IDEAS home Printed from https://ideas.repec.org/a/blg/journl/v9y2014i1p81-94.html
   My bibliography  Save this article

An Application Of Time Series Arima Forecasting Model For Predicting Sugarcane Production In India

Author

Listed:
  • KUMAR Manoj

    (Victoria University College, Yangon, Myanmar)

  • ANAND Madhu

    (Agra University, UP, India)

Abstract

A time series modeling approach (Box-Jenkins’ ARIMA model) has been used in this study to forecast sugarcane production in India. The order of the best ARIMA model was found to be (2,1,0). Further, efforts were made to forecast, as accurate as possible, the future sugarcane production for a period upto five years by fitting ARIMA(2,1,0) model to our time series data. The forecast results have shown that the annual sugarcane production will grow in 2013, then will take a sharp dip in 2014 and in subsequent years 2015 through 2017, it will continuously grow with an average growth rate of approximately 3% year-on-year.

Suggested Citation

  • KUMAR Manoj & ANAND Madhu, 2014. "An Application Of Time Series Arima Forecasting Model For Predicting Sugarcane Production In India," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 9(1), pages 81-94, April.
    • Handle: RePEc:blg:journl:v:9:y:2014:i:1:p:81-94
    as

    Download full text from publisher

    File URL: http://eccsf.ulbsibiu.ro/RePEc/blg/journl/918kumar&anand.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kenny, Geoff & Meyler, Aidan & Quinn, Terry, 1998. "Forecasting Irish inflation using ARIMA models," Research Technical Papers 3/RT/98, Central Bank of Ireland.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. S, Suresh Kumar & V, Joseph James, 2016. "Precision in Predicting the Stock Prices –An Empirical Approach to Accuracy in Forecasting," MPRA Paper 109026, University Library of Munich, Germany.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Abdullah Ghazo, 2021. "Applying the ARIMA Model to the Process of Forecasting GDP and CPI in the Jordanian Economy," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 12(3), pages 70-77, May.
    2. Kenny, Geoff & Meyler, Aidan & Quinn, Terry, 1998. "Bayesian VAR Models for Forecasting Irish Inflation," Research Technical Papers 4/RT/98, Central Bank of Ireland.
    3. Syarifah Inayati & Nur Iriawan & Irhamah, 2024. "A Markov Switching Autoregressive Model with Time-Varying Parameters," Forecasting, MDPI, vol. 6(3), pages 1-23, July.
    4. Meyler, Aidan, 1999. "A Statistical Measure Of Core Inflation," Research Technical Papers 2/RT/99, Central Bank of Ireland.
    5. Tamerlan Mashadihasanli, 2022. "Stock Market Price Forecasting Using the Arima Model: an Application to Istanbul, Turkiye," Journal of Economic Policy Researches, Istanbul University, Faculty of Economics, vol. 9(2), pages 439-454, July.
    6. Jeff Tayman & Stanley Smith & Jeffrey Lin, 2007. "Precision, bias, and uncertainty for state population forecasts: an exploratory analysis of time series models," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 26(3), pages 347-369, June.
    7. Quinn, Terry & Kenny, Geoff & Meyler, Aidan, 1999. "Inflation Analysis: An Overview," Research Technical Papers 1/RT/99, Central Bank of Ireland.
    8. Mohammad Almasarweh & S. AL Wadi, 2018. "ARIMA Model in Predicting Banking Stock Market Data," Modern Applied Science, Canadian Center of Science and Education, vol. 12(11), pages 309-309, November.
    9. Aguilar, Ruben & Valdivia, Daney, 2011. "Precios de exportación de gas natural para Bolivia: Modelación y pooling de pronósticos [Bolivian natural gas export prices: Modeling and forecast pooling]," MPRA Paper 35485, University Library of Munich, Germany.
    10. Mohammad Rafiqul Islam & Nguyet Nguyen, 2020. "Comparison of Financial Models for Stock Price Prediction," JRFM, MDPI, vol. 13(8), pages 1-19, August.
    11. Shiying Tu & Jiehu Huang & Huailong Mu & Juan Lu & Ying Li, 2024. "Combining Autoregressive Integrated Moving Average Model and Gaussian Process Regression to Improve Stock Price Forecast," Mathematics, MDPI, vol. 12(8), pages 1-15, April.
    12. Sallahuddin Hassan & Zalila Othman, 2018. "Forecasting on the long-term sustainability of the employees provident fund in Malaysia via the Box-Jenkins’ ARIMA model," Business and Economic Horizons (BEH), Prague Development Center, vol. 14(1), pages 43-53, January.
    13. Ntebogang Dinah Moroke, 2014. "The robustness and accuracy of Box-Jenkins ARIMA in modeling and forecasting household debt in South Africa," Journal of Economics and Behavioral Studies, AMH International, vol. 6(9), pages 748-759.
    14. S. AL Wadi & Mohammad Almasarweh & Ahmed Atallah Alsaraireh, 2018. "Predicting Closed Price Time Series Data Using ARIMA Model," Modern Applied Science, Canadian Center of Science and Education, vol. 12(11), pages 181-181, November.
    15. Han Hwa Goh & Kim Leng Tan & Chia Ying Khor & Sew Lai Ng, 2016. "Volatility and Market Risk of Rubber Price in Malaysia: Pre- and Post-Global Financial Crisis," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 14(2), pages 323-344, December.
    16. Kimolo, Deogratius, 2009. "Modelling and Forecasting Inflation in Tanzania: A Univariate Time Series Analysis," MPRA Paper 114782, University Library of Munich, Germany.
    17. Friedrich Fritzer & Gabriel Moser & Johann Scharler, 2002. "Forecasting Austrian HICP and its Components using VAR and ARIMA Models," Working Papers 73, Oesterreichische Nationalbank (Austrian Central Bank).
    18. Ramón Egea Pérez & Mónica Cortés-Molina & Francisco J. Navarro-González, 2021. "Analysis of Rainfall Time Series with Application to Calculation of Return Periods," Sustainability, MDPI, vol. 13(14), pages 1-18, July.
    19. Nyoni, Thabani, 2019. "Forecasting UK consumer price index using Box-Jenkins ARIMA models," MPRA Paper 92410, University Library of Munich, Germany.
    20. Nyoni, Thabani, 2019. "ARIMA modeling and forecasting of Consumer Price Index (CPI) in Germany," MPRA Paper 92442, University Library of Munich, Germany.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:blg:journl:v:9:y:2014:i:1:p:81-94. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mihaela Herciu (email available below). General contact details of provider: https://edirc.repec.org/data/feulbro.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.