Study on ARIMA Modelling to Forecast Area and Production of Kharif Rice in West Bengal

Biswas, Ria

doi:10.9734/bpi/cras/v12/10700d

Cited by 5 publications

(5 citation statements)

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“…The parameters were estimated using the Akaike information criterion (AIC) and Bayesian information criterion (BIC) values. A model with the lowest AIC, BIC, and Q-statistics, as well as a high R-square, could be considered suitable for predicting [15]. The model is regarded as unacceptable in an application if the computed p-value associated with the Q-Statistics is modest (p-value) [14].…”

Section: Data and Descriptive Statisticsmentioning

confidence: 99%

Hybrid Neural Network Prediction for Time Series Analysis of COVID-19 Cases in Nigeria

Adedotun¹

2022

JIMD

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The lethal coronavirus illness (COVID-19) has evoked worldwide discussion. This contagious, sometimes fatal illness, is caused by the severe acute respiratory syndrome coronavirus 2. So far, COVID-19 has quickly spread to other countries, sickening millions across the globe. To predict the future occurrences of the disease, it is important to develop mathematical models with the fewest errors. In this study, classification and regression tree (CART) models and autoregressive integrated moving averages (ARIMAs) are employed to model and forecast the one-month confirmed COVID-19 cases in Nigeria, using the data on daily confirmed cases. To validate the predictions, these models were compared through data tests. The test results show that the CART regression model outperformed the ARIMA model in terms of accuracy, leading to a fast growth in the number of confirmed COVID-19 cases. The research findings help governments to make proper decisions on how the prepare for the outbreak. Besides, our analysis reveals the lack of quarantine wards in Nigeria, in addition to the insufficiency of medications, medical staff, lockdown decisions, volunteer training, and economic preparation.

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Section: Data and Descriptive Statisticsmentioning

confidence: 99%

Hybrid Neural Network Prediction for Time Series Analysis of COVID-19 Cases in Nigeria

Adedotun¹

2022

JIMD

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“…where Y denotes the sequence after difference operation. Then, p and q are determined by the Akaike information criterion (AIC) [39] or observed from the auto-correlation and partial auto-correlation.Finally, the ARIMA verifies whether its residual error is white noise.…”

Section: Arima Modelmentioning

confidence: 99%

A Hybrid ARIMA-GABP Model for Predicting Sea Surface Temperature

Chen

Zeng

et al. 2022

Electronics

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Sea surface temperature (SST) is one of the most important parameters in air–sea interaction, and its accurate prediction is of great significance in the study of global climate change. However, SST is affected by heat flux, ocean dynamic processes, cloud coverage, and other factors, which means it contains linear and nonlinear components. Existing prediction models, especially single prediction models, cannot effectively handle these linear and nonlinear components in the meantime, degrading their accuracy concerning the prediction of SST. To remedy this weakness, this paper proposes a novel prediction model by the Lagrange multiplier method to combine the auto-regressive integrated moving average (ARIMA) model and the back propagation (BP) neural network model, where these two models have superior prediction performance for linear and nonlinear components, respectively. Moreover, the genetic algorithm is exploited to construct the genetic algorithm BP (GABP) neural network to further improve the performance of the proposed model. To verify the effectiveness of the proposed model, experiments predicting the SST based on historic time-series data are performed. The experiment results indicate that the mean absolute error (MAE) of the ARIMA-GABP model is only 0.3033 °C and the root mean square error (RMSE) is 0.3970 °C, which is better than the ARIMA model, BP neural network model, long short-term memory (LSTM) model, GABP neural network model, and ensemble empirical model decomposition BP model among various datasets. Therefore, the proposed model has superior and robust performance concerning predicting SST.

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“…The forecast suggested 23430.3 hectogram / hectare yield increase in 2020 compared to the 2016 figure of 19624 hectogram / hectare. [ 20 ] used the ARIMA model to forecast Kharif rice production in West Bengal, India which contributes about 15% of the total paddy in India. [ 21 ] used the ARIMA model to forecast sorghum production in South Africa from 2017 to 2020.…”

Section: Introductionmentioning

confidence: 99%

Time series and power law analysis of crop yield in some east African countries

2023

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We carry out a time series analysis on the yearly crop yield data in six east African countries (Burundi, Kenya, Somalia, Tanzania, Uganda and Rwanda) using the autoregressive integrated moving average (ARIMA) model. We describe the upper tail of the yearly crop yield data in those countries using the power law, lognormal, Fréchet and stretched exponential distributions. The forecast of the fitted ARIMA models suggests that the majority of the crops in different countries will experience neither an increase nor a decrease in yield from 2019 to 2028. A few exceptional cases correspond to significant increase in the yield of sorghum and coffee in Burundi and Rwanda, respectively, and significant decrease in the yield of beans in Burundi, Kenya and Rwanda. Based on Vuong’s similarity test p–value, we find that the power law distribution captured the upper tails of yield distribution better than other distributions with just one exceptional case in Uganda, suggesting that these crops have the tendency for producing high yield. We find that only sugar cane in Somalia and sweet potato in Tanzania have the potential of producing extremely high yield. We describe the yield behaviour of these two crops as black swan, where the “rich getting richer” or the “preferential attachment” could be the underlying generating process. Other crops in Burundi, Kenya, Somalia, Tanzania, Uganda and Rwanda can only produce high but not extremely high yields. Various climate adaptation/smart strategies (use of short-duration pigeon pea varieties, use of cassava mosaic disease resistant cassava varieties, use of improved maize varieties, intensive manuring with a combination of green and poultry manure, early planting, etc) that could be adapted to increase yields in east Africa are suggested. The paper could be useful for future agricultural planning and rates calibration in crop risk insurance.

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Study on ARIMA Modelling to Forecast Area and Production of Kharif Rice in West Bengal

Cited by 5 publications

References 0 publications

Hybrid Neural Network Prediction for Time Series Analysis of COVID-19 Cases in Nigeria

Hybrid Neural Network Prediction for Time Series Analysis of COVID-19 Cases in Nigeria

A Hybrid ARIMA-GABP Model for Predicting Sea Surface Temperature

Time series and power law analysis of crop yield in some east African countries

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