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Using Bayesian Regression for Stacking Time Series Predictive Models

机译：使用贝叶斯回归来堆叠时间序列预测模型

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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.
机译：本文描述了使用贝叶斯推理对时间序列的不同预测模型进行堆叠回归。 ARIMA，神经网络，随机森林，额外树等模型用于模型集成的第一级预测。在第二层上，这些模型在验证集上的时间序列预测通过贝叶斯回归用于堆叠。这种方法给出了这些模型的回归系数的分布。它使得估计每个模型对叠加结果的不确定性成为可能。有关这些分布的信息使我们可以在考虑领域知识的情况下选择最佳的堆叠模型集。概率模型用于堆叠预测模型，使我们能够对在决策过程中至关重要的预测进行风险评估。

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