Extreme learning machine and particle swarm optimization for inflation forecasting

Open

Adyan Nur Alfiyatin, Agung Mustika Rizki, Wayan Firdaus Mahmudy, Candra Fajri Ananda

2019 International Journal of Advanced Computer Science and Applications Vol. 10 Issue 4 Article Cited by 7 SDG 9 Quartile

Abstract

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. © 2018 The Science and Information (SAI) Organization Limited.

Affiliations

Faculty of Computer Science, Brawijaya University, Malang, Indonesia; Faculty of Economics and Business, Brawijaya University, Malang, Indonesia

Research at a Glance

Premium content — register to unlock

Research at a Glance

Register to unlock

Topics & SDG Alignment

Premium content — register to unlock

Topics & SDG Alignment

Register to unlock

Collaboration

Premium content — register to unlock

Collaboration

Register to unlock

Author Profile (Selected)

Premium content — register to unlock

Author Profile (Selected)

Register to unlock

References Overview

Premium content — register to unlock

References Overview

Register to unlock

Journal & Source

Premium content — register to unlock

Journal & Source

Register to unlock

Metadata & Integrity

Premium content — register to unlock

Metadata & Integrity

Register to unlock