forecasting model
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Author(s):  
Zhao Zhen ◽  
Gang Qiu ◽  
Shengwei Mei ◽  
Fei Wang ◽  
Xuemin Zhang ◽  
...  

Author(s):  
Vian Ahmed ◽  
Sara Saboor ◽  
Ahmad Saad ◽  
Hasan Saleh ◽  
Nikita Kasianov ◽  
...  

2022 ◽  
Vol 24 (1) ◽  
Author(s):  
SANJU TAMANG ◽  
POLY SAHA

Collar rot caused by Sclerotium rolfsii Sacc. is one of the major biotic constraints of chickpea production worldwide. It is soil-borne fungi having wider host range and infection mainly occurs at the juvenile stage of crop growth resulting crop failure in no time. The pathogen is greatly influenced by soil temperature (ST) and soil moisture (SM) therefore, experiment formulated to develop a suitable forecasting model for its future use in computer simulation of plant disease prognostication by feeding only soil temperature and moisture data. The popular desi type chickpea variety Anuradha sown at different dates to get a range of soil temperature and soil moisture combination and its corresponding effect on disease incidence was recorded under natural epiphytotic conditions. The data obtained were analyzed using binary logistic regression and discriminant analysis to assess disease risk and non-risk period. The model developed was Y'= -73.9 + 1.251 SM + 0.017 ST. The outcome recorded, a unique statistically significant contribution of soil moisture (p value=0.029) on the establishment of the disease whereas, the effect of soil temperature was detected as statistically non-significant. The model developed and the correctness of the model determined to predict the disease severity with 80 % accuracy.


Econometrics ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 3
Author(s):  
Philip Hans Franses ◽  
Max Welz

We propose a simple and reproducible methodology to create a single equation forecasting model (SEFM) for low-frequency macroeconomic variables. Our methodology is illustrated by forecasting annual real GDP growth rates for 52 African countries, where the data are obtained from the World Bank and start in 1960. The models include lagged growth rates of other countries, as well as a cointegration relationship to capture potential common stochastic trends. With a few selection steps, our methodology quickly arrives at a reasonably small forecasting model per country. Compared with benchmark models, the single equation forecasting models seem to perform quite well.


2022 ◽  
pp. 1287-1300
Author(s):  
Balaji Prabhu B. V. ◽  
M. Dakshayini

Demand forecasting plays an important role in the field of agriculture, where a farmer can plan for the crop production according to the demand in future and make a profitable crop business. There exist a various statistical and machine learning methods for forecasting the demand, selecting the best forecasting model is desirable. In this work, a multiple linear regression (MLR) and an artificial neural network (ANN) model have been implemented for forecasting an optimum societal demand for various food crops that are commonly used in day to day life. The models are implemented using R toll, linear model and neuralnet packages for training and optimization of the MLR and ANN models. Then, the results obtained by the ANN were compared with the results obtained with MLR models. The results obtained indicated that the designed models are useful, reliable, and quite an effective tool for optimizing the effects of demand prediction in controlling the supply of food harvests to match the societal needs satisfactorily.


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