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Found 3 papers, 0 papers with code
The mbsts package: Multivariate Bayesian Structural Time Series Models in R
The multivariate Bayesian structural time series (MBSTS) model is a general machine learning model that deals with inference and prediction for multiple correlated time series, where one also has the choice of using a different candidate pool of contemporaneous predictors for each target series.
Linear Iterative Feature Embedding: An Ensemble Framework for Interpretable Model
The LIFE algorithm is able to fit a wide single-hidden-layer neural network (NN) accurately with three steps: defining the subsets of a dataset by the linear projections of neural nodes, creating the features from multiple narrow single-hidden-layer NNs trained on the different subsets of the data, combining the features with a linear model.
Multivariate Bayesian Structural Time Series Model
This paper deals with inference and prediction for multiple correlated time series, where one has also the choice of using a candidate pool of contemporaneous predictors for each target series.
