scholarly journals Identifying the Most Effective Model for Understanding the Growth Rate of Government e-Transactions: Brown’s Model of Exponential Smoothing

2018 ◽  
Vol 7 (2) ◽  
pp. 81-86
Author(s):  
Abhishek Roy ◽  
Gautam Dutta ◽  
Prabir Kumar Das
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Exponential Smoothing ◽  
The State ◽  
Current Status ◽  
Time Series Models ◽  
Adoption Rate ◽  
Electronic Transactions ◽  
User Friendly ◽  
Adoption Pattern

The purpose of this study is to investigate the current status of e-transactions time series growth rate in the state of Jharkhand from January 2013 to 20th May, 2018 to understand the citizen adoption pattern of various e-government services. Government spends lots of money in developing and implementing e-services, but despite spending huge money the adoption rate is low. This study will analyze the adoption rate by using time series models and find the adoption pattern to improve the future adoption rate of e-services. Besides this, the paper will help us to understand whether current e-transaction methods are user friendly or not. Therefore, identify the best model to evaluate the growth rate of e-transactions in the context of government electronic transactions. In this regard, various existing time series models have been evaluated to obtain the result of this study. The paper draws-up the emergent model derived from the analysis. Finally, a framework is suggested to select Brown’s Exponential Smoothing Model as an ideal model for evaluating the growth rate of government e-transaction for the state of Jharkhand. This will help government to recommend and strategize better e-services plan for the state. This research can be used in policy making, strategizing and finding the key to user acceptance of innovation in technology in government electronic transaction with the help of Brown’s Exponential Smoothing as it can reduce the impacts of seasonal factors.

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

scholarly journals Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia

2021 ◽  
Vol 18 (16) ◽  
pp. 8660
Author(s):  
Isra Al-Turaiki ◽  
Fahad Almutlaq ◽  
Hend Alrasheed ◽  
Norah Alballa
Keyword(s):  
Time Series ◽  
Saudi Arabia ◽  
Time Series Data ◽  
Arima Model ◽  
Empirical Evaluation ◽  
Cubic Spline ◽  
Exponential Smoothing ◽  
Series Data ◽  
Time Series Models ◽  
Forecasting Models

COVID-19 is a disease-causing coronavirus strain that emerged in December 2019 that led to an ongoing global pandemic. The ability to anticipate the pandemic’s path is critical. This is important in order to determine how to combat and track its spread. COVID-19 data is an example of time-series data where several methods can be applied for forecasting. Although various time-series forecasting models are available, it is difficult to draw broad theoretical conclusions regarding their relative merits. This paper presents an empirical evaluation of several time-series models for forecasting COVID-19 cases, recoveries, and deaths in Saudi Arabia. In particular, seven forecasting models were trained using autoregressive integrated moving average, TBATS, exponential smoothing, cubic spline, simple exponential smoothing Holt, and HoltWinters. The models were built using publicly available daily data of COVID-19 during the period of 24 March 2020 to 5 April 2021 reported in Saudi Arabia. The experimental results indicate that the ARIMA model had a smaller prediction error in forecasting confirmed cases, which is consistent with results reported in the literature, while cubic spline showed better predictions for recoveries and deaths. As more data become available, a fluctuation in the forecasting-accuracy metrics was observed, possibly due to abrupt changes in the data.

scholarly journals Demand For Electricity In Lebanon

2011 ◽  
Vol 8 (1) ◽  
Author(s):  
Salah Abosedra ◽  
Abdallah Dah ◽  
Sajal Ghosh
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Time Span ◽  
Time Series Models ◽  
Detailed Data ◽  
Economic Variables ◽  
Univariate Time Series ◽  
Modeling Techniques ◽  
Socio Economic Variables ◽  
Future Demand

This paper estimates the demand for electricity in Lebanon by employing three modeling techniques namely OLS, ARIMA and exponential smoothing for the time span January 1995 to December 2005. In- sample forecasts reveal that the forecasts made by ARIMA (0,1,3) (1,0,0)12 is superior in terms of lowest RMSE, MSE and MAPE criteria, followed by exponential smoothing and OLS. Therefore, the planners in Lebanon could utilize linear univariate time-series models for forecasting future demand of electricity until detailed data on various socio-economic variables are available, which, in the future, may result in other modeling techniques being superior to estimate the demand for electricity in the country.

scholarly journals ECONOMETRIC ANALYSIS OF THE VOLUME OF MUTUAL TRADE OF THE EAEU MEMBER STATES

2021 ◽  
Author(s):  
Mariya Tsvil ◽  
Ella Guleva ◽  
Margarita Zubkova
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Multiplicative Model ◽  
Econometric Analysis ◽  
Time Series Models ◽  
Member States ◽  
Quarterly Data ◽  
Mutual Trade

The article provides econometric time series models for the volumes of mutual trade of the EAEU member states based on quarterly data from the 1st quarter of 2017 to the 3rd quarter of 2021. An exponential smoothing model and a multiplicative model are built. Also, a forecast was made for the volume of mutual trade in the IV quarter of 2021

scholarly journals Modeling and Forecasting of COVID-19 New Cases in the Top 10 Infected African Countries Using Regression and Time Series Models

2020 ◽  
Author(s):  
Alemayehu Argawu
Keyword(s):  
South Africa ◽  
Time Series ◽  
Regression Models ◽  
Study Finding ◽  
Exponential Smoothing ◽  
Time Series Models ◽  
African Countries ◽  
Mass Gatherings ◽  
Preparedness Planning ◽  
Best Fit

Background: COVID-19 total cases have reached 1,083,071 (83.5%) in the top 10 infected African countries (South Africa, Egypt, Morocco, Ethiopia, Nigeria, Algeria, Ghana, Kenya, Cameroon, and Cote-dIvoire) from Feb 14 to Sep 6, 2020. Then, this study aimed to model and forecast of COVID-19 new cases in these top 10 infected African countries. Methods: In this study, the COVID 19 new cases data have been modeled and forecasted using curve estimation regression and time series models for these top 10 infected African countries from Feb 14 to Sep 6, 2020. Results: From July to August, the prevalence of COVID-19 cumulative cases was declined in South Africa, Cote dʹIvoire, Egypt, Ghana, Cameron, Nigeria, and Algeria by 31%, 26%, 22%, 20%, 14%, 12%, and 4%, respectively. But, it was highly raised in Ethiopia and Morocco by 41%, and 38% in this period, respectively. In Kenya, it was raised only by 1%. In this study, the cubic regression models for the ln(COVID-19 new cases) data were relatively the best fit for Egypt, Ethiopia, Kenya, Morocco, Nigeria and South Africa. And, the quadratic regression models for the data were the best fit for Cameroon, Cote-dIvoire and Ghana. The Algeria data was followed the logarithmic regression model. In the time series analysis, the Algeria, Egypt, and South Africa COVID-19 new cases data have fitted the ARIMA (0,1,0), ARIMA (0,1,0), and ARIMA (0,1,14) models, respectively. The Cameroon, Cote-dIvoire, Ghana, and Nigeria data have fitted the simple exponential smoothing models. The Ethiopia, Kenya, and Morocco data have followed the Damped trend, Holt, and Brown exponential smoothing models, respectively. In the analysis, the trends of COVID-19 new cases will be declined for Algeria and Ethiopia, and the trends will be constantan for Cameroon, Cote-dIvoire, Ghana and Nigeria. But, it will be raised slightly for Egypt and Kenya, and significantly for Morocco and South Africa from September 7 to October 6, 2020. Conclusion: This study was conducted with the current measures; the forecasts and trends obtained may differ from the number of cases that occur in the future. Thus, the study finding should be useful in preparedness planning against further spread of the COVID-19 epidemic in African countries. And, the researcher recommended that as many countries continue to relax restrictions on movement and mass gatherings, and more are opening their airspaces, and the countries other public and private sectors are reopening. So, strong appropriate public health and social measures must be instituted on the grounds again.

scholarly journals Modelling and forecasting of potato sales prices in Ukraine

2021 ◽  
Vol 7 (4) ◽  
pp. 160-179
Author(s):  
Inna Koblianska ◽  
Larysa Kalachevska ◽  
Stanisław Minta ◽  
Nataliia Strochenko ◽  
Svitlana Lukash
Keyword(s):  
Time Series ◽  
State Space ◽  
Security Policy ◽  
Exponential Smoothing ◽  
Food Prices ◽  
Information Criteria ◽  
The State ◽  
Agricultural Enterprises ◽  
Use Of Resources ◽  
Modelling And Forecasting

Purpose. Under the background of the climate change and other crises, the world food system is becoming increasingly vulnerable to price fluctuations. This highlights the need to consider and better manage the risks associated with price volatility in accordance with the principles of a market economy and simultaneously protecting the most vulnerable groups of population. Responding to these challenges, in this study we aim to determine the main parameters of time series of potato sales prices in agricultural enterprises in Ukraine, to build an appropriate model, and to form a short-term (one-year) forecast. Methodology / approach. We used in the research the data from the State Statistics Service of Ukraine on average monthly sales prices of potatoes in agricultural enterprises from December 2012 to July 2021 (104 observations) adjusted for the price index of crop products sold by enterprises for the month (with December 2012 base period). Decomposition was used to determine the characteristics of the time series; exponential smoothing methods (Holt-Winters and State Space Framework – ETS) and autoregressive-moving average were used to find the model that fits the actual data the best and has high prognostic quality. We applied the Rstudio forecast package to model and to forecast the time series. Results. The time series of potato sales prices in enterprises is characterized by seasonality (mainly related to seasonal production) with the lowest prices in November, and the highest – in June; although, other periods of price growth were identified during the year: in January and April. The ARMA (2, 2) (1,0)12 with a non-zero mean was found to be the best model for forecasting potatoes sales prices. ARMA (2, 2) (1,0)12, compared to the state-space exponential smoothing model with additive errors – ETS (A), better fits the observed data and provides more accurate forecasting model (with lower errors). Forecast made with ARMA (2, 2) (1,0)12 shows that potato sale prices in agricultural enterprises in November 2021 (months with the lowest price) will range from 2154.76 UAH/t to 7414.57 UAH/t, in June 2022 – from 3016.72 UAH/t to 14051.63 UAH/t (prices of July 2021) with a probability of 95%. The forecast’s mean absolute percentage error is 14.87%. Originality / scientific novelty. This research deepens the methodological basis for food prices modelling and forecasting, thus contributing to the agricultural economics science development. The obtained results confirm the previous research findings on the better quality of food prices forecasts made with autoregressive models (for univariate time series) compared with exponential smoothing. Additionally, the study reveals advantages of the state space framework for exponential smoothing (ETS) compared to Holt-Winters methods in case of time series with seasonality: although the ETS model overlaps with the observed (train) data, it is better in terms of information criteria and forecasting (for the test data). Practical value / implications. The obtained results can serve as an information basis for decision-making on potato production and sales by producers, on more efficient use of resources by the population, on more effective measures to support industrial potato growing, to implement social programs and food security policy by the government.

scholarly journals TRACKING THE MUTANT: FORECASTING AND NOWCASTING COVID-19 IN THE UK IN 2021

2021 ◽  
Vol 256 ◽  
pp. 110-126
Author(s):  
Andrew Harvey ◽  
Paul Kattuman ◽  
Craig Thamotheram
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Reproduction Number ◽  
Time Series Models ◽  
Daily Number ◽  
New Class ◽  
Bivariate Models ◽  
The Uk ◽  
Different Sources

A new class of time series models is used to track the progress of the COVID-19 epidemic in the UK in early 2021. Models are fitted to England and the regions, as well as to the UK as a whole. The growth rate of the daily number of cases and the instantaneous reproduction number are computed regularly and compared with those produced by SAGE. The results from figures published each day are compared with results based on figures by specimen date, which may be more accurate but are subject to substantial revisions. It is then shown how data from the two different sources can be combined in bivariate models.

scholarly journals Variability and Sources of Output Growth in Major Oilseeds of Rajasthan

2021 ◽  
Vol 66 (1) ◽  
Author(s):  
Kailash Chand Bairwa
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Time Series Data ◽  
Output Growth ◽  
The State ◽  
Series Data ◽  
Oilseed Crops ◽  
Significant Growth ◽  
Area Effect ◽  
Relative Contribution

Rajasthan state is the second largest oilseeds producer and land coverage in the country. The share of oilseed crops is scheduled the significant growth in area and output in latest 20 years. Nevertheless, compare to wheat and gram, the growth rate of area and production of several oilseeds is less significant and there exist wide instability in their productivity in scattered part of the state. This study investigates to growth, its contributors and variability in area, production and productivity of major oilseed crops. The study period from 1990-91 to 2019-20 was divided into three sub-periods viz., period-I (1990-91 to 2004-05); period-II (2005-06 to 2019-20) and Overall study Period (1990-91 to 2018-19). Time series data were collected from various public E-sources to compute the growth, instability and decomposition in oilseeds production. It was revealed from the analysis that growth of kharif oilseeds was higher than rabi oilseeds. The highest instability (31.78) in production and productivity was reported in period-I for kharif oilseeds. In case of relative contribution, the area effect (416.85) and yield effects (211.10) were more effective in production of taramira and sesame crops, respectively. This analysis suggested that during period –I and II area effect was dominant in changing output of taramira and rapeseed-mustard.

scholarly journals Forecasting Wheat Production Using Time Series Models in Pakistan

2019 ◽  
Vol 8 (2) ◽  
pp. 172-177
Author(s):  
M Asif Masood ◽  
Irum Raza ◽  
Saleem Abid
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Series Data ◽  
Time Series Models ◽  
Linear Quadratic ◽  
Wheat Production ◽  
S Curve ◽  
Double Exponential Smoothing

The present paper was designed to forecast wheat production for 2017-18, 2018-19 and 2019-2020 respectively by using time series data from 1971-72 to 2016-17 with best selected time series models. Linear, Quadratic, Exponential, S-Curve, Double Exponential Smoothing, Single exponential smoothing, Moving average and ARIMA were estimated for wheat production. The results showed a mix trend in production of wheat for selected time period. ARIMA (2,1,2) was found best one keeping in view close forecasts with actual reported wheat production. So the preference inclined towards the ARIMA (2,1,2) than quadratic to forecasts of wheat production.

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

2020 ◽  
Author(s):  
Handan Ankaralı ◽  
Nadire Erarslan ◽  
Özge Pasin
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Seasonal Effects ◽  
Time Series Models ◽  
Double Exponential ◽  
Wide Range ◽  
Simple Exponential Smoothing ◽  
Double Exponential Smoothing ◽  
Prediction And Forecasting ◽  
Made In

ABSTRACTBackgroundThe coronavirus, which originated in Wuhan, causing the disease called COVID-19, spread more than 200 countries and continents end of the March. There is a lot of data since the virus started. However, these data will be explanatory when accurate analyzes are made and will allow future predictions to be made. In this study, it was aimed to model the outbreak with different time series models and also predict the indicators.MethodsThe 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.ResultsChina 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.ConclusionsMore 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.

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