MoTIF decomposes irregular time series into interpretable temporal patterns using a mixture-of-experts attention mechanism.
Read the original at arxiv.org→arXiv:2611.00337v1 Announce Type: new Abstract: Irregularly-spaced sequential data, such as transaction logs, encode multi-scale temporal structure that is critical for prediction but difficult to recover explicitly...
Original headline: "Decomposing Irregular Time Series into Interpretable Temporal Patterns with Mixture-of-Experts"
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- Nov 3, 05:00 UTC arXiv cs.LG lead source Decomposing Irregular Time Series into Interpretable Temporal Patterns with Mixture-of-Experts