Forecasting maize yield using ARIMA-Genetic Algorithm approach

Rathod, Santosha; Singh, K. N.; Arya, Prawin; Ray, Mrinmoy; Mukherjee, Anirban; Sinha, Kishore; Kumar, Prakash P.; Shekhawat, Ravindra Singh

doi:10.1177/0030727017744933

Cited by 16 publications

(15 citation statements)

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“…To overcome the modelling of above-mentioned mixture of linear and nonlinear pattern in spatiotemporal time series data, the two stage STARMA model was developed to capture the nonlinear spatiotemporal pattern in rice yield data. Similar modelling approaches were developed by the combination of two different models in different agricultural commodities like; maize yield [19], mango and banana yield prediction [29,31], coffee yield prediction [30] and rice yield prediction [32,33].…”

Section: Discussionmentioning

confidence: 99%

“…Due to computational difficulties and availabilities of secondary data on spatiotemporal phenomenon, the spatiotemporal time series modelling was a less exploited area of research. In univariate time series modelling, autocorrelation between the successive observations over a period is characterized and to model these kinds of series, the Box-Jenkins Autoregressive Moving Average (ARIMA) [18] model is the most commonly used model [19]. On other hand, spatially auto-correlated time series information can be modelled using STARMA model.…”

Section: Introductionmentioning

confidence: 99%

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Two-Stage Spatiotemporal Time Series Modelling Approach for Rice Yield Prediction & Advanced Agroecosystem Management

et al. 2021

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A robust forecast of rice yields is of great importance for medium-to-long-term planning and decision-making in cereal production, from regional to national level. Incorporation of spatially correlated adjacent effects in forecasting models in general, results in accurate forecast. The Space Time Autoregressive Moving Average (STARMA) is the most popular class of model in linear spatiotemporal time series modelling. However, STARMA cannot process nonlinear spatiotemporal relationships in datasets. Alternately, Time Delay Neural Network (TDNN) is a most popular machine learning algorithm to model the nonlinear pattern in data. To overcome these limitations, two-stage STARMA approach was developed to predict rice yield in some of the most intensive national rice agroecosystems in India. The Mean Absolute Percentage Errors value of proposed STARMA-II approach is lower compared to Autoregressive Moving Average (ARIMA) and STARMA model in all examined districts, while the Diebold-Mariano test confirmed that STARMA-II model is significantly different from classical approaches. The proposed STARMA-II approach is promising alternative to classical linear and nonlinear spatiotemporal time series models for estimating mixed linear and nonlinear patterns and can be advanced tool for mid-to-long-term sustainable planning and management of crop yields and patterns in agroecosystems, i.e., food supply and demand from local to regional levels.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Two-Stage Spatiotemporal Time Series Modelling Approach for Rice Yield Prediction & Advanced Agroecosystem Management

et al. 2021

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“…Correlogram adalah metode yang digunakan dalam penentuan koefisien model SARIMA dengan melihat plot dari ACF dan PACF (Yani, 2018). Pengecekan ACF dan PACF dilakukan untuk membangun model SARIMA dengan melihat lag yang telah melewati batas garis pada plotnya (Rathod et al, 2017). Data yang menggunakan metode SARIMA terbagi menjadi dua tipe, yakni data non-musiman dan musiman.…”

Section: Identifikasi Model Dengan Correlogramunclassified

Prediksi Produksi Bawang Merah Di Kabupaten Nganjuk Dengan Metode Seasonal Arima (Sarima)

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et al. 2022

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Produksi bawang merah merupakan komoditas holtikultura yang dikembangkan secara nasional dengan pembinaan yang intensif. Faktor utama yang mempengaruhi produksi bawang merah adalah varietas benih, lahan dan cuaca. Penelitian ini bertujuan untuk memprediksi produksi bawang merah agar komoditas bawang merah dapat menjaga kestabilan harga dan ketersediaan barang di kabupaten Nganjuk. Data pada penelitian ini bersumber dari BPS kabupaten Nganjuk yang digunakan dalam membangun model terbaik dengan metode SARIMA untuk memprediksi produksi bawang merah periode 2021-2023. Berdasarkan hasil analisis yang dilakukan, model terbaik adalah model SARIMA (3,0,2)(2,1,2)12 yang memiliki nilai MAPE sebesar 2,01%.

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“…In many studies, ARIMA models have been successfully applied to forecast the time series of various consumptions and requirements [8], including the production and exports of different agricultural commodities [9][10][11][12][13]. In addition, an ARIMA genetic algorithm was recently employed to estimate maize yield [14] and oilseed production [15] in India's agroecosystems.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Forecasting of Rice Prices in India during the COVID-19 Lockdown Using Machine Learning Approaches

et al. 2022

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Via national lockdowns, the COVID-19 pandemic disrupted the production and distribution of foodstuffs worldwide, including rice (Oryza sativa L.) production, affecting the prices in India’s agroecosystems and markets. The present study was performed to assess the impact of the COVID-19 national lockdown on rice prices in India, and to develop statistical machine learning models to forecast price changes under similar crisis scenarios. To estimate the rice prices under COVID-19, the general time series models, such as the autoregressive integrated moving average (ARIMA) model, the artificial neural network (ANN) model, and the extreme learning machine (ELM) model, were applied. The results obtained using the ARIMA intervention model revealed that during the COVID-19 lockdown in India, rice prices increased by INR 0.92/kg. In addition, the ELM intervention model was faster, with less computation time, and provided better results vs other models because it detects the nonlinear time series data, along with the intervention variable, which was considered an exogenous variable. The use of forecasting models can be a useful tool in supporting decision makers, especially under unpredictable crises. The study results are of great importance for the national agri-food sector, as they can bolster authorities and policymakers in planning and designing more sustainable interventions in the food market during (inter)national crisis situations.

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Forecasting maize yield using ARIMA-Genetic Algorithm approach

Cited by 16 publications

References 11 publications

Two-Stage Spatiotemporal Time Series Modelling Approach for Rice Yield Prediction & Advanced Agroecosystem Management

Two-Stage Spatiotemporal Time Series Modelling Approach for Rice Yield Prediction & Advanced Agroecosystem Management

Prediksi Produksi Bawang Merah Di Kabupaten Nganjuk Dengan Metode Seasonal Arima (Sarima)

Modeling and Forecasting of Rice Prices in India during the COVID-19 Lockdown Using Machine Learning Approaches

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