Enhancement of Sales Forecast Using Hybrid Sarima and Extreme Machine Learning : A Case for a Jewelry Retailer in Viet Nam

A reliable sales forecasting system is essential for fashion merchants within the fashion retailing industry. The accuracy of forecast will determine the retailer's profitability or loss, which allows a business to anticipate future market demand and change inventory levels accordingly. Therefore, this study proposes a novel hybrid method based on Seasonal Autoregressive Integrated Moving Average (SARIMA) and Extreme machine learning (ELM) for improving sales forecasting accuracy. Following, comparing the current methods used in the company, such as SARIMA and Holt-Exponential Winter's Smoothing (Holt-Winters) with SARIMA-ELM algorithms to produce high-accuracy consumer transaction estimates