Global Economic Market Forecast and Decision System for IoT and Machine Learning

Abstract

The fast growth of IoT in wearable devices, smart sensors, and home appliances will affect every aspect of our lives. With the rapid development of economic globalization, how to integrate science and technology into economic decision-making is the focus of the current research field, and the research of this paper is precisely to solve this problem. This paper proposes a global economic market forecasting and decision-making system research based on the Internet of Things and machine learning. Using the wireless sensor network of the Internet of Things technology to perceive and predict the global economic market, through the decision tree method in machine learning, and combine the global economic market to make economic decisions, this paper explores the decision tree algorithm with the highest execution efficiency through the experimental comparison of four decision tree algorithms: ID3 algorithm, C4.5 algorithm, CART algorithm, and IQ algorithm. The output of the experiments in the paper indicates that the C4.5 algorithm has the fastest running speed. When the dataset increases to 110,000, its running time reaches 503 s.