Into the ORBIT for Time Series: Training Regimes for Foundation Models

Time series foundation models (TSFMs) have advanced primarily through architectural innovation, while training regimes for large-scale heterogeneous corpora remain under-explored. As a result, pre-training distributions are often poorly controlled with respect to domain imbalance, context requirements, prediction horizons, and missingness. We introduce ORBIT (Omni-Range Bootstrap Incremental Training), a training paradigm that makes this distribution explicit and controllable. ORBIT combines Boo

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ethics.ai (13 August 2026), “Into the ORBIT for Time Series: Training Regimes for Foundation Models,” evidence record 19173, https://ethics.ai/record/19173 (originally published by arXiv).

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