Generalized Exponential smoothing in prediction of hierarchical time series

TL;DR

This paper introduces a modified generalized exponential smoothing method for hierarchical time series forecasting, aiming to improve robustness and accuracy in demand prediction models.

Contribution

It extends previous grouped functional time series forecasting approaches by integrating generalized exponential smoothing for better robustness.

Findings

Simulation studies demonstrate improved forecast accuracy.

Real data analysis confirms robustness of the proposed method.

Enhanced prediction performance over existing approaches.

Abstract

Shang and Hyndman (2017) proposed a grouped functional time series forecasting approach as a combination of individual forecasts obtained using generalized least squares method. We modify their methodology using generalized exponential smoothing technique for the most disaggregated functional time series in order to obtain more robust predictor. We discuss some properties of our proposals basing on results obtained via simulation studies and analysis of real data related to a prediction of a demand for electricity in Australia in 2016.