{"ID":2891065,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.21155","arxiv_id":"2507.21155","title":"SPADE-S: A Sparsity-Robust Foundational Forecaster","abstract":"Despite significant advancements in time series forecasting, accurate modeling of time series with strong heterogeneity in magnitude and/or sparsity patterns remains challenging for state-of-the-art deep learning architectures. We identify several factors that lead existing models to systematically underperform on low-magnitude and sparse time series, including loss functions with implicit biases toward high-magnitude series, training-time sampling methods, and limitations of time series encoding methods. SPADE-S is a robust forecasting architecture that significantly reduces magnitude- and sparsity-based systematic biases and improves overall prediction accuracy. Empirical results demonstrate that SPADE-S outperforms existing state-of-the-art approaches across a diverse set of use cases in demand forecasting. In particular, we show that, depending on the quantile forecast and magnitude of the series, SPADE-S can improve forecast accuracy by up to 15%. This results in P90 overall forecast accuracy gains of 2.21%, 6.58%, and 4.28%, and P50 forecast accuracy gains of 0.92%, 0.77%, and 1.95%, respectively, for each of three distinct datasets, ranging from 3 million to 700 million series, from a large online retailer.","short_abstract":"Despite significant advancements in time series forecasting, accurate modeling of time series with strong heterogeneity in magnitude and/or sparsity patterns remains challenging for state-of-the-art deep learning architectures. We identify several factors that lead existing models to systematically underperform on low-...","url_abs":"https://arxiv.org/abs/2507.21155","url_pdf":"https://arxiv.org/pdf/2507.21155v2","authors":"[\"Malcolm Wolff\",\"Matthew Li\",\"Ravi Kiran Selvam\",\"Hanjing Zhu\",\"Kin G. Olivares\",\"Ruijun Ma\",\"Abhinav Katoch\",\"Shankar Ramasubramanian\",\"Mengfei Cao\",\"Roberto Bandarra\",\"Rahul Gopalsamy\",\"Stefania La Vattiata\",\"Sitan Yang\",\"Michael W. Mahoney\"]","published":"2025-07-24T19:30:24Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"stat.ML\"]","methods":"[]","has_code":false}