Open AccessThe Study of a Sales Forecast Model Based on SA-LSTM
Author(s) -
Yuzhen Wang,
Dan Chang,
Changjian Zhou
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1314/1/012215
Subject(s) - sales forecasting , computer science , artificial neural network , sales management , order (exchange) , artificial intelligence , machine learning , data mining , econometrics , finance , marketing , mathematics , business
Sales forecast is an indispensable link in the business activities of enterprises, and the accuracy of prediction is directly related to the effectiveness of enterprise sales and operation activities. In order to improve the prediction accuracy, a sales forecasting model based on LSTM is proposed. The model uses SA to optimize the initial connection weights of LSTM neural network, which solves the problem that the LSTM neural network converges to the local optimal, thus improving the network performance, and then makes an empirical analysis of the construction of the sales forecasting model based on SA-LSTM. The results show that the sales forecasting model improves the prediction accuracy, also reduces the number of iterations, and obtains a good prediction effect.