Deep Learning based Seasonality and Trend Detection in Sales Forecasting

Abstract

Sales forecasting is essential for business planning, as it aids inventory management, marketing, and decision-making.  Deep Learning combined with time-series analysis boosts prediction accuracy by capturing intricate temporal patterns.  Precise sales forecasting remains difficult because of trends, seasonality, and noise.  Previous techniques have issues with feature extraction and sequential dependencies, resulting in suboptimal efficiency.  This study aims to develop a Hybrid Deep Learning (HDL) technique that combines the benefits of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to improve sales prediction accuracy.  The primary emphasis is on combining feature extraction and temporal sequence learning to address the shortcomings of conventional methods.  The proposed HDL framework prepares a sales dataset for time-series evaluation using a structured workflow that includes data exploration, preprocessing, and aggregation.  To better comprehend the fundamental patterns, seasonal decomposition and autocorrelation analyses are used.  The sliding window method is used to produce sequential data, which is then split into training and testing sets.  Three predictive models—CNN, LSTM, and a hybrid CNN-LSTM—are built and trained using hyperparameter tuning.  The models are evaluated using performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE).  Experimental results demonstrate that the proposed HDL surpasses CNN and LSTM with the lowest RMSE (2171.38), MAE (1219.79), and MAPE (538.18).  The HDL technique combines CNN and LSTM to enhance sales prediction accuracy by capturing patterns and seasonality for better demand prediction and business evaluation.