Time Series Sales Forecasting

The ability to accurately forecast data is highly desirable in a wide variety of fields such as sales, stocks, sports performance, and natural phenomena. Presented here is a study of several time series forecasting methods applied to retail sales data, comprising weekly sales figures from various Walmart department stores across the United States over a period of approximately 2 and a half years. Significant surges in sales are noticeable in the data during pre-holiday and holiday weeks, which present a challenge for any developed forecasting models. The prediction models implemented herein are regression decision trees, Seasonal-Trend Decomposition using Loess and Autoregressive Integrated Moving-Average (STL + ARIMA) models, and timelagged feed-forward neural networks (FFNNs). In particular, the STL + ARIMA and the time-lagged FFNN’s performed reasonably well in forecasting the weekly sales data. The best FFNN implementation, using a time-lag value d = 4 and mean weekly sales as inputs, achieved a mean absolute error of 1252. Weekly sales for the store departments are in the tens of thousands. It is also notable that the results achieved by the time-lagged FFNN’s did not require any deseasonalizing of the sales data, indicating that neural networks may be able to effectively detect and consider any seasonality during training and prediction. ————————————————————————