A hybrid deep learning framework with CNN and Bi-directional LSTM for store item demand forecasting. (October 2022)

• Record Type: Journal Article
• Authors: Joseph, Reuben Varghese; Mohanty, Anshuman; Tyagi, Soumyae; Mishra, Shruti; Satapathy, Sandeep Kumar; Mohanty, Sachi Nandan
• Is Part Of: Computers & electrical engineering. Volume 103(2022)
• Publication Date: 2022-10
• Subjects: Convolutional neural network -- Bidirectional long short-term memory -- Product demand forecasting -- Lazy Adam optimizer -- Inventory prediction -- Supply chain -- R-squared score -- Mean absolute error -- Mean absolute percentage error -- Machine learning -- Time series analysis; ARIMA Auto-Regressive Integrated Moving Average -- ARIMAX Auto-Regressive Integrated Moving Average with Explanatory Variable -- ARMA Auto-Regressive Moving Average -- BiLSTM Bidirectional Long Short-Term Memory -- BMA Bayesian Model Averaging -- CART Classification and Regression Trees -- CNN Convolutional Neural Network -- DBN Deep Belief Network -- FC Fully Connected -- FFNN Feed Forward Neural Network -- GBRT Gradient Boosted Regression Trees -- GRU Gated Recurrent Unit -- KNN K-Nearest Neighbors -- LSTM Long Short-Term Memory -- MAE Mean Absolute Error -- MAPE Mean Absolute Percentage Error -- MLP Multilayer Perceptron -- MSE Mean Squared Error -- ReLU Rectified Linear Unit -- RMSE Root Mean Square Error -- RNN Recurrent Neural Network -- SARIMA Seasonal Auto-Regressive Integrated Moving Average -- SARIMAX Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors -- SCM Supply Chain Management; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108358
• Languages: English
• ISSNs: 0045-7906

Abstract

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 (R 2 ) value and Mean Absolute Error (MAE), it was observed that the proposed framework having more accurecy as compare to the traditional approaches.