Impact of Lockdown on India's Index of Industrial Production – Traditional and Deep Learning Statistical Approach

Preethi Patil, Jyothirani S. A. and Haragopal V. V.
Additional contact information
Preethi Patil: Osmania University, India
Jyothirani S. A.: Osmania University, India
Haragopal V. V.: BITS-Pilani, India

European Journal of Mathematics and Statistics, 2022, vol. 3, issue 4, 62-70

Abstract: Index of Industrial Production (IIP) data is one of the important economic indicators that track the manufacturing activity of different sectors of an economy. In this paper, an attempt is made to forecast the IIP data using traditional and deep learning statistical approaches. The data from Apr-2012 to Feb-2020 is used for forecasting. The appropriate best model is evaluated by comparing mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). The results of the study show that RNN is performing better than the other models i.e ARIMA (Traditional method), FFNN, and LSTM (ANN models). Therefore RNN model is used for forecasting. The forecasted values from Mar-2020 to Jun-2021 are compared with the actual IIP values and resulted in a clear decline in industrial production because of lockdown.

Keywords: ARIMA; FFNN; Index of Industrial Production (IIP); LSTM; RNN (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
https://eu-opensci.org/index.php/ejmath/article/view/14124 Abstract page (text/html)
https://eu-opensci.org/index.php/ejmath/article/download/14124/3181 Full text (application/pdf)

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:epw:ejmath:v:3:y:2022:i:4:id:14124

DOI: 10.24018/ejmath.2022.3.4.124

Access Statistics for this article

More articles in European Journal of Mathematics and Statistics from European Open Science
Bibliographic data for series maintained by Support Team ().