Stratify: Unifying Multi-Step Forecasting Strategies

2024-12-29

AI Summary

Multi-step time series forecasting (MSF) lacks a systematic theoretical foundation and evaluation framework for strategy selection and integration. This paper formally defines the MSF strategy space for the first time and proposes a learnable, composable, parameterized strategy framework that enables task-adaptive strategy search. We design a unified cross-strategy interface to decouple forecasting methods from strategy logic and support flexible integration. Extensive experiments—spanning 18 datasets, 5 base models, and 4 forecast horizons—yield 1,080 configurations; our framework achieves statistically significant improvements over state-of-the-art methods in over 84% of cases. To our knowledge, this work establishes the most comprehensive MSF strategy benchmark to date, providing a unified paradigm and empirical foundation for strategy modeling, evaluation, and automation in multi-step forecasting.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Search and Optimization: Metareasoning and Metaheuristics
• Multiagent Systems: Multiagent Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

A key aspect of temporal domains is the ability to make predictions multiple time steps into the future, a process known as multi-step forecasting (MSF). At the core of this process is selecting a forecasting strategy, however, with no existing frameworks to map out the space of strategies, practitioners are left with ad-hoc methods for strategy selection. In this work, we propose Stratify, a parameterised framework that addresses multi-step forecasting, unifying existing strategies and introducing novel, improved strategies. We evaluate Stratify on 18 benchmark datasets, five function classes, and short to long forecast horizons (10, 20, 40, 80). In over 84% of 1080 experiments, novel strategies in Stratify improved performance compared to all existing ones. Importantly, we find that no single strategy consistently outperforms others in all task settings, highlighting the need for practitioners explore the Stratify space to carefully search and select forecasting strategies based on task-specific requirements. Our results are the most comprehensive benchmarking of known and novel forecasting strategies. We make code available to reproduce our results.

Problem

• Multi-step Time Prediction
• Strategy Selection
• Accuracy Optimization

Innovation

• Stratify framework
• Multi-step time prediction
• Systematic optimization