Forecasting Sugarcane Yield of India based on rough set combination approach

Haresh Kumar Sharma; Kriti  Kumari; Samarjit  Kar

doi:10.31181/dmame210402163s

Authors

Haresh Kumar Sharma Department of Mathematics, Shree Guru Gobind Singh Tricentenary University, Gurugram, India
Kriti Kumari Department of Mathematics, Banasthali Vidyapith, Jaipur, Rajasthan, India
Samarjit Kar Department of Mathematics, National Institute of Technology Durgapur, West Bengal, India

DOI:

https://doi.org/10.31181/dmame210402163s

Keywords:

Sugarcane, Forecast, time series models, Rough set combination

Abstract

This study applied a novel rough set combination approach for forecasting sugarcane production in India. The paper uses autoregressive integrated moving average (ARIMA), double exponential smoothing (DES) and Grey model (GM) to generate the single forecasts. Also, the weight coefficient is evaluated by underlying the rough set approach to combine the single forecasts obtained from different models. To validate our proposed analysis, Sugarcane from 1950 to 2011 was used for the overall empirical analysis and generate out-sample forecasts from 2012 to 2021 for the comparative analysis. Also, ARIMA (2, 1, 1) model is found more appropriate for forecasting Sugarcane production.

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References

Ahmed E. F., W. J. Yang, & M. Y. Abdullah. (2009). Novel method of the combination of forecasts based on rough sets, Journal of Computer Science, 440-444.

Aiolfi, M., & Timmermann, A. (2006). Persistence in forecasting performance and conditional combination strategies. Journal of Econometrics, 135, 31–53.

Ala’raj, M., Majdalawieh, M., & Nizamuddin, N. (2021). Modeling and forecasting of COVID-19 using a hybrid dynamic model based on SEIRD with ARIMA corrections. Infectious Disease Modelling, 6, 98-111.

Andrawis, R. R., A. F. Atiya, & H. E. Shishiny. (2011). Combination of long term and short-term forecasts, with application to tourism demand forecasting. International Journal of Forecasting, 27, 870–886.

Armstrong, J. S. (2001). Combining Forecasts in Principles of Forecasting: A Handbook for Researchers and Practitioners, 417-439.

Bajpai, P. K., & R. Venugopalan. (1996). Forecasting sugarcane production by time series modeling. Indian Journal of Sugarcane Technology, 11(1), 61–65.

Balanagammal, D., C. R. Ranganathan, and K. Sundaresan. (2000). Forecasting of agricultural scenario in Tamilnadu—A time series analysis. Journal of Indian Society of Agricultural Statistics, 53(3), 273–286.

Balasubramanian, P., & Dhanavanthan, P. (2002). Seasonal modeling and forecasting of crop production. Statistics and Applications, 4(2), 107-118.

Bao, Y., M. Huang., Zhu., Zheyan., Y. He., & X. Li. (2006). Application of combination forecasting based on rough sets theory on electric power system, Proceedings of the 6th Congress on Intelligent Control and Automation. June 21-23. Dallan, China: 1745-1748.

Bates, J. M., and C. Granger. (1969). The combination of forecasts, Operational Research Quarterly, 20, 451-468.

Boken, V. K. (2000). Forecasting spring wheat yield using time series analysis: A case study for the Canadian prairies. Agricultural Journal, 92(6), 1047–1053.

Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.

Chandran, K. P. Prajneshu. (2005). Nonparametric regression with jump points methodology for describing country’s oilseed yield data. Journal of Indian Society of Agricultural Statistics, 59(2), 126-130.

Deng, J. (1982). Control problems of Grey systems. Systems and Control Letters, 1(1), 288-294.

Elgabbanni, B. O. S., M. O. Khozium, & M. A. Ahmed. (2014). Combination prediction model of traffic accident using Rough Set technology approach, International Journal of Enhanced Research in Science Technology Engineering, 3(1), 47-56.

Hansen, J. V., McDonald, J. B., & Nelson, R. D. (1999). Time series prediction with Genetic‐Algorithm designed neural networks: An empirical comparison with modern statistical models. Computational Intelligence, 15(3), 171-184.

Holt, C. C. (1957). Forecasting seasonals and trends by exponentially weighted moving averages. Pittsburgh PA: Carnegie Institute of Technology.

Indira, R., & A. Datta. (2003). Univariate forecasting of state-level agricultural production. Economic and Political Weekly, 38, 1800–1803.

Jahangir, H., Golkar, M. A., Alhameli, F., Mazouz, A., Ahmadian, A., & Elkamel, A. (2020). Short-term wind speed forecasting framework based on stacked denoising auto-encoders with rough ANN. Sustainable Energy Technologies and Assessments, 38, 100601.

Karavidić, Z., & Projović, D. (2018). A multi-criteria decision-making (MCDM) model in the security forces operations based on rough sets. Decision Making: Applications in Management and Engineering, 1(1), 97-120.

Kumar, M., & Anand, M. (2014). An application of time series ARIMA forecasting model for predicting sugarcane production in India. Studies in Business and Economics, 9(1), 81-94.

Lewis, C. D. (1982). International and business forecasting methods. London: Butterworths.

Li, S., & Wang, Q. (2019). India's dependence on foreign oil will exceed 90% around 2025-The forecasting results based on two hybridized NMGM-ARIMA and NMGM-BP models. Journal of Cleaner Production, 232, 137-153.

Maccioitta, N. P. P., D. Vicario, G. Pulina, & A. Cappio-Borlino. (2002). Test day and lactation yield predictions in Italian simmental cows by ARMA methods. Journal of Dairy Science, 85, 3107–3114.

Maccioitta, N.P. P., A. Cappio-Borlino, & G. Pulina. (2000). Time series autoregressive integrated moving average modeling of test-day milk yields of dairy ewes. Journal of Dairy Science, 83, 1094–1103.

De Menezes, L. M., Bunn, D. W., & Taylor, J. W. (2000). Review of guidelines for the use of combined forecasts. European Journal of Operational Research, 120(1), 190-204.

Mukhametzyanov, I. & Pamucar, D. (2018). A sensitivity analysis in MCDM problems: A statistical approach. Decision Making: Applications in Management and Engineering, 1(2), 51-80.

Pamucar, D., & Ćirović, G. (2018). Vehicle route selection with an adaptive neuro fuzzy inference system in uncertainty conditions. Decision Making: Applications in Management and Engineering, 1(1), 13-37.

Patra, S., & Barman, B. (2021). A novel dependency definition exploiting boundary samples in rough set theory for hyperspectral band selection. Applied Soft Computing, 99, 106944.

Pawlak, Z. (1982). Rough sets, International Journal of Computer and Information Science, 11, 341-356.

Predki, B., Słowiński, R., Stefanowski, J., Susmaga, R., & Wilk, S. (1998). ROSE-software implementation of the rough set theory. In Rough Sets and Current Trends in Computing: First International Conference, RSCTC’98 Warsaw, Poland, June 22–26, 1998 Proceedings 1 (pp. 605-608). Springer Berlin Heidelberg.

Roy, J., Adhikary, K., Kar, S. & Pamucar, D. (2018). A rough strength relational DEMATEL model for analysing the key success factors of hospital service quality. Decision Making: Applications in Management and Engineering, 1(1), 121-142.

Roy, J., Sharma, H., Kar, S., Kazimieras Zavadskas, E. & Saparauskas, J. (2019). An extended COPRAS model for multi-criteria decision-making problems and its application in web-based hotel evaluation and selection. Economic research – Ekonomska istraživanja, 32 (1), 219-253.

Sahu, P. K. (2006). Forecasting yield behavior of potato, mustard, rice, and wheat under irrigation. Journal of Vegetable Science, 12(1), 81–99.

Sharma, H. K., Kumari, K., & Kar, S. (2019). Short-term forecasting of air passengers based on hybrid rough set and double exponential smoothing models. Intelligent Automation and Soft Computing, 25(1), 1-13.

Sharma, H. K., Kumari, K., & Kar, S. (2020). A rough set approach for forecasting models. Decision Making: Applications in Management and Engineering, 3(1), 1-21.

Sharma, H. K., Kumari, K., & Kar, S. (2018). Air passengers forecasting for Australian airline based on hybrid rough set approach. Journal of Applied Mathematics, Statistics and Informatics, 14(1), 5-18.

Sharma, H. K., Jagannath, R., Kar, S., & Prentkovskis, O. (2018). Multi criteria evaluation framework for prioritizing indian railway stations using modified rough ahp-mabac method. Transport and Telecommunication, 19(2), 113-127.

Sugar and Molasses Production (2019). https://sugarcane.icar.gov.in/index.php/en/sugar-stats/sugarcane-statistics.

Suo, R., M. Huang, & Y. Liu. (2013). The application of combination forecasting method in total power of agriculture machinery based on RS, Advanced Materials Research, 601, 476 – 483.

Suresh, K. K., & S. R. Krishna Priya. (2011). Forecasting Sugarcane Yield of Tamilnadu Using ARIMA Models, Sugar Tech, 13(1), 23–26

Tang, J., Zhang, X., Yu, T., & Liu, F. (2021). Missing traffic data imputation considering approximate intervals: A hybrid structure integrating adaptive network-based inference and fuzzy rough set. In Press.

Vasiljević, M., Fazlollahtabar, H., Stević, Željko, & Vesković, S. (2018). A rough multicriteria approach for evaluation of the supplier criteria in automotive industry. Decision Making: Applications in Management and Engineering, 1(1), 82-96.

Wang, C. H. (2004). Predicting tourism demand using fuzzy time series and hybrid Grey theory. Tourism Management, 25 (3), 367-374.

Wang, Q., Li, S., Li, R., & Ma, M. (2018). Forecasting US shale gas monthly production using a hybrid ARIMA and metabolic nonlinear grey model. Energy, 160, 378-387.

Xiao, Z., K. Gong, & Y. Zoy. (2009). A combined forecasting approach based on fuzzy soft sets’’, Journal of Computational and Applied Mathematics, 228, 326-333.

Xu, S., Wangshu, S., Jianzhou, W., Yixin, Z. & Yining, G. (2016). Using a Grey-Markov model optimized by Cuckoo search algorithm to forecast the annual foreign tourist arrivals to China. Tourism Management, 52, 369-379.

Yuan, L., & Xu, F. (2013). Research on the multiple combination weight based on rough set and clustering analysis––the knowledge transfer risk in IT outsourcing taken as an example. Procedia Computer Science, 17, 274-281.

Zhou, J., & X. Zhang. (2013). Combined forcasting model based on the rough set to predict the Chinese Co2 emissions, Advanced materials Research, 773, 831– 836.

Žižović, M., & Pamucar, D. (2019). New model for determining criteria weights: Level Based Weight Assessment (LBWA) model. Decision Making: Applications in Management and Engineering, 2(2), 126-137.