Title: RAP:Reconstruction-Augmented Predictors for Robust Time-series Forecasting
Primary area: learning on time series and dynamical systems
Keywords: Long-term time-series forecasting (LTSF); robustness

Abstract: Long-term time-series forecasting (LTSF) underpins applications in energy, traffic, and healthcare, yet state-of-the-art models remain brittle when inputs are corrupted by sensor or preprocessing noise. To resolve this problem, we introduce a reconstruction-augmented predictor (RAP) that improves robustness without data augmentation by coupling the standard forecasting loss with a self-reconstruction objective on the history window. A single encoder feeds two linear heads—one for reconstruction, one for prediction—so that the representation is explicitly regularized to preserve fine-grained, multi-scale structure rather than overfitting noise-specific artifacts. We theoretically analyze RAP in a small-noise regime and show that it provably reduces a first-order measure of noise sensitivity (the Jacobian energy of the predictor) under mild spectral constraints. Also, to instantiate RAP effectively, we adopt a dual-axis Transformer that alternates attention along temporal and channel dimensions and generates the full target sequence conditioned on the history. Across representative LTSF benchmarks in high-dimensional datasets, RAP consistently outperforms competitive baselines in clean conditions and sustains its advantage as synthetic or real noise levels increase, with the largest gains on the most challenging settings. Ablations quantify the effect of the reconstruction weight and the reconstruction head, and analysis highlights failure modes of augmentation- and filtering-based robustness strategies.

TL;DR: We propose RAP, a reconstruction-augmented predictor for LTSF that regularizes representations with a self-reconstruction objective.