

• Decomposes time series into components
• Uses probabilistic diffusion for seasonality
• Enhances linear models for trends