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arXiv AI By Kwangryeol Park, Seulki Lee

OliO: ODE-based Linear Transition Operator for Self-Supervised Time Series Forecasting

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OliO is a self‑supervised learning method for time‑series forecasting that models bidirectional temporal flows using a linear ODE‑based transition operator. By enforcing a strictly upper triangular constraint on the transition matrix, it ensures numerical stability and captures long‑term dependencies without exponential divergence. Experiments on multiple backbones and benchmarks show up to 5.75% MSE improvement over state‑of‑the‑art baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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