Using Bayesian Regression for Stacking Time Series Predictive Models
Bohdan M. Pavlyshenko · 2020
The paper describes the use of Bayesian inference for stacking regression of different predictive models for time series. The models ARIMA, Neural Network, Random Forest, Extra Tree were used for the prediction on the first level of model ensemble. On the second level, time series predictions of these models on the validation set were used for stacking by Bayesian regression. This approach gives distributions for regression coefficients of these models. It makes it possible to estimate the uncertainty contributed by each model to stacking result. The information about these distributions allows us to select an optimal set of stacking models, taking into account the domain knowledge. A probabilistic approach for stacking predictive models allows us to make risk assessment for the predictions that is important in a decision-making process.