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Found 3 papers, 3 papers with code
Dynamic Multi-Network Mining of Tensor Time Series
(a) Interpretable: it characterizes the cluster with multiple networks, each of which is a sparse dependency network of a corresponding non-temporal mode, and thus provides visible and interpretable insights into the key relationships.
Fast and Multi-aspect Mining of Complex Time-stamped Event Streams
Thanks to its concise but effective summarization, CubeScope can also detect the sudden appearance of anomalies and identify the types of anomalies that occur in practice.
SSMF: Shifting Seasonal Matrix Factorization
In general, given a data stream of events with seasonal patterns that innovate over time, how can we effectively and efficiently forecast future events?
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