文章基本信息

标题：Extreme Learning Machine and Particle Swarm Optimization for Inflation Forecasting
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作者：Adyan Nur Alfiyatin ; Agung Mustika Rizki ; Wayan Firdaus Mahmudy 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：4
页码：473-478
DOI：10.14569/IJACSA.2019.0100459
出版社：Science and Information Society (SAI)
摘要：Inflation is one indicator to measure the development of a nation. If inflation is not controlled, it will have a lot of negative impacts on people in a country. There are many ways to control inflation, one of them is forecasting. Forecasting is an activity to find out future events based on past data. There are various kinds of artificial intelligence methods for forecasting, one of which is the extreme learning machine (ELM). ELM has weaknesses in determining initial weights using trial and error methods. So, the authors propose an optimization method to overcome the problem of determining initial weights. Based on the testing carried out the purposed method gets an error value of 0.020202758 with computation time of 5 seconds.
关键词：Extreme learning machine; particle swarm optimization; inflation; prediction