Fuzzy Time Series based method for Wheat production Forecasting

Kumar, Sachin; Kumar, Narendra

doi:10.5120/6313-8651

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…The research results were remarkably near to the actual annual production. The time series work almost perfectly if there is no such a sudden rise or fall in production (Kumar and Kumar, 2012).…”

Section: Introductionmentioning

confidence: 95%

“…Kumar used adaptive neuro fuzzy inference system (ANFIS) technique based on time series of 27 years to forecast rice yield in India (Kumar, 2011). The visual observation based on the graphical comparison between observed and predicted values and the qualitative performance assessment of the model indicates that ANFIS can be used effectively for crop yield forecasting (Kumar, 2011).Kumar and Kumar provided a number of modified techniques for time series based forecasting for the yield of any crop year (Kumar and Kumar, 2012). The study can contribute to the inventory management of wheat yield and management of storage space.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of the Efficiency of Classical and Fuzzy Regression Models for Crop Yield Forecasting With Climatological Aspect

Parviz¹,

Paymai²

2017

AgricultForest

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SUMMARYThis paper presents the application of fuzzy concepts in the field of crop yield forecasting. In this regard, classical and fuzzy, using symmetric and nonsymmetric triangular fuzzy number coefficients; regression models for wheat and oil seeds yield forecasting were used in Zanjan, West and East Azarbaijan Provinces (1984Provinces ( /2013. The predominance of various climatological parameters was determined using maximum correlation coefficient between climatological parameters and crop yield. The sensitivity analysis of climatological parameters indicated the diversity of climatological parameters in different Provinces. RRMSE criteria decreased 37.76% in symmetric fuzzy regression compare to classical and 15.6% in non-symmetric fuzzy compare to symmetric fuzzy regression. Based on error criteria, fuzzy regression has better performance in relation to the classical regression. There were not major differences between the performance of symmetric and non-symmetric fuzzy regression.

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Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Comparison of the Efficiency of Classical and Fuzzy Regression Models for Crop Yield Forecasting With Climatological Aspect

Parviz¹,

Paymai²

2017

AgricultForest

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“…Rice yield forecasting has been extensively examined using various methods all around the world. In order to forecast rice yield, Kumar and Kumar (2012) added fuzzy values to the time series [12]. Alam et al (2018) applied two hybrid approaches including ARIMAX-ANN and ARIMAX-SVM for estimating rice yield in India [13].…”

Section: Introductionmentioning

confidence: 99%

A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh

2023

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In this study, we attempt to anticipate annual rice production in Bangladesh (1961–2020) using both the Autoregressive Integrated Moving Average (ARIMA) and the eXtreme Gradient Boosting (XGBoost) methods and compare their respective performances. On the basis of the lowest Corrected Akaike Information Criteria (AICc) values, a significant ARIMA (0, 1, 1) model with drift was chosen based on the findings. The drift parameter value shows that the production of rice positively trends upward. Thus, the ARIMA (0, 1, 1) model with drift was found to be significant. On the other hand, the XGBoost model for time series data was developed by changing the tunning parameters frequently with the greatest result. The four prominent error measures, such as mean absolute error (MAE), mean percentage error (MPE), root mean square error (RMSE), and mean absolute percentage error (MAPE), were used to assess the predictive performance of each model. We found that the error measures of the XGBoost model in the test set were comparatively lower than those of the ARIMA model. Comparatively, the MAPE value of the test set of the XGBoost model (5.38%) was lower than that of the ARIMA model (7.23%), indicating that XGBoost performs better than ARIMA at predicting the annual rice production in Bangladesh. Hence, the XGBoost model performs better than the ARIMA model in predicting the annual rice production in Bangladesh. Therefore, based on the better performance, the study forecasted the annual rice production for the next 10 years using the XGBoost model. According to our predictions, the annual rice production in Bangladesh will vary from 57,850,318 tons in 2021 to 82,256,944 tons in 2030. The forecast indicated that the amount of rice produced annually in Bangladesh will increase in the years to come.

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“…The shape of the membership function was important in fuzzy time series model thus that triangular and trapezoidal membership with centroid defuzzification used by Basyigit et al, [2014] [1] . Kumar et al, [2010] [3] applied a fuzzy time series model to forecast wheat production. Kumar et al, [2019] [4] build a mathematical prediction model that depends on the high-order fuzzy logical relationship to reduce the average forecasting error of the existing fuzzy time series forecasting method and increased the accuracy of prediction value in agricultural production.…”

Section: Introductionmentioning

confidence: 99%

Chen’s model to understand the invariant fuzzy time series model through maize production in India

2023

Int. J. Stat. Appl. Math.

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The present study has applied a time-invariant fuzzy time series model for maize Production in India. Most of the time series data is forecasted using AR and ARIMA models but the present study considered the Chen fuzzy time series model to understand fuzzy time series and endeavour to forecast. Generally, fuzzy models are based on uncertainty, non-probabilistic and linguistic variables. The maize production of India during 1951-2020 data was divided into seven subsets. The subsets are equal intervals. Chen's model has used arithmetic operations for fuzzy logic relations and forecasting. The accuracy of the model was measured by Root Mean Square Error (1949.93) and Mean Absolute Percentage Error (25.86). The Chen model will give impetus to the higher-order fuzzy time series model.

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Fuzzy Time Series based method for Wheat production Forecasting

Cited by 6 publications

References 6 publications

Comparison of the Efficiency of Classical and Fuzzy Regression Models for Crop Yield Forecasting With Climatological Aspect

Comparison of the Efficiency of Classical and Fuzzy Regression Models for Crop Yield Forecasting With Climatological Aspect

A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh

Chen’s model to understand the invariant fuzzy time series model through maize production in India

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