Extreme Learning Machine and Particle Swarm Optimization for Inflation Forecasting

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.

@article{Alfiyatin2019,
  title     = {Extreme Learning Machine and Particle Swarm Optimization for Inflation Forecasting},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {10},
  number    = {4},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {Adyan Nur Alfiyatin and Agung Mustika Rizki and Wayan Firdaus Mahmudy and Candra Fajri Ananda},
  doi       = {10.14569/IJACSA.2019.0100459},
  url       = {https://doi.org/10.14569/IJACSA.2019.0100459}
}