CMAF FFT: Identifying sequential changes in mean and variance within more complex model structures
Channel: Lancaster CMAF
Date: 2021-10-04

This is the first 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
02:58 - The start
04:30 - Motivation
09:08 - Problems
12:11 - Changepoint in forecasting
18:41 - Online changepoint
20:20 - Detecting changes
27:00 - Simulations
29:04 - A&E Admissions example
32:22 - Royal Mail example
35:12 - Threshold choice
36:56 - Discussion by Nikos Kourentzes
37:35 - How relevant is anomaly detection in practice?
41:13 - Are we overfitting when including changepoint elements?
45:20 - Can changepoint detection be used in judgmental forecasting?
49:28 - Is there any work on changepoint detection of earthquakes?
51:38 - How to incorporate structural changes in forecasts?
52:20 - How long should the lags be for the changepoint?
54:12 - Final words

The abstract: In many organisations, accurate forecasts are essential for making adequate informed decisions for a variety of applications from inventory management to staffing optimization. Whatever forecasting model is used, changes in the underlying process can lead to inaccurate forecasts, which will be damaging for decision making.  At the same time forecasting models are becoming increasingly complex and identifying change through direct modelling is problematic. We present a novel framework for monitoring forecasts to ensure they remain accurate. By utilizing sequential changepoint techniques on the forecast errors, our framework allows for the real-time identification of potential changes in the process caused by various external factors. We demonstrate the effectiveness of this framework on numerous forecasting frameworks through simulations, and show its effectiveness over alternative approaches. Finally, we present two concrete examples, one from Royal Mail parcel delivery volumes and one from NHS A&E admissions relating to gallstones.
Speaker: Rebecca Killick
Bio: Dr Killick is Associate Professor in Statistics at Lancaster University, UK. Her primary research interests lie in the development of novel methodology for the analysis of univariate and multivariate nonstationary time series models; changepoints and locally stationary models. This covers many topics including developing models, model selection, efficient estimation, diagnostics, clustering and prediction.  Rebecca is highly motivated by real world problems and has worked with data in a range of fields including Bioinformatics, Energy, Engineering, Environment, Finance, Health, Linguistics and Official Statistics.
Discussant: Prof. Nikolaos Kourentzes