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Found 4 papers, 2 papers with code
Time Series Anomaly Detection with label-free Model Selection
Our algorithm is easily parallelizable, more robust for ill-conditioned and seasonal data, and highly scalable for a large number of anomaly models.
Boosted Embeddings for Time Series Forecasting
Many advanced autoregressive methods such as ARIMA were used to develop forecasting models.
KATRec: Knowledge Aware aTtentive Sequential Recommendations
Sequential recommendation systems model dynamic preferences of users based on their historical interactions with platforms.
Block-Structure Based Time-Series Models For Graph Sequences
Although the computational and statistical trade-off for modeling single graphs, for instance, using block models is relatively well understood, extending such results to sequences of graphs has proven to be difficult.
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