Abstract
This study examines the comparison of fuzzy logic regression methodology and Autoregressive Integrated Moving Average (ARIMA) models to determine appropriate forecasting model for prediction of yield production of food crops. Due to environmental changes based on multifactor, uncertainty has increased in yield production of different crops around the globe. So, in these circumstances a novel forecasting method should be applied to get precise information before time. Now a days many robust methodologies are being used for forecasting purposes. Fuzzy logic and regression model is one of them, which is capable in conditions of uncertainty. In this study fuzzy time series forecasting model is applied along with traditional forecasting tool on gram production data of Pakistan to retrieve significant model for prediction. Initially, 7 fuzzy intervals are constructed using fuzzy logic computations with second and third-degree relationship and then evaluated the fuzzified values with different regression models. ARIMA model with different orders of p, d & q are formulated based on statistical accuracy measures such as autocorrelation function (ACF) and partial autocorrelation function (PACF). Applied the techniques of Akaike Information Criterion, Bayesian Information Criterion and other accuracy measures to select appropriate model for forecasting of gram production. Overall, models evaluation demonstrate that fuzzy logic and regression model perform well than ARIMA model in forecasting of gram production. The precise information about yield production will help the policy makers to take decision about import export, management, planning and other related issues.
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References
Rahman, N.M.F., Baten, M.A.: Forecasting area and production of black gram pulse in Bangladesh using ARIMA models. Pak. J. Agric. Sci. 53(4), 759–765 (2016)
Vishwajith, K., et al.: Time series modeling and forecasting of pulses production in India. J. Crop Weed 10(2), 147–154 (2014)
Lecerf, R., et al.: Assessing the information in crop model and meteorological indicators to forecast crop yield over Europe. Agric. Syst. 168, 191–202 (2019)
Arif, M., et al.: Band segmentation and detection of DNA by using fast fuzzy c-mean and neuro adaptive fuzzy inference system. In: International Conference on Smart City and Informatization. Springer (2019)
Arif, M., Wang, G., Peng, T.: Track me if you can? Query based dual location privacy in VANETs for V2V and V2I. In: 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). IEEE (2018)
Arif, M., et al.: SDN-based secure VANETs communication with fog computing. In: International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage. Springer (2018)
Bel, W., et al.: Parametric prediction model using expert system and fuzzy harmonic system. In: International Workshop Soft Computing Applications. Springer (2016)
Hassan, S.G., et al.: Fish as a source of acoustic signal measurement in an aquaculture tank: acoustic sensor based time frequency analysis. Int. J. Agric. Biol. Eng. 12(3), 110–117 (2019)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
Garg, B., Aggarwal, S., Sokhal, J.: Crop yield forecasting using fuzzy logic and regression model. Comput. Electr. Eng. 67, 383–403 (2018)
Box, G.E., Pierce, D.A.: Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. J. Am. Statis. Assoc. 65(332), 1509–1526 (1970)
Chen, S.-M., Hwang, J.-R.: Temperature prediction using fuzzy time series. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 30(2), 263–275 (2000)
Huarng, K.: Heuristic models of fuzzy time series for forecasting. Fuzzy Sets Syst. 123(3), 369–386 (2001)
Yu, H.-K.: Weighted fuzzy time series models for TAIEX forecasting. Phys. A 349(3–4), 609–624 (2005)
Singh, S.R.: A robust method of forecasting based on fuzzy time series. Appl. Math. Comput. 188(1), 472–484 (2007)
Singh, S.R.: A computational method of forecasting based on fuzzy time series. Math. Comput. Simul. 79(3), 539–554 (2008)
Ghosh, H., Chowdhury, S., Prajneshu: An improved fuzzy time-series method of forecasting based on L–R fuzzy sets and its application. J. Appl. Statis. 43(6), 1128–1139 (2016)
Iqbal, S., et al.: A new fuzzy time series forecasting method based on clustering and weighted average approach. J. Intell. Fuzzy Syst. 38, 6089–6098 (2020)
Tseng, F.-M., Tzeng, G.-H.: A fuzzy seasonal ARIMA model for forecasting. Fuzzy Sets Syst. 126(3), 367–376 (2002)
Acknowledgement
We would like to thank the referees and the journal editorial team for providing valuable advice that improved the quality of the original manuscript. This work is supported by National Nature Sciences Foundation of China (11671104).
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Iqbal, S., Zhang, C., Arif, M., Wang, Y., Dicu, A.M. (2021). A Comparative Study of Fuzzy Logic Regression and ARIMA Models for Prediction of Gram Production. In: Balas, V., Jain, L., Balas, M., Shahbazova, S. (eds) Soft Computing Applications. SOFA 2018. Advances in Intelligent Systems and Computing, vol 1222. Springer, Cham. https://doi.org/10.1007/978-3-030-52190-5_21
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DOI: https://doi.org/10.1007/978-3-030-52190-5_21
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