scholarly journals Forecasting monthly farm tractor demand for India using MSARIMA and ARMAX models

2019 ◽  
Author(s):  
Alok Yadav ◽  
Sajal Ghosh
Keyword(s):  
Moving Average ◽  
Demand Forecasting ◽  
Raw Material ◽  
Autoregressive Moving Average ◽  
Average Model ◽  
Autoregressive Integrated Moving Average ◽  
Autoregressive Moving Average Model ◽  
Farm Tractor ◽  
The World ◽  
Moving Average Model

Because of long product development cycles, effective production planning of automobiles requires accurate demand forecasting in order to effectively managing resources and maximizing revenue. Errors in demand forecasts have often led to enormous costs and loss of revenue due to suboptimal utilization of resources. Since early 2000 India has been the largest manufacturer and consumer of farm tractors in the world. This paper develops multiplicative seasonal autoregressive integrated moving average (MSARIMA) and autoregressive moving average model with exogenous variable (ARMAX) to forecast monthly demand for farm tractor. The result indicates that ARMAX with real agriculture credit has found to be outperformed MSARIMA model in forecasting demand of farm tractors in the horizon of six months. The accurate monthly forecasting of farm tractor would help the manufacturers for better raw material, inventory and supply chain management. Keywords

scholarly journals Forecasting of salmonellosis epidemic proces in Ukraine using autoregressive integrated moving average model

2020 ◽  
pp. 346-354
Author(s):  
Serhii Bohdanov ◽  
Yulia Polyvianna ◽  
Tetyana Chumachenko ◽  
Dmytro Chumachenko
Keyword(s):  
Moving Average ◽  
Incidence Rates ◽  
Autoregressive Moving Average ◽  
Average Model ◽  
Autoregressive Integrated Moving Average ◽  
Autoregressive Moving Average Model ◽  
Moving Average Model ◽  
Complex Models ◽  
Optimal Values ◽  
Predicted Values

The article highlights the problem of salmonellosis among the population of the Kharkov region, Ukraine. Three time series were used for calculations: a series of incidence rates for men, a series of incidence rates for women and a series of incidence rates for the general population, each of the series was an ordered set of monthly values from December 2015 to December 2018. It was revealed that the most effective tool for analyzing these statistical data is the use of the autoregressive moving average model (ARIMA). The following steps were used: identification and replacement of outliers, the use of smoothing and decomposition of the series. The developed model allows you to explicitly indicate the order of the model using the arima () function or automatically generate a set of optimal values (p, d, q) using the auto.arima () function. The validated model allows to calculate the predicted values of the incidence of salmonellosis for 50 days. In certain cases, models of exponential smoothing are able to give forecasts that are not inferior in accuracy to forecasts obtained using more complex models.

scholarly journals Risk Prediction and Assessment: Duration, Infections, and Death Toll of the COVID-19 and Its Impact on China’s Economy

2020 ◽  
Vol 13 (4) ◽  
pp. 66 ◽  
Author(s):  
Xiao-Guang Yue ◽  
Xue-Feng Shao ◽  
Rita Yi Man Li ◽  
M. James C. Crabbe ◽  
Lili Mi ◽  
...  
Keyword(s):  
Inflection Point ◽  
Moving Average ◽  
Comparison Method ◽  
Autoregressive Moving Average ◽  
Average Model ◽  
Autoregressive Integrated Moving Average ◽  
Autoregressive Moving Average Model ◽  
Moving Average Model ◽  
The Impact ◽  
China’S Economy

This study first analyzes the national and global infection status of the Coronavirus Disease that emerged in 2019 (COVID-19). It then uses the trend comparison method to predict the inflection point and Key Point of the COVID-19 virus by comparison with the severe acute respiratory syndrome (SARS) graphs, followed by using the Autoregressive Integrated Moving Average model, Autoregressive Moving Average model, Seasonal Autoregressive Integrated Moving-Average with Exogenous Regressors, and Holt Winter’s Exponential Smoothing to predict infections, deaths, and GDP in China. Finally, it discusses and assesses the impact of these results. This study argues that even if the risks and impacts of the epidemic are significant, China’s economy will continue to maintain steady development.

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