CMAF FFT: Making your model generalise better: Cross-validation and data augmentation
Channel: Lancaster CMAF
Date: 2022-03-18

This is the eleventh webinar in Season 2 of "Friday Forecasting Talks", hosted by Centre for Marketing Analytics and Forecasting of Lancaster University, UK.
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-- Contents of this video --------------------------
00:00 - Introduction
03:10 - Start of the presentation
08:20 - Advantages and limitations of data-driven learning
11:50 - Data requirements
15:25 - Local vs global models
16:50 - Creating a train set
19:20 - Bootstrapping in forecasting
31:31 - Experimental setup
34:06 - Results
39:00 - Cross-validation vs rolling origin
41:55 - Discussion with Sven Crone
56:23 - Q&A

Speaker: Evangelos Spiliotis

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.

Bio: Evangelos Spiliotis is a Research Fellow at the Forecasting & Strategy Unit, National Technical University of Athens (NTUA), where he also serves as Coordinator. He graduated from the School of Electrical and Computer Engineering, NTUA, where he also got his PhD in Forecasting Support Systems. His research interests include time series forecasting, decision support systems, machine learning, optimisation, and energy efficiency and conservation. He co-organised the M4 and M5 forecasting competitions.

Discussant: Sven Crone