A hybrid deep learning framework with CNN and Bi-directional LSTM for store item demand forecasting

Abstract

In the era of ever-changing market landscape, enterprises tend to make quick and informed decisions to survive and prosper in the competition. Decision makers within an organization must be supplied with data in a way that could be easily analyzed and comprehended to build strategies in order to achieve business goals. Accurate demand forecasting of products is one of such decisions which is crucial for retail operators to have a clear picture on the future demand of their products and services. With a certainty in estimation, retailers might keep a check on how many items to allocate, order and restock thus boosting their gross sales and profits. Machine Learning approaches are widely used for demand forecasting of different items. In this work, we have used the Store Item Demand Forecasting Challenge dataset from Kaggle to implement our proposed framework. The main novelty of this study was to build a coupled CNN-BiLSTM framework with Lazy Adam optimizer to make an accurate forecast of product demand of store items. Various State-of-art machine learning techniques like SGD (Stochastic Gradient Descent), Linear Regression, K-Nearest Neighbour, Bagging, Random Forest, SVR, XgBoost (extreme gradient boosting) and CNN-LSTM. for demand forecasting has been implemented and the results were compared with the proposed model. On evaluation with metrics including Mean Absolute Percentage Error (MAPE), R-Squared (R2) value and Mean Absolute Error (MAE), it was observed that the proposed framework having more accurecy as compare to the traditional approaches.