Demand Forecasting for E-Commerce Platforms

It gets difficult for e-commerce companies to understand market conditions. The proposed work predicts the demand for the products as per the sales in the e-commerce companies so that there is no shortage of raw materials or the number of units on the inventory side. This paper is a comparative study of the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and Long Short-Term Memory network (LSTM) to predict product demand for the given dataset. Performance, scalability, execution time, accessibility and convenience are the various factors based on which the two models are compared. In SARIMA, the model with the minimum value of the Akaike Information Criterion (AIC) was selected from all the admissible models. The nonlinear demand relationships available in the E-commerce product assortment hierarchy is exploited well by the LSTM.