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

Soumya Das, Sarojananda Mishra and ManasRanjan Senapati

Journal of Management Analytics, 2021, vol. 8, issue 1, 113-133

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.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/23270012.2020.1818321 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjmaxx:v:8:y:2021:i:1:p:113-133

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjma20

DOI: 10.1080/23270012.2020.1818321

Access Statistics for this article

Journal of Management Analytics is currently edited by Li Xu

More articles in Journal of Management Analytics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().