Modelling and forecasting sorghum (Sorghum bicolor) production in India using hierarchical time-series models
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Authors
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SOUMEN PAL
ICAR–Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi 110 012
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RANJIT KUMAR PAUL
ICAR–Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi 110 012
Keywords:
Bottom-up, Forecasting, Hierarchical time-series, Middle-out, Sorghum Production, Top-downAbstract
Hierarchical time-series comprises of several dataset maintaining certain hierarchical relationship among them. There are certain specialized strategies, viz. top-down, bottom-up, middle-out and optimal approaches which take care of predicting future values for such multi-level data. For forecasting of individual series at different levels of hierarchy, a method of aggregation or disaggregation is followed. In the present study, these methodologies are investigated thoroughly. Further, state-wise seasonal sorghum [Sorghum bicolor (L.) moench] production data of India is analyzed by employing the hierarchical forecasting approaches. A comparative study on performance of different methods is carried out from the viewpoint of multi-step-ahead forecasts on the basis of Mean absolute error (MAE) and Root mean square error (RMSE). The findings show that the middle-out technique outperforms other approaches and traditional method of forecasting as well. This fact has been confirmed statistically by using pair-wise t-test. Finally, using the middle-out approach, forecasts of sorghum production for 2015 till 2017 have been carried out at all hierarchical levels. For statistical analysis, R software package has been employed.
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Athanasopoulos G, Ahmed R A and Hyndman R J. 2009. Hierarchical forecasts for Australian domestic tourism. International Journal of Forecasting 25(1): 146–66. DOI: https://doi.org/10.1016/j.ijforecast.2008.07.004
Box G E P, Jenkins G M and Reinsel G C. 2007. Time-Series Analysis: Forecasting and Control, 3rd edition. Pearson Education, India. DOI: https://doi.org/10.1002/9781118619193.ch5
Dangerfield B J and Morris J S. 1992. Top-down or bottom-up: Aggregate versus disaggregate extrapolations. International Journal of Forecasting 8(2): 233–41. DOI: https://doi.org/10.1016/0169-2070(92)90121-O
Dekker M, Donselaar K V and Ouwehand P. 2004. How to use aggregation and combined forecasting to improve seasonal demand forecasts. International Journal of Production Economics 90(2): 151–67. DOI: https://doi.org/10.1016/j.ijpe.2004.02.004
Espasa A, Senra E and Albacete R. 2002. Forecasting inflation in the European Monetary Union: A disaggregated approach by countries and by sectors. European Journal of Finance 8(4): 402–21. DOI: https://doi.org/10.1080/13518470210167284
Fliedner G. 1999. An investigation of aggregate variable time- series forecast strategies with specific subaggregate time-series statistical correlation. Computers and Operations Research 26(10-11): 1 133–49. DOI: https://doi.org/10.1016/S0305-0548(99)00017-9
Gross C and Sohl J. 1990. Disaggregation methods to expedite product line forecasting. Journal of Forecasting 9(3): 233–54. DOI: https://doi.org/10.1002/for.3980090304
Gooijer J G D and Hyndman R J. 2006. 25 years of time series forecasting. International Journal of Forecasting
(3): 443–73.
Hyndman R J, Ahmed R A, Athanasopoulos G and Shang H L. 2011. Optimal combination forecasts for hierarchical time- series. Computational Statistics and Data Analysis 55(9): 2 579–89. DOI: https://doi.org/10.1016/j.csda.2011.03.006
Hyndman R J, Ahmed R A and Shang H L. 2013. hts: Hierarchical and grouped time-series. R package version 4.3, URL http://CRAN.R-project.org/package=hts.
Hyndman R J and Khandakar Y. 2008. Automatic time-series forecasting: The forecast package for R. Journal of Statistical Software 27(3): 1–22. DOI: https://doi.org/10.18637/jss.v027.i03
Lo S, Wang F and Lin J T. 2008. Forecasting for the LCD monitor market. Journal of Forecasting 27(4): 341–56. DOI: https://doi.org/10.1002/for.1055
Moon S, Hicks C and Simpson A. 2012. The development of a hierarchical forecasting method for predicting spare parts demand in the South Korean Navy-A case study. International Journal of Production Economics 140(2): 794–802. DOI: https://doi.org/10.1016/j.ijpe.2012.02.012
Narasimhan S L, McLeavey D W and Billington P. 1994. Production Planning and Inventory Control, 2ndEdn. Allyn & Bacon, USA.
Widiarta H, Viswanathan S and Piplani R. 2007. On the effectiveness of top-down approach for forecasting autoregressive demands. Naval Research Logistics 54(2): 176–88. DOI: https://doi.org/10.1002/nav.20200
Zellner A and Tobias J. 2000. A note on aggregation, disaggregation and forecasting performance. Journal of Forecasting 19(5): 457–65. DOI: https://doi.org/10.1002/1099-131X(200009)19:5<457::AID-FOR761>3.0.CO;2-6
Zotteri G, Kalchschmidt M and Caniato F. 2005. The impact of aggregation level on forecasting performance. International Journal of Production Economics 93-94: 479–91. DOI: https://doi.org/10.1016/j.ijpe.2004.06.044
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