{"ID":2823326,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.00970","arxiv_id":"2601.00970","title":"Zero-shot Forecasting by Simulation Alone","abstract":"Zero-shot time-series forecasting holds great promise, but is still in its infancy, hindered by limited and biased data corpora, leakage-prone evaluation, and privacy and licensing constraints. Motivated by these challenges, we propose the first practical univariate time series simulation pipeline which is simultaneously fast enough for on-the-fly data generation and enables notable zero-shot forecasting performance on M-Series and GiftEval benchmarks that capture trend/seasonality/intermittency patterns, typical of industrial forecasting applications across a variety of domains. Our simulator, which we call SarSim0 (SARIMA Simulator for Zero-Shot Forecasting), is based off of a seasonal autoregressive integrated moving average (SARIMA) model as its core data source. Due to instability in the autoregressive component, naive SARIMA simulation often leads to unusable paths. Instead, we follow a three-step procedure: (1) we sample well-behaved trajectories from its characteristic polynomial stability region; (2) we introduce a superposition scheme that combines multiple paths into rich multi-seasonality traces; and (3) we add rate-based heavy-tailed noise models to capture burstiness and intermittency alongside seasonalities and trends. SarSim0 is orders of magnitude faster than kernel-based generators, and it enables training on circa 1B unique purely simulated series, generated on the fly; after which well-established neural network backbones exhibit strong zero-shot generalization, surpassing strong statistical forecasters and recent foundation baselines, while operating under strict zero-shot protocol. Notably, on GiftEval we observe a \"student-beats-teacher\" effect: models trained on our simulations exceed the forecasting accuracy of the AutoARIMA generating processes.","short_abstract":"Zero-shot time-series forecasting holds great promise, but is still in its infancy, hindered by limited and biased data corpora, leakage-prone evaluation, and privacy and licensing constraints. Motivated by these challenges, we propose the first practical univariate time series simulation pipeline which is simultaneous...","url_abs":"https://arxiv.org/abs/2601.00970","url_pdf":"https://arxiv.org/pdf/2601.00970v1","authors":"[\"Boris N. Oreshkin\",\"Mayank Jauhari\",\"Ravi Kiran Selvam\",\"Malcolm Wolff\",\"Wenhao Pan\",\"Shankar Ramasubramanian\",\"Kin G. Olivares\",\"Tatiana Konstantinova\",\"Andres Potapczynski\",\"Mengfei Cao\",\"Dmitry Efimov\",\"Michael W. Mahoney\",\"Andrew G. Wilson\"]","published":"2026-01-02T19:41:42Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}