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

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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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Data availibility

Data was collected from https://www.ceicdata.com/en/indicator/india for different parameters and then combined together to form a dataset on which the research was carried out. In case of more details, the corresponding author can be contacted.

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  1. Vijayendra Vishal Jha, Kanushree Sandeep Jajoo, B. K. Tripathy & M. A. Saleem Durai

    Present address: Vellore Institute of Technology, Vellore, Tamil Nadu, India

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    1. Vijayendra Vishal Jha
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    Jha, V.V., Jajoo, K.S., Tripathy, B.K. et al. An improved monarch butterfly optimization based multivariate fuzzy time series approach for forecasting GDP of India. Evol. Intel. 16, 605–619 (2023). https://doi.org/10.1007/s12065-021-00686-8

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