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Demand Forecasting For Wholesale Store Using Deep Learning Methods

Rajput, Aryan (2022) Demand Forecasting For Wholesale Store Using Deep Learning Methods. Masters thesis, Dublin, National College of Ireland.

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Demand forecasting has become an interesting topic among e-commerce domain, and it is very pivotal among warehouses. According to the present market trend, it has become increasingly important to deliver things to customers as quickly as possible and on schedule. It is necessary for warehouses to keep track of the availability and demand of things that clients seem to purchase. Forecasting demand is a way of creating a model that seems to predict the demand for a certain item at any point in the future. The demand forecasting tactics can be linked to maintain a smooth distribution of the products that consumers will buy at certain time in the future. Along with to provide better stock management, stock management’s another primary aim is to reduce the total cost of stocked inventories. To complete this task, Seasonal Autoregressive Integrated Moving Average(SARIMA) and Recurrent Neural Network(RNN) has been applied. The outcome of both the models has been compared with mean error. SARIMA performed with a Root Mean Square Error(RMSE) value of 235498.28, whereas for RNN the Mean Absolute Error(MAE) value is obtained as 56.87% and correctness of 43.12%.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Demand Forecasting; Seasonal Autoregressive Integrated Moving Average; Recurrent Neural Network; deep learning
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
H Social Sciences > HD Industries. Land use. Labor > Business Logistics
H Social Sciences > HF Commerce > Electronic Commerce
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 01 Mar 2023 12:48
Last Modified: 01 Mar 2023 17:28

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