W.F. Mahmudy, - Inflation Rate Forecasting Using Extreme Learning Machine and Improved Particle Swarm Optimization.
Abstract
Inflation is an important tool to assess the current condition of a nation economy. Uncontrolled inflation rate may have a lot of negative impacts on economic development. Forecasting can be used to control the inflation by making appropriate economic policies. However, uncertainty pattern of the inflation rate may make it hard to forecast. This study proposes an extreme learning machine (ELM) for the inflation rate forecasting. As part of the machine learning algorithm, the ELM has an ability to address uncertainty in data input pattern. However, ELM had weakness in determining initial weights that may produce inaccurate results. So, we propose particle swarm optimization (PSO) to determine good initial weights for the ELM. PSO is a metaheuristic method that gives good results in local searches but requires longer computation time to locate its particles on the global optimum point in the vast search space area. To overcome this problem, auto-speed acceleration algorithm is employed to drive particles of the PSO in searching of the global optimum with lower computation time. The performance of the proposed approach is evaluated using root mean square error (RMSE). A series of computational experiments prove that the proposed approach achieves better results with the average RMSE of 0.01926. This result is better than RMSE of 0.02020 achieved by the original version of ELM.
English Abstract
Inflation is an important tool to assess the current condition of a nation economy. Uncontrolled inflation rate may have a lot of negative impacts on economic development. Forecasting can be used to control the inflation by making appropriate economic policies. However, uncertainty pattern of the inflation rate may make it hard to forecast. This study proposes an extreme learning machine (ELM) for the inflation rate forecasting. As part of the machine learning algorithm, the ELM has an ability to address uncertainty in data input pattern. However, ELM had weakness in determining initial weights that may produce inaccurate results. So, we propose particle swarm optimization (PSO) to determine good initial weights for the ELM. PSO is a metaheuristic method that gives good results in local searches but requires longer computation time to locate its particles on the global optimum point in the vast search space area. To overcome this problem, auto-speed acceleration algorithm is employed to drive particles of the PSO in searching of the global optimum with lower computation time. The performance of the proposed approach is evaluated using root mean square error (RMSE). A series of computational experiments prove that the proposed approach achieves better results with the average RMSE of 0.01926. This result is better than RMSE of 0.02020 achieved by the original version of ELM.
| Item Type: | Article |
|---|---|
| Depositing User: | Unnamed user with email prayoga |
| Date Deposited: | 15 Dec 2021 14:21 |
| Last Modified: | 15 Dec 2021 14:21 |
| URI: | http://repository.ub.ac.id/id/eprint/187137 |
Actions (login required)
 |
View Item |