Event Details

CMAF Friday Forecasting Talk

The abstract: Machine Learning (ML) methods, such as Neural Networks and Regression Trees, have been proven to be very accurate solutions for time series forecasting. The results of many recent forecasting competitions and studies strongly support their use and, therefore, companies and organisations have increased their expectations in terms of performance. Nevertheless, the post-sample accuracy of ML methods is significantly affected by the hyper-parameters used and the explanatory variables selected for making forecasts, especially when the data available for training are noisy or limited in number. If not tuned properly, ML methods may overfit historical observations and adapt poorly to new, previously unseen data. In this webinar, Evangelos from National Technical University of Athens will talk about two techniques that can be used to make ML methods generalise better, namely cross-validation and data augmentation. These techniques have been employed by the winners of several time series forecasting competitions, including the M5, highlighting their potential and practical importance.