Prediction of footwear demand using Prophet and SARIMA

Abstract

[EN]In an increasingly globalized market, where world container traffic since 2000 has almost quadrupled, the prediction of demand is an element of great importance for the optimal business development of a company. This work focuses on demand forecasting in the fashion sector. It is a very volatile market, with some characteristics such as: seasonality, culture and fashion trends, that makes it difficult to estimate the inter-seasonal footwear demand. In recent years, many algorithms for the prediction of demand have been studied; they can be divided into three natures: statistical algorithms, artificial intelligence algorithms and hybrid algorithms, each of them with its own characteristics. AI-generated predictions provide business professionals with the ability to organize the purchase of materials, manage production processes and stock quantity. Therefore, the purpose of this work is to forecast the long-term sales of a highly seasonal footwear model based on its historical data, using the Prophet and SARIMA (Seasonal Autoregressive Integrated Moving Average) algorithms. This represents a novelty as sales predictions for footwear in the state of the art are not typically made over the long term or highly seasonal, and there is no model that clearly outperforms others. Additionally, a set of Key Performance Indicators has been established to evaluate the prediction outcomes, as the same indicators such as MAE, MAPE and RMSE are commonly used in the state of the art. Furthermore, a relational database structure has been proposed for the organized storage of future predictions. Finally, the results between Prophet and SARIMA have been compared to ascertain whether Prophet (a non-linear statistical algorithm) outperforms SARIMA (a linear statistical algorithm). In the model prediction Prophet obtains an accuracy of 98.8% and a 158.8 MAE, while SARIMA reaches an accuracy of 93% and a 83.9 MAE; all in all really positive results taking into account long-term prediction and high seasonality. It has been observed how Prophet provides better results when it comes to predicting results of annual quantities, for example, the number of shoes that are expected to be sold next year. However, SARIMA returns better results for those KPI that are calculated considering the monthly distribution of the prediction, as well as being 15 times faster in the mean prediction time.