Abstract
This article presents the model to sales forecast in marketplace and compares with different machine learning models to predict the demand in the future. With the recent advancement of deep learning architecture, it is possible to handle large voluminous market data. In this article, we have proposed long short-term memory (LSTM) network which takes the historical sales data of the products as input and forecasts the demand of each product for next three time series. The raw sales data are first preprocessed using various techniques, and then, the processed input is fed into the model. The sales data are fine-tuned periodically based on the actual demand. The real-time sales data are acquired from markets situated in and around southern state of India, and our work is validated with 60–40 split in which 60% data are used as training and 40% data are used as testing and gives best forecasting accuracy with lowest error value.
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Lakshmanan, B., Vivek Raja, P.S.N., Kalathiappan, V. (2020). Sales Demand Forecasting Using LSTM Network. In: Dash, S., Lakshmi, C., Das, S., Panigrahi, B. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 1056. Springer, Singapore. https://doi.org/10.1007/978-981-15-0199-9_11
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DOI: https://doi.org/10.1007/978-981-15-0199-9_11
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