Stochastic Time Series Modeling for Coking Coal Production in India
Dr. T. Jai Sankar
, Ms. I. Angel Agnes Mary , Ms. K. Nalini
ARIMA, BIC, Forecasting, MAPE, Coal Production, RMSE
This paper deals with thestochastic time series modelling for Coking Coal (Metallurgical and Non-Metallurgical) production in India during the years from 1981 to 2021. Coking Coal is an essential input for production of Iron and Steel. The largest single use of Coal in the Steel Industry is as a fuel for the blast furnace and for the production of Coal for reduction of iron ore or for injection with the hot blast. This study considers Autoregressive (AR), Moving Average (MA) and ARIMA processes to select the appropriate ARIMA model for Coking Coal production in India. ARIMA (p, d, q) and its components autocorrelation function (ACF), partial autocorrelation function (PACF), root mean square error (RMSE), mean absolute percentage error (MAPE), normalized BIC and ARIMA (1,2,2). Based on the selected model, Coking Coal production in India is projected to decline from 44.79 million tonnes in 2022 to 32.6 million tonnes in 2031.
"Stochastic Time Series Modeling for Coking Coal Production in India", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.7, Issue 10, page no.443 - 449, October-2022, Available :https://ijsdr.org/papers/IJSDR2210077.pdf
Volume 7
Issue 10,
October-2022
Pages : 443 - 449
Paper Reg. ID: IJSDR_201990
Published Paper Id: IJSDR2210077
Downloads: 000347228
Research Area: Applied Mathematics
Country: Tiruchirappalli, Tamil Nadu, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
