HIGH FREQUENCY SALES FORECAST WITH TIME SERIES MODELS: THE CASE OF A SUPERMARKET NETWORK
Name: HENRIQUE GAVA SERRANO
Publication date: 17/02/2022
Advisor:
Name |
Role |
|---|---|
| ADONAI JOSÉ LACRUZ | Co-advisor * |
| HÉLIO ZANQUETTO FILHO | Advisor * |
Examining board:
| Name |
Role |
|---|---|
| HÉLIO ZANQUETTO FILHO | Advisor * |
| MARCOS PAULO VALADARES DE OLIVEIRA | Internal Examiner * |
Summary: Recent research on sales forecasting at the individual product level is done with weekly
granularity for supermarkets (FILDES et al., 2019). The main objective of the present
research is to compare daily sales forecasting models with weekly ones in the context
of supermarket retailing. Secondarily, it is intended to discuss static and dynamic
models and present the impact of the forecast horizon and training frequency on the
performance of forecast models. In this research, a database of one store of a
Supermarket Chain in Espírito Santo was used. We chose time series models (HoltWinters and ARIMA) and TBATS because it considers multiple seasonalities in the
high frequency (daily) forecast to perform the sales forecast. As a result, it is noted that
the daily forecast provides better predictive performance than the weekly forecast for
the context in question. In addition, the dynamic models provided more accurate
forecasts than the static models. And when comparing the performance of the HoltWinters, ARIMA, and TBATS models, the latter showed greater assertiveness, which
can be explained by the presence of complex seasonality in supermarket retail sales
data.
