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Long-term time series forecasting requires learning temporal dependencies that remain stable and generalizable under temporal distribution shifts. However, real-world time series are often noisy, non-stationary, and heterogeneous, making deep learning models prone to overfitting incidental temporal interactions in the...
Current mainstream research in multivariate time series (MTS) forecasting often assumes that all data are static. However, real-world MTS data typically arrives continuously in a streaming manner, which we refer to as streaming MTS. The statistical characteristics and spatiotemporal graph topology of these data evolve...
DiDPO is proposed, a critic-free RL method that constructs fine-grained credit units directly from the structure of code diffs directly from the structure of code diffs, offering a principled framework for fine-grained credit assignment in coding agent training.
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