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

Sunho Kim, Susik Yoon

TL;DR

Back to the Future (BTTF) presents a lightweight, augmentation-driven framework for long-term time series forecasting that blends direct multi-step prediction with autoregressive temporal coherence. By augmenting the input with segments of the initial forecast and training multiple parallel second-stage refiners, BTTF achieves robust improvements for simple linear baselines without requiring heavy architectures. A principled top‑K ensemble selects an optimal mix of refiners using variance and inter-model correlation statistics, leading to substantial gains (up to 58% in some cases) and practical diagnostic value when a base model is already well optimized. The approach suggests that model-generated forecasts can serve as informative future context, offering a simple yet powerful avenue to enhance LTSF in real-world settings.

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.

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

TL;DR

Back to the Future (BTTF) presents a lightweight, augmentation-driven framework for long-term time series forecasting that blends direct multi-step prediction with autoregressive temporal coherence. By augmenting the input with segments of the initial forecast and training multiple parallel second-stage refiners, BTTF achieves robust improvements for simple linear baselines without requiring heavy architectures. A principled top‑K ensemble selects an optimal mix of refiners using variance and inter-model correlation statistics, leading to substantial gains (up to 58% in some cases) and practical diagnostic value when a base model is already well optimized. The approach suggests that model-generated forecasts can serve as informative future context, offering a simple yet powerful avenue to enhance LTSF in real-world settings.

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.
Paper Structure (13 sections, 11 equations, 2 figures, 1 table, 1 algorithm)

This paper contains 13 sections, 11 equations, 2 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Setting
  4. Methodology
  5. Overview
  6. Look-ahead Augmentation
  7. Parallel Self-Refinement
  8. Ensembled Forecasting
  9. Experiments
  10. Experiment Settings
  11. Overall Performance
  12. Computational Efficiency
  13. Conclusion and Future Work

Figures (2)

  • Figure 1: BTTF effectively leverages the strengths of both DMS and IMS for long-term time series forecasting.
  • Figure 2: Overall procedure of BTTF: (i) Look-ahead Augmentation, where the initial predictions are split into segments and appended to inputs to form augmented sequences; (ii) Parallel Self-Refinement, where $N$ second prediction models are trained on these augmented inputs to refine first predictions; and (iii) Ensembled Forecasting, which ranks models by validation performance, aggregates them via a step-wise top $K$ ensemble, and selects the optimal $K^*$ to generate the final output sequence.