Open AccessComparative Analysis of Prophet and LSTM Model in Drug Sales Forecasting
Author(s) -
Jie Meng,
Xiao Yang,
Chengwei Yang,
Yang Liu
Publication year - 2021
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 - 10.1088/1742-6596/1910/1/012059
Subject(s) - sales forecasting , sales management , pharmaceutical industry , key (lock) , computer science , value (mathematics) , artificial intelligence , business , operations research , machine learning , marketing , engineering , medicine , computer security , pharmacology
With the rapid development of China’s pharmaceutical industry, accurate prediction of drug sales has become the key to enterprises’ competitiveness. Sales forecasting research has very important value for strategic decisions and improvement measures made by enterprises. We studied mainly two machine learning methods of pharmaceutical sales prediction in this paper, analyzed deeply the prophet model and LSTM, and carried out a comparative experiment on these two methods using real sales data. The experimental results show that the LSTM model is more accurate than the Prophet model on drug sales forecasting.