One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data

2026-06-09

AI Summary

Existing Transformer-based time series forecasting models often suffer from systematic residual biases due to architectural limitations, insufficient modeling of stochasticity, or inadequate multi-scale representations. To address this, this work proposes a two-stage, model-agnostic correction framework: an initial prediction is first generated by a base Transformer, followed by a multi-scale residual-aware meta-corrector that dynamically models structured errors across multivariate channels, enabling end-to-end adaptive refinement. By decoupling forecasting and residual learning into distinct representation stages, the approach formally expands the hypothesis space and overcomes the approximation limitations inherent in single-stage architectures. Evaluated on eight mainstream benchmarks, the method significantly outperforms existing approaches, achieving substantial reductions in both MSE and MAE, effectively mitigating systematic bias and enhancing robustness to complex temporal dynamics.

Abstract

Transformer-based models have emerged as leading paradigms in time-series forecasting in recent years, employing self-attention mechanisms to capture long-range dependencies. Despite their success, these single-stage forecasting architectures exhibit persistent systematic residual biases arising from structural discrepancies, unmodeled stochastic components, or inadequate multi-scale temporal representations. This limitation persists when residuals are treated as irreducible noise, precluding adaptive correction of structured error patterns. To address this limitation, we introduce a two-stage, model-agnostic framework that explicitly decouples forecasting and residual learning into distinct stages of representation learning. A base transformer first generates the initial predictions. Subsequently, a dedicated meta-corrector dynamically models structured error patterns across multivariate channels, preserves cross-variable dependencies, and iteratively refines the residual bias of the base transformer. By formalizing this pipeline as a hypothesis space expansion, our framework addresses approximation limitations inherent in single-stage architectures, removes reliance on restrictive assumptions, and enables end-to-end learning of complex error dynamics. Evaluated on eight popular benchmark datasets using established protocols, our approach achieves state-of-the-art performance, with significant improvements in standard metrics (MSE, MAE). The results demonstrate the framework's ability to mitigate systematic biases and enhance robustness to complex temporal dynamics, advancing the practical applicability of transformer-based forecasting models.

Problem

• time series forecasting
• residual bias
• multi-scale representation
• systematic error
• transformer models

Innovation

• residual-aware learning
• two-stage forecasting
• multi-scale representation
• transformer-based time series
• model-agnostic correction