RNN / LSTM with modified Adam optimizer in deep learning approach for automobile spare parts demand forecasting

• Published in: Multimedia tools and applications Vol. 80; no. 17; pp. 26145 - 26159
• Main Authors: Chandriah, Kiran Kumar, Naraganahalli, Raghavendra V.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2021; Springer Nature B.V
• Subjects: Algorithms; Automobile sales; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Deep learning; Demand; Economic forecasting; Inventory; Inventory control; Lead time; Machine learning; Multimedia Information Systems; Recurrent neural networks; Spare parts; Special Purpose and Application-Based Systems; Deep learning; Recurrent neural networks; Spare parts; Long-short term memory; Modified-Adam; Demand forecasting

The spare parts demand forecasting is very much essential for the organizations to minimize the cost and prevent the stock outs. The demand of spare parts/ car sales distribution is an important factor in inventory control. The valuation of the demand is challenging as the automobile spare parts/car...