Forecasting of bse sensex using simple exponential smoothing (SES) method

2021 ◽  
Vol 11 (3) ◽  
pp. 656-665
Author(s):  
M. Ramesh ◽  
C. Mani ◽  
B. Hari Mallikarjuna Reddy ◽  
M. Venkataramanaiah
Keyword(s):  
Exponential Smoothing ◽  
Simple Exponential Smoothing

Analysis of Energy System in South Africa Using Exponential Smoothing Approach and Regression Technique

2016 ◽  
Vol 5 (3) ◽  
pp. 51-67
Author(s):  
Mohammad Mehdi Ghiasi ◽  
Alireza Aslani ◽  
Younes Noorollahi
Keyword(s):  
South Africa ◽  
Growth Rate ◽  
Energy Demand ◽  
Nuclear Energy ◽  
Fossil Fuels ◽  
Energy System ◽  
Primary Energy ◽  
Exponential Smoothing ◽  
Industrial Growth ◽  
Simple Exponential Smoothing

The energy demand has increased dramatically in the recent decades. Due to the limitations and environmental effects of fossil fuels, secure level of energy supply is vital for economic and social development. This work is to review the energy sector in South Africa. After that, the consumptions of coal, oil, natural gas, and nuclear energy are estimated by employing simple exponential smoothing methodology. Finding shows that the primary energy consumption in the South Africa is correlated as a function of population growth rate, industrial growth rate, and GDP.

scholarly journals Modeling and Short-Term Forecasts of Indicators for COVID-19 Outbreak in 25 Countries at the end of March

2020 ◽  
pp. 6-20
Author(s):  
Handan Ankaralı ◽  
Nadire Erarslan ◽  
Özge Pasin ◽  
Abu Kholdun Al Mahmood
Keyword(s):  
Time Series ◽  
Medical Science ◽  
Exponential Smoothing ◽  
Seasonal Effects ◽  
Time Series Models ◽  
Double Exponential ◽  
Wide Range ◽  
Simple Exponential Smoothing ◽  
Double Exponential Smoothing ◽  
Prediction And Forecasting

Objective: The coronavirus, which originated in Wuhan, causing the disease called COVID-19, spread more than 200 countries and continents end of the March. In this study, it was aimed to model the outbreak with different time series models and also predict the indicators. Materials and Methods: The data was collected from 25 countries which have different process at least 20 days. ARIMA(p,d,q), Simple Exponential Smoothing, Holt’s Two Parameter, Brown’s Double Exponential Smoothing Models were used. The prediction and forecasting values were obtained for the countries. Trends and seasonal effects were also evaluated. Results and Discussion: China has almost under control according to forecasting. The cumulative death prevalence in Italy and Spain will be the highest, followed by the Netherlands, France, England, China, Denmark, Belgium, Brazil and Sweden respectively as of the first week of April. The highest daily case prevalence was observed in Belgium, America, Canada, Poland, Ireland, Netherlands, France and Israel between 10% and 12%.The lowest rate was observed in China and South Korea. Turkey was one of the leading countries in terms of ranking these criteria. The prevalence of the new case and the recovered were higher in Spain than Italy. Conclusion: More accurate predictions for the future can be obtained using time series models with a wide range of data from different countries by modelling real time and retrospective data. Bangladesh Journal of Medical Science Vol.19(0) 2020 p.06-20

Hurst Exponent with ARIMA and Simple Exponential Smoothing for Measuring Persistency of M3- Competition Series

2019 ◽  
Vol 17 (05) ◽  
pp. 815-822
Author(s):  
Mirna Patricia Ponce Flores ◽  
Juan Frausto Solis ◽  
Guadalupe Castilla Valdez ◽  
Juan Javier Gonzalez Barbosa ◽  
Joaquin Perez Ortega ◽  
...  
Keyword(s):  
Hurst Exponent ◽  
Exponential Smoothing ◽  
Simple Exponential Smoothing

scholarly journals Forecasting With Exponential Smoothing Whats The Right Smoothing Constant?

2013 ◽  
Vol 17 (3) ◽  
pp. 117-126 ◽  
Author(s):  
Handanhal V. Ravinder
Keyword(s):  
Time Series ◽  
Linear Trend ◽  
Exponential Smoothing ◽  
Forecasting Model ◽  
Error Measures ◽  
Nonlinear Trend ◽  
Simple Exponential Smoothing ◽  
The Right ◽  
Numerous Time

This paper examines exponential smoothing constants that minimize summary error measures associated with a large number of forecasts. These forecasts were made on numerous time series generated through simulation on a spreadsheet. The series varied in length and underlying nature no trend, linear trend, and nonlinear trend. Forecasts were made using simple exponential smoothing as well as exponential smoothing with trend correction and with different kinds of initial forecasts. We found that when initial forecasts were good and the nature of the underlying data did not change, smoothing constants were typically very small. Conversely, large smoothing constants indicated a change in the nature of the underlying data or the use of an inappropriate forecasting model. These results reduce the confusion about the role and right size of these constants and offer clear recommendations on how they should be discussed in classroom settings.

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