Scale-Invariant Conditional VAE for Coarse-Grained Economic Time-Series Forecasting

摘要

Coarse-grained time series (CGTS) are critical for business and macroeconomic analysis. However, CGTS are typically updated infrequently and contain few observations, so model-centric training on raw data is prone to overfitting and degraded forecast accuracy. To address this, we propose SI-CVAE, a scale-invariant conditional generative model built around variable-length subsequences, and adopt a data-centric "train on synthetic, test on real" paradigm to enhance downstream forecasting. Specifically, we first introduce a variable-length subsequence clustering-and-matching algorithm to capture cross-scale recurring patterns within a series. We then design a frequency-domain conditional VAE with a frequency-domain linear decoder to enable controllable, arbitrary-length sequence synthesis while enforcing scale-invariance constraints. Finally, we develop a time-domain reconstruction strategy with subsequence-conditioned fusion to ensure temporal continuity and spectral consistency across concatenated segments. We conduct extensive experiments on six ship-sales datasets and four U.S. macroeconomic indicators. Results show that replacing or augmenting the original training set with SI-CVAE–generated data yields consistent accuracy gains across multiple forecasting baselines, and that SI-CVAE attains higher synthetic-data quality than state-of-the-art generators on standard metrics.