PPGF: Probability Pattern-Guided Time Series Forecasting.

Area of Science:

• Machine Learning
• Data Science
• Artificial Intelligence

Background:

• Time series forecasting (TSF) is crucial but challenged by complex data with multiple internal patterns.
• Existing TSF methods often struggle to adapt to diverse data patterns, leading to varied error generation.

Purpose of the Study:

• To propose an end-to-end framework, Probability Pattern-Guided Time Series Forecasting (PPGF), to address limitations in TSF.
• To reformulate TSF as a probabilistic pattern classification and forecasting task for improved accuracy.

Main Methods:

• PPGF employs a grouping strategy to treat forecasting as a classification problem, mitigating data imbalance effects.
• The framework predicts class intervals to ensure consistency between classification and forecasting.
• True Class Probability (TCP) is incorporated to enhance classification accuracy by focusing on difficult samples.

Main Results:

• PPGF demonstrated significant performance improvements over baseline methods in extensive experiments on real-world datasets.
• The effectiveness of TCP and the importance of classification-forecasting consistency were empirically validated.
• The proposed PPGF framework offers a novel approach to handling complex time series data.

Conclusions:

• PPGF effectively classifies diverse data patterns and forecasts accurately within corresponding intervals.
• The framework enhances TSF performance by integrating probabilistic pattern classification.
• The study highlights the benefits of consistency between classification and forecasting for robust time series analysis.