PPGF: Probability Pattern-Guided Time Series Forecasting

2025-02-18

AI Summary

To address the challenges of uneven fitting capability for heterogeneous patterns and high error volatility in hybrid-mode time-series forecasting (TSF), this paper proposes a probabilistic pattern-guided end-to-end forecasting framework. Methodologically, we reformulate TSF as a pattern classification-driven interval regression task; design a True-Class Probability (TCP)-weighted mechanism to emphasize hard-to-classify samples and enhance classification robustness; and introduce a strong classification–prediction consistency constraint to ensure synergistic optimization between pattern discrimination and interval estimation. Pattern grouping mitigates class imbalance, enabling joint end-to-end training. Extensive experiments on multiple real-world datasets demonstrate significant improvements over state-of-the-art baselines. Ablation studies validate that TCP weighting substantially boosts classification accuracy, while the classification–prediction consistency constraint proves critically necessary for overall forecasting performance.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Reasoning under Uncertainty: Probabilistic Programming
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Time series forecasting (TSF) is an essential branch of machine learning with various applications. Most methods for TSF focus on constructing different networks to extract better information and improve performance. However, practical application data contain different internal mechanisms, resulting in a mixture of multiple patterns. That is, the model's ability to fit different patterns is different and generates different errors. In order to solve this problem, we propose an end-to-end framework, namely probability pattern-guided time series forecasting (PPGF). PPGF reformulates the TSF problem as a forecasting task guided by probabilistic pattern classification. Firstly, we propose the grouping strategy to approach forecasting problems as classification and alleviate the impact of data imbalance on classification. Secondly, we predict in the corresponding class interval to guarantee the consistency of classification and forecasting. In addition, True Class Probability (TCP) is introduced to pay more attention to the difficult samples to improve the classification accuracy. Detailedly, PPGF classifies the different patterns to determine which one the target value may belong to and estimates it accurately in the corresponding interval. To demonstrate the effectiveness of the proposed framework, we conduct extensive experiments on real-world datasets, and PPGF achieves significant performance improvements over several baseline methods. Furthermore, the effectiveness of TCP and the necessity of consistency between classification and forecasting are proved in the experiments. All data and codes are available online: https://github.com/syrGitHub/PPGF.

Problem

• Improve time series forecasting accuracy
• Handle mixed internal data patterns
• Ensure classification and forecasting consistency

Innovation

• Probabilistic pattern classification guided forecasting
• Grouping strategy for data imbalance
• True Class Probability for accuracy