Abstract

This paper explores how localized mixture models can be used for prediction using time series data. The estimation method presented in this study is a kernel-weighted version of an EM-algorithm, where exponential kernels with different bandwidths are used as weight functions. Nadaraya–Watson and local linear estimators are used to carry out localized estimations. Furthermore, in order to demonstrate suitability for prediction at a future time point, a methodology for bandwidth selection and adequate methods are outlined for each model, and then compared with competing forecasting routines. A simulation study is executed to assess the performance of these models for prediction. Furthermore, real data is used to investigate the performance of the localized mixture models for prediction. The data used is predominately taken from the International Energy Agency (IEA).

摘要

本文探讨了局部混合模型如何用于使用时间序列数据进行预测。本研究中提出的估计方法是 EM 算法的核加权版本，其中使用不同带宽的指数核作为权重函数。Nadaraya-Watson 和局部线性估计器用于进行局部估计。此外，为了证明在未来时间点预测的适用性，为每个模型概述了带宽选择方法和适当方法，然后与竞争预测程序进行比较。执行模拟研究以评估这些模型的预测性能。此外，真实数据用于研究局部混合模型的预测性能。