A Hybrid Approach for Sales Forecasting: Combining Deep Learning and Time Series Analysis

Abstract

Sales forecasting is an essential task for businesses as it enables suppliers to analyze customer preferences, thereby optimizing profits, reducing costs, and minimizing product returns. Confronting the complexities of sales forecasting, this research introduces a new hybrid model for sales forecasting that combines classic time series analysis with advanced deep learning techniques to address the limitations present in existing forecasting models. This model combines Long Short-Term Memory (LSTM), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Triple Exponential Smoothing (Holt-Winters) to capture complex patterns, handle linear trends and seasonal patterns, and emphasize recent sales trends. A comparative analysis using the Mean Absolute Percentage Error (MAPE) metric demonstrates the enhanced performance of the hybrid model over the individual components. The findings indicate that the hybrid model surpasses LSTM, SARIMA, and Holt-Winters models by 9%, 39%, and 43%, respectively. This improvement in forecasting accuracy significantly benefits marketplace management by offering more reliable sales predictions. Applying this model facilitates the prediction of sales for the next ‘n’ days, informing inventory management, pricing strategies, and promotional planning to optimize sales performance.