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Real-world time series forecasting systems operate under non-stationary data streams, where forecasting performance may degrade over time. Although retraining can recover the performance, it incurs non-trivial computational and operational costs. Under limited deployment resources, the key challenge is therefore not on...
MambaSL is proposed, a framework that minimally redesigns the selective SSM and projection layers of a single-layer Mamba, guided by four TSC-specific hypotheses, and achieves state-of-the-art performance with statistically significant average improvements, while ensuring reproducibility via public checkpoints for all...
The proposed mask-aware state space classifier for irregular time series (MASCIT), which supplies observation masks to the encoder and excludes invalid steps from gated temporal aggregation, supports selective state space models as effective, executable backbones for naturally irregular time series classification.
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