Forecasting Volume of Sales During the Abnormal Time Period of COVID-19. An Investigation on How to Forecast, Where the Classical ARIMA Family of Models Fail

Sammanfattning: During the COVID-19 pandemic, customer shopping habits have changed. Some industries experienced an abrupt shift during the pandemic outbreak while others navigate in new normal states. For some merchants, the highly-uncertain new phenomena of COVID-19 expresses as outliers in time series of volume of sales. As forecasting models tend to replicate past behavior of a series, outliers complicates the procedure of forecasting; the abnormal events tend to unreliably replicate in forecasts of the subsequent year(s). In this thesis, we investigate how to forecast volume of sales during the abnormal time period of COVID-19, where the classical ARIMA family of models produce unreliable forecasts. The research revolved around three time series exhibiting three types of outliers: a level shift, a transient change and an additive outlier. Upon detecting the time period of the abnormal behavior in each series, two experiments were carried out as attempts for increasing the predictive accuracy for the three extreme cases. The first experiment was related to imputing the abnormal data in the series and the second was related to using a combined model of a pre-pandemic and a post-abnormal forecast. The results of the experiments pointed at significant improvement of the mean absolute percentage error at significance level alpha=0.05 for the level shift when using a combined model compared to the pre-pandemic best-fit SARIMA model. Also, at significant improvement for the additive outlier when using a linear impute. For the transient change, the results pointed at no significant improvement in the predictive accuracy of the experimental models compared to the pre-pandemic best-fit SARIMA model. For the purpose of generalizing to large-scale conclusions of methods' superiority or feasibility for particular abnormal behaviors, empirical evaluations are required. The proposed experimental models were discussed in terms of reliability, validity and quality. By residual diagnostics, it was argued that the models were valid; however, that further improvements can be made. Also, it was argued that the models fulfilled desired attributes of simplicity, scaleability and flexibility. Due to the uncertain phenomena of the COVID-19 pandemic, it was suggested not to take the outputs as long-term reliable solutions. Rather, as temporary solutions requiring more frequent updating of forecasts.