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arXiv · 2608.13262

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

Hongjie Xia·Yiding Liu·Yifan Hu·Peiyuan Liu·Zewei Dong
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Abstract

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 Bootstrap Multi-Level Sampling, which controls dataset exposure and samples records, target variables, context windows, and prediction horizons, with Omni-Range Incremental Training, which varies context lengths and prediction horizons throughout a single training stage. Under ORBIT, we train Falcon-2.0, a simple univariate encoder-only Transformer with missingness-aware triple-channel patch tokenization and parallel patch prediction. We further introduce Rank-Guided Cross-Depth Alignment, a training objective that uses late-layer representations as stop-gradient teachers for shallow layers without additional inference cost. Evaluations on GIFT-Eval and fev-bench demonstrate strong zero-shot forecasting performance across diverse domains and frequencies.

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Hongjie Xia, Yiding Liu, Yifan Hu, Peiyuan Liu, Zewei Dong. 2026-08-13. Into the ORBIT for Time Series: Training Regimes for Foundation Models. https://arxiv.org/abs/2608.13262

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