Demand Forecasting Models for Medicines through Wireless Sensor Networks Data and Topic Trend Analysis

Abstract

Demand forecasting in the biomedical area is becoming more important because of radical changes in the macroeconomic environment and consumption trends. Moreover, the need for big data analysis on data from wireless sensor networks and social media is increasing because it shows not only the rapidly changing environmental data such as fine dust concentration but also the responses of potential customers that are expected to affect the demand for a medicine. Therefore, demand forecasting models based on data analysis in wireless sensor networks and topic modeling of buzzwords in blog documents were suggested in this study. First, we analyzed topics of documents from blogs that describe the symptoms of certain diseases related to selected medicines. Thereafter, we extracted topic trends for a selected period and constructed demand forecasting models that consist of topic trends, environmental data from wireless sensor networks, and time-series sales data. The experiment results show that topic trends about medicines significantly affect the performance of demand forecasting for these medicines.