Extreme Learning Machine and Particle Swarm Optimization for Inflation Forecasting

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.

Website Visitors Forecasting using Recurrent Neural Network Method

“…In forecasting research that used Neural Networks, there are many methods used in the following examples. In the research of Alfiyatin [2] and Oktanisa [12] discusses inflation forecasting in Indonesia. For E research using the method (ELM optimization with the PSO method), and for the I method (SVR optimization with the GA method) The results in research E forecasting with ELM and ELM optimized with PSO, the results are almost the same with the difference of error 0.0000019.…”

Extreme Learning Machine Weight Optimization using Particle Swarm Optimization to Identify Sugar Cane Disease

“…Optimization of the weight value of neurons at Extreme Learning Machine using Particle Swarm Optimization aims to provide the best weight values in the Extreme Learning Machine process [5]. In Particle Swarm Optimization there are particles or a repetitionation of solutions that are the solution to this problem.…”

“…One of the disadvantages of ELM is that the quality of performance results from its classification is strongly influenced by the accuracy of the weight values in its hidden neurons [5]. The initial weight value applied to classifying is not necessarily the best weight value.…”

Inflation Rate Forecasting using Extreme Learning Machine and Improved Particle Swarm Optimization

“…International Journal of Intelligent Engineering and Systems, Vol. 14 The fluctuate pattern of the inflation rate and numerous variables that may influence the rate make the forecasting is a challenging task. Thus, a powerful method is required for the inflation rate forecasting.…”

“…The methods include artificial neural network based methods [8] and fuzzy logic based methods [9]. For inflation rate forecasting , many methods have been used such as backpropagation neural network [10], fuzzy neural system [11], extreme learning machine [12], Hybridizes Fuzzy and PSO-auto speed acceleration algorithm [13], Hybrid ELM and particle swarm optimization [14], Hybrid ELM and genetic algorithms [14]. While the studies report promising result, there are opportunity to do a similar study by implementing a more powerful approach with higher accuracy and lower computational time.…”