A comparative study of univariate time-series methods for sales forecasting. (24th October 2022)

• Record Type: Journal Article
• Authors: Shah, Vishvesh; Dimitrov, Stanko
• Is Part Of: International journal of business and data analytics. Volume 2:Number 2(2023)
• Page Start: 187
• Page End: 216
• Publication Date: 2022-10-24
• Subjects: sales time-series forecasting -- SARIMA -- long short-term memory -- LSTM -- comparison via root mean square error; Commercial statistics -- Data processing -- Periodicals; Industrial management -- Mathematical models -- Periodicals; Business -- Mathematical models -- Periodicals; Management -- Statistical methods -- Periodicals; Business -- Research -- Periodicals; 658.403
• Languages: English
• ISSNs: 2515-9100

Abstract

Firms use time-series forecasting methods to predict sales. However, it is still a question which time-series method a forecaster is best, if only a single forecast is needed. This study investigates and evaluates different sales time-series forecasting methods: multiplicative Holt-Winters (HW), additive HW, seasonal auto regressive integrated moving average (SARIMA) [a variant of auto regressive integrated moving average (ARIMA)], long short-term memory (LSTM) recurrent neural networks and the Prophet method by Facebook on 32 univariate sales time-series. The data used to forecast sales is taken from Time Series Data Library (TSDL). With respect to the root mean square error (RMSE) evaluation metric, we find that forecasting sales with the SARIMA method offers the best performance, on average, relative to the other compared methods. To support the findings, both mathematical and economic drivers of the observed performance are provided.