Back to the Future: Look-ahead Augmentation and Parallel Self-Refinement for Time Series Forecasting

• URL: http://arxiv.org/abs/2602.02146v1
• Date: Mon, 02 Feb 2026 14:23:31 GMT
• Title: Back to the Future: Look-ahead Augmentation and Parallel Self-Refinement for Time Series Forecasting
• Authors: Sunho Kim, Susik Yoon,
• Abstract summary: Back to the Future is a simple yet effective framework that enhances forecasting stability through look-ahead augmentation and self-corrective refinement.<n>Despite its simplicity, our approach consistently improves long-horizon accuracy and mitigates the instability of linear forecasting models.<n>These results suggest that leveraging model-generated forecasts as augmentation can be a simple yet powerful way to enhance long-term prediction, even without complex architectures.
• Score: 10.615433089293228
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-term time series forecasting (LTSF) remains challenging due to the trade-off between parallel efficiency and sequential modeling of temporal coherence. Direct multi-step forecasting (DMS) methods enable fast, parallel prediction of all future horizons but often lose temporal consistency across steps, while iterative multi-step forecasting (IMS) preserves temporal dependencies at the cost of error accumulation and slow inference. To bridge this gap, we propose Back to the Future (BTTF), a simple yet effective framework that enhances forecasting stability through look-ahead augmentation and self-corrective refinement. Rather than relying on complex model architectures, BTTF revisits the fundamental forecasting process and refines a base model by ensembling the second-stage models augmented with their initial predictions. Despite its simplicity, our approach consistently improves long-horizon accuracy and mitigates the instability of linear forecasting models, achieving accuracy gains of up to 58% and demonstrating stable improvements even when the first-stage model is trained under suboptimal conditions. These results suggest that leveraging model-generated forecasts as augmentation can be a simple yet powerful way to enhance long-term prediction, even without complex architectures.

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