@article{kumar_arima_2023,
	title = {{ARIMA} model to forecast the {RSS}-1 rubber price in {India}: {A} case study for textile industry},
	volume = {74},
	issn = {12225347},
	shorttitle = {{ARIMA} model to forecast the {RSS}-1 rubber price in {India}},
	url = {http://revistaindustriatextila.ro/images/2023/2/14%20KEPULAJE%20ABHAYA%20KUMAR%20INDUSTRIA%20TEXTILA%20no.2_2023.pdf},
	doi = {10.35530/IT.074.02.2022132},
	abstract = {Various rubber products are used in the textile industry. Due to increased foreign supply and synthetic rubber production,
the price of natural Rubber in India has become more volatile. This paper aims to develop an appropriate model to
predict the weekly price using the Box Jenkins methodology. The weekly price for Indian RSS-1 Rubber for the sample
period from January 2002 to December 2019 has been collected from the official website of the Indian Rubber Board.
ACF and PACF correlograms check the series stationarity and identify the model parameters. A model with the
maximum number of significant coefficients, lowest volatility, lowest Akaike's information criterion (AIC), lowest Schwarz
criterion and highest Adjusted R-squared is tentatively selected as the appropriate model and for the same model
diagnostic check is carried out. An appropriate model to forecast the weekly price for the RSS-1 variety of Rubber is
ARIMA (1, 1, 4).},
	number = {02},
	urldate = {2023-05-07},
	journal = {Industria Textila},
	author = {Kumar, Kepulaje Abhaya and Pinto, Prakash and Spulbar, Cristi and Birau, Ramona and Hawaldar, Iqbal Thonse and Vishal, Samartha and Bărbăcioru, Iuliana Carmen},
	month = may,
	year = {2023},
	pages = {238--245},
}