CMAF FFT: On performance of temporal aggregation in time series forecasting
Channel: Lancaster CMAF
Date: 2022-03-04

This is the tenth 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
02:15 - Start of the presentation
07:50 - Forecasting problems
10:36 - Types of temporal aggregation
20:54 - Experiment 1
35:15 - Experiment 2. TS features
50:08 - Discussion from Juan Ramon Trapero
01:01:00 - Q&A

Speaker: Bahman Rostami-Tabar

The abstract: When forecasts are required over the lead-time period, forecasters are presented with three distinct time series to select from: i) the original, ii) the non-overlapping and iii) the overlapping temporally aggregated time series. For the former, the forecast is first generated for h-step ahead and then aggregated to get the total value over the lead-time, however for the latter cases, the time series is first aggregated to match the lead-time period and then 1-atep ahead forecast is produced. Very often, practitioners are encouraged to aggregate the time series to a frequency relevant to the decisions the eventual forecasts will support, using non-overlapping temporal aggregation. Using M4 competition data, we design and execute a full factorial experiment exploring the forecast accuracy of three approaches by varying the lead-time. We then develop an approach to combine forecasts generated from the three-time series that results in more accurate forecast. Moreover, we investigate how temporal aggregation affects time series features and examine the association between the original time series features and the performance of approaches using machine learning

Bio: Bahman is Associate Professor in Management Science at Cardiff Business School, Cardiff University, UK. Bahman holds a Ph.D. in Industrial Engineering from the University of Bordeaux, France. He is the founder of Forecasting for Social Good and Democratising Forecasting initiatives sponsored by the International Institute of Forecasters (You can check out these initiatives at https://www.f4sg.org/). In his research, he has been developing and using forecasting models in healthcare, humanitarian operations and supply chain. He has been working with many organisations including the National Health Service (NHS), Welsh Ambulance Service Trusts (WAST), United States Agency for International Developments (USAID) and John Snow Inc.

Discussant: Juan Ramón Trapero Arenas