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      Forecasting Monthly Export Price of Sugarcane in India Using Sarima Modelling

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          Abstract

          Sugarcane is the primary agricultural industry that sustains and promotes economic growth in India. In 2018, the majority of India's sugarcane production, specifically 79.9%, was allocated for the manufacturing of white sugar. A smaller portion, 11.29%, was used to produce jaggery, while 8.80% was utilized as seed and feed components. A total of 840.16 million metric tonnes of cane sugar was shipped in the year 2019. The primary objective of this research is to determine the most suitable forecasting model for predicting the monthly export price of sugarcane in India. The input consists of a time series with 240 monthly observations of the export price of sugarcane in India, spanning from January 1993 to December 2013. The SARIMA approach was employed to predict the monthly export price of sugarcane and it is concluded that the SARIMA (0, 1, 1), (0, 0, 0) 12 model is the best-fitted one by the expert modeler method. As a result, the fitted model appears to be adequate. The RMSE and MAPE statistics are used to analyze the precision of the model.

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          Solving of the Inverse Boundary Value Problem for the Heat Conduction Equation in Two Intervals of Time

          The boundary value problem, BVP, for the PDE heat equation is studied and explained in this article. The problem declaration comprises two intervals; the (0, T) is the first interval and labels the heating of the inside burning chamber, and the second (T, ∞) interval defines the normal cooling of the chamber wall when the chamber temperature concurs with the ambient temperature. It is necessary to prove the boundary function of this problem has its place in the space H10,∞ in order to successfully apply the Fourier transform method. The applicability of the Fourier transform for time to this problem is verified. The method of projection regularization is used to solve the inverse boundary value problem for the heat equation and to obtain an evaluation for the error between the approximate and the real solution. These results are new and of practical interest as shown in the numerical case study.
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            Improved Salp Swarm Optimization Algorithm for Damping Controller Design for Multimachine Power System

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              Predicting Seasonal Influenza Based on SARIMA Model, in Mainland China from 2005 to 2018

              Seasonal influenza is one of the mandatorily monitored infectious diseases, in China. Making full use of the influenza surveillance data helps to predict seasonal influenza. In this study, a seasonal autoregressive integrated moving average (SARIMA) model was used to predict the influenza changes by analyzing monthly data of influenza incidence from January 2005 to December 2018, in China. The inter-annual incidence rate fluctuated from 2.76 to 55.07 per 100,000 individuals. The SARIMA (1, 0, 0) × (0, 1, 1) 12 model predicted that the influenza incidence in 2018 was similar to that of previous years, and it fitted the seasonal fluctuation. The relative errors between actual values and predicted values fluctuated from 0.0010 to 0.0137, which indicated that the predicted values matched the actual values well. This study demonstrated that the SARIMA model could effectively make short-term predictions of seasonal influenza.
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                Author and article information

                Journal
                BIO Web of Conferences
                BIO Web Conf.
                EDP Sciences
                2117-4458
                2024
                April 05 2024
                2024
                : 97
                : 00142
                Article
                10.1051/bioconf/20249700142
                e8b95fc8-7908-4014-96e7-aff0857f0205
                © 2024

                https://creativecommons.org/licenses/by/4.0/

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