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Found 3 papers, 1 papers with code
Statistics and Deep Learning-based Hybrid Model for Interpretable Anomaly Detection
Hybrid methods have been shown to outperform pure statistical and pure deep learning methods at both forecasting tasks, and at quantifying the uncertainty associated with those forecasts (prediction intervals).
A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality Modeling
Difficulties with applying hybrid forecast methods to multivariate data include ($i$) the high computational cost involved in hyperparameter tuning for models that are not parsimonious, ($ii$) challenges associated with auto-correlation inherent in the data, as well as ($iii$) complex dependency (cross-correlation) between the covariates that may be hard to capture.
Multivariate Time Series Forecasting
Prediction Intervals
+1
Multivariate Anomaly Detection based on Prediction Intervals Constructed using Deep Learning
We benchmark our approach against the oft-preferred well-established statistical models.
