OliO: ODE-based Linear Transition Operator for Self-Supervised Time Series Forecasting
In brief
- OliO uses linear ODEs to model time series temporal flow.
- An upper triangular constraint ensures stability and long-term dependency capture.
- 5.75% MSE reduction compared to current baselines.
A new self-supervised method, OliO, leverages ordinary differential equations (ODEs) to model time series temporal flow. It enhances numerical stability and long-term dependency capture. Tests show a 5.75% error reduction compared to current models.
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