Improving time series forecasting using elephant herd optimization with feature selection methods

Abstract

The time series data is chaotic, non seasonal, non stationary and random in nature. It becomes quite challenging to discover the hidden patterns of time series data. In this paper the time series data is predicted with the help of a machine learning algorithm i.e. Elephant Herd Optimization (EHO). Three different types of time series data are used to testify the superiority of the proposed method namely stock market data, currency exchange data and absenteeism at work. The data are first subjected to feature selection methods namely ANOVA and Friedman test. The feature selection methods provide relevant set of features which is fed to the neural network trained with the method. The proposed method is also compared with other methods such as local linear radial basis functional neural network and particle swarm optimization. The results prove supremacy of EHO over other methods.

Keywords:

• particle Swarm Optimization (PSO)
• Local Linear Radial Basis Functional Neural Network (LLRBFNN)
• Elephant Herding Optimization (EHO)
• ANOVA
• Friedman test

Disclosure statement

No potential conflict of interest was reported by the author(s).