Forecasting Multivariate Time Series under Predictive Heterogeneity: A Validation-Driven Clustering Framework

2026-04-15

AI Summary

This work addresses the challenge of prediction heterogeneity in high-dimensional multivariate time series forecasting, where global models often underperform and naive specialization risks negative transfer. The authors formulate adaptive pooling as a statistical decision problem and propose a validation-driven clustering framework that dynamically determines when and how to specialize sequences based on out-of-sample predictive performance rather than representational similarity. Clusters are iteratively refined using validation errors derived from Huber and pinball losses, while a leakage-free fallback strategy and a rigorous train-validation-test protocol ensure robustness. Evaluated on large-scale traffic datasets, the method significantly outperforms strong baselines and maintains stable performance even under weak heterogeneity.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

We study adaptive pooling under predictive heterogeneity in high-dimensional multivariate time series forecasting, where global models improve statistical efficiency but may fail to capture heterogeneous predictive structure, while naive specialization can induce negative transfer. We formulate adaptive pooling as a statistical decision problem and propose a validation-driven framework that determines when and how specialization should be applied. Rather than grouping series based on representation similarity, we define partitions through out-of-sample predictive performance, thereby aligning data organization with predictive risk, defined as expected out-of-sample loss and approximated via validation error. Cluster assignments are iteratively updated using validation losses for both point (Huber) and probabilistic (pinball) forecasting, improving robustness to heavy-tailed errors and local anomalies. To ensure reliability, we introduce a leakage-free fallback mechanism that reverts to a global model whenever specialization fails to improve validation performance, providing a safeguard against performance degradation under a strict training-validation-test protocol. Experiments on large-scale traffic datasets demonstrate consistent improvements over strong baselines while avoiding degradation when heterogeneity is weak. Overall, the proposed framework provides a principled and practically reliable approach to adaptive pooling in high-dimensional forecasting problems.

Problem

• multivariate time series forecasting
• predictive heterogeneity
• adaptive pooling
• negative transfer
• validation-driven clustering

Innovation

• adaptive pooling
• predictive heterogeneity
• validation-driven clustering
• multivariate time series forecasting
• fallback mechanism