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



Abstract Views
211

Downloads
182

Citations

Share

##plugins.themes.bootstrap3.article.sidebar##

Submitted May 26, 2022
Published Aug 29, 2022
10.24018/ejmath.2022.3.4.124

Abstract viewed = 211 times

Table of Contents

##plugins.themes.bootstrap3.article.main##

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

References

  1. Singh SS, Devi TL, Roy TD. Time series analysis of index of industrial production of India. IOSR Journal of Mathematics. 2016; 12(3): 1-7.
     Google Scholar
  2. Department, Minister’s Secretariat, Ministry of Economy, Trade, and Industry. Economic Analysis Office Research and Statistics. 2015.
     Google Scholar
  3. Çekim HÖ. Examination of industry production index in Turkey with time series method. BalıkesirÜniversitesi Fen BilimleriEnstitüsüDergisi. 2018; 20(1): 547-54.
     Google Scholar
  4. Tomić D, Stjepanović S. Forecasting Capacity of ARIMA Models; A Study on Croatian Industrial Production and its Sub-sectors. Zagreb International Review of Economics & Business. 2017; 20(1): 81-99.
     Google Scholar
  5. Permanasari AE, Hidayah I, Bustoni IA. SARIMA (Seasonal ARIMA) implementation on time series to forecast the number of Malaria incidence.International Conference on Information Technology and Electrical Engineering (ICITEE). 2013; 203-207.
     Google Scholar
  6. Shakti SP, Hassan MK, Zhenning Y, Caytiles RD, Iyenger NC. Annual automobile sales prediction using ARIMA model. International Journal of Hybrid Information Technology. 2017; 10(6): 13-22.
     Google Scholar
  7. Mas JF, Flores JJ. The application of artificial neural networks to the analysis of remotely sensed data. International Journal of Remote Sensing. 2008; 29(3): 617-63.
     Google Scholar
  8. Wikipedia.org. Index of industrial production [Internet]. 2022. Available from: https://en.wikipedia.org/wiki/Index_of_industrial_production.
     Google Scholar