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15.01.2022 | Research Paper

An improved monarch butterfly optimization based multivariate fuzzy time series approach for forecasting GDP of India

verfasst von: Vijayendra Vishal Jha, Kanushree Sandeep Jajoo, B. K. Tripathy, M. A. Saleem Durai

Erschienen in: Evolutionary Intelligence | Ausgabe 2/2023

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Abstract

Gross Domestic Product (GDP) is a crucial indicator to evaluate national economic development of a nation and the status of the macro-economy of a country. In the present work, we have proposed a novel approach for predicting India’s nominal GDP. Six new variables have been considered to predict the GDP of India for which a hybridised model comprising of the Multivariate Fuzzy Time Series (MVFTS) model and the Monarch Butterfly Optimization (MBO) algorithm is used. MBO is used to determine the optimal length of intervals in the Universe of Discourse (UoD) while keeping the number of intervals constant. The accuracy of the resulting algorithm is determined by taking the measures, Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The outcome obtained shows that the proposed MVFTS-MBO algorithm outperforms the existing methods for the prediction of India’s GDP.

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Titel: An improved monarch butterfly optimization based multivariate fuzzy time series approach for forecasting GDP of India
verfasst von: Vijayendra Vishal Jha
Kanushree Sandeep Jajoo
B. K. Tripathy
M. A. Saleem Durai
Publikationsdatum: 15.01.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: Evolutionary Intelligence / Ausgabe 2/2023
Print ISSN: 1864-5909
Elektronische ISSN: 1864-5917
DOI: https://doi.org/10.1007/s12065-021-00686-8

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