Forecasting GDP using ARIMA and Artificial
Neural Networks Models under Indian
Environment

Sunitha.G; Sampath Kumar.K; JyothiRani.S. A; Haragopal.V.V

doi:https://doi.org/10.14445/22315373/IJMTT-V56P508

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 56 | Number 1 | Year 2018 | Article Id. IJMTT-V56P508 | DOI : https://doi.org/10.14445/22315373/IJMTT-V56P508

Forecasting GDP using ARIMA and Artificial Neural Networks Models under Indian Environment

Sunitha.G, Sampath Kumar.K, JyothiRani.S. A, Haragopal.V.V

Citation :

Sunitha.G, Sampath Kumar.K, JyothiRani.S. A, Haragopal.V.V, "Forecasting GDP using ARIMA and Artificial Neural Networks Models under Indian Environment," International Journal of Mathematics Trends and Technology (IJMTT), vol. 56, no. 1, pp. 60-70, 2018. Crossref, https://doi.org/10.14445/22315373/IJMTT-V56P508

Abstract

Gross Domestic Product (GDP) is one of the most important economic factors world over. India’s growth majorly depends on the market and economy as a whole. In this paper an attempt is made to forecast the GDP growth. From the past experience it is evident that the variation in the GDP economy was cyclical. To see this behaviour we evaluate the analytics by considering the data drawn from Reserve Bank of India (RBI) for the period 1951 to 2016. Out of a variety of forecasting models, Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) – Multilayer Perception Model are evaluated to forecast the GDP. In this study Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) are calculated for ARIMA model and ANN model. Using specifically RMSE and MAPE values, both the models are compared and it is observed from the analytics, that ANN is performing better than the traditional statistical models viz., ARIMA.

Keywords

GDP, ARIMA, ANN, RMSE, MAPE, Forecasting.

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