Comparison Study on Exponential Smoothing and ARIMA Model for the Fuel Price

2018 ◽  
pp. 33-41
Author(s):  
Sheik Abdullah Abdul Azees ◽  
Ramraj Sasikumar
Keyword(s):  
Arima Model ◽  
Exponential Smoothing ◽  
Comparison Study ◽  
Fuel Price

scholarly journals MATHEMATICAL MODELLING AS AN ELEMENT OF PLANNING RAIL TRANSPORT STRATEGIES

Transport ◽  
2021 ◽  
Vol 36 (4) ◽  
pp. 354-363
Author(s):  
Anna Borucka ◽  
Dariusz Mazurkiewicz ◽  
Eliza Łagowska
Keyword(s):  
Time Series ◽  
Development Strategy ◽  
Time Series Data ◽  
Arima Model ◽  
Exponential Smoothing ◽  
Rail Transport ◽  
Transport Infrastructure ◽  
Rolling Stock ◽  
Series Data ◽  
Passenger Transport

Effective planning and optimization of rail transport operations depends on effective and reliable forecasting of demand. The results of transport performance forecasts usually differ from measured values because the mathematical models used are inadequate. In response to this applicative need, we report the results of a study whose goal was to develop, on the basis of historical data, an effective mathematical model of rail passenger transport performance that would allow to make reliable forecasts of future demand for this service. Several models dedicated to this type of empirical data were proposed and selection criteria were established. The models used in the study are: the seasonal naive model, the Exponential Smoothing (ETS) model, the exponential smoothing state space model with Box–Cox transformation, ARMA errors, trigonometric trend and seasonal components (TBATS) model, and the AutoRegressive Integrated Moving Average (ARIMA) model. The proposed time series identification and forecasting methods are dedicated to the processing of time series data with trend and seasonality. Then, the best model was identified and its accuracy and effectiveness were assessed. It was noticed that investigated time series is characterized by strong seasonality and an upward trend. This information is important for planning a development strategy for rail passenger transport, because it shows that additional investments and engagement in the development of both transport infrastructure and superstructure are required to meet the existing demand. Finally, a forecast of transport performance in sequential periods of time was presented. Such forecast may significantly improve the system of scheduling train journeys and determining the level of demand for rolling stock depending on the season and the annual rise in passenger numbers, increasing the effectiveness of management of rail transport.

Forecasting Petroleum Fuel Price in Malaysia by ARIMA Model

2021 ◽  
pp. 671-678
Author(s):  
Rajalingam Sokkalingam ◽  
Richard M. N. Y. Sarpong-Streetor ◽  
Mahmod Othman ◽  
Hanita Daud ◽  
Derrick Asamoah Owusu
Keyword(s):  
Arima Model ◽  
Fuel Price ◽  
Petroleum Fuel

Optimization of pumping schedule based on water demand forecasting using a combined model of autoregressive integrated moving average and exponential smoothing

2014 ◽  
Vol 15 (1) ◽  
pp. 188-195 ◽  
Author(s):  
Hyeong-Seok Kang ◽  
Hyunook Kim ◽  
Jaekyeong Lee ◽  
Ingyu Lee ◽  
Byoung-Youn Kwak ◽  
...  
Keyword(s):  
Water Supply ◽  
Moving Average ◽  
Arima Model ◽  
Exponential Smoothing ◽  
Water Supply Systems ◽  
Combined Model ◽  
Autoregressive Integrated Moving Average ◽  
Electricity Cost ◽  
Water Demands ◽  
Water Pumping

Stable water supply to end users is the most important element in water supply systems (WSSs). The portion of energy used by the water distribution system is up to 40% of the total energy consumed by WSSs. To save energy cost for pumping systems, a number of attempts have been made. Especially, an optimization scheme for scheduling the water-pumping operation has attracted the interest of water engineers. In this paper, a binary integer program was applied to optimize pumping schedule of a WSS in Polonnaruwa, Sri Lanka based on the hourly water demands for the next day. The water demands were forecasted by a combined model consisting of an autoregressive integrated moving average (ARIMA) model and an error compensation routine based on exponential smoothing technique. The result showed that the optimization system could reduce the operation cost of the WSS by minimizing electricity for water pumping; electricity cost for pump operation could be reduced by 55%.

scholarly journals Forecasting Techniques for Sales Prediction

2020 ◽  
Vol 8 (5) ◽  
pp. 4924-4927
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Fuel Price ◽  
Common People ◽  
Forecasting Models ◽  
Sales Prediction ◽  
The Future ◽  
Feed Forward Neural Networks ◽  
Past Data ◽  
Predicted Values

All of us are very curious about future, very excited to know what will happen in the next moment. Similarly, retailers are also curious about the future of their business, its progress and their future sales. Walmart is the world’s biggest retailer and also has a vast grocery chain over the world. It was initially established in America 1962. In 2019, it has more than 11,000 stores in 28 countries but the sales differ from place to place. Many sales strategies, discount rates will be introduced for the improvement of sales. Retailers always try to attract the common people to visit their store. They always focus on improving the future sales. Using some Machine learning forecasting models, we can estimate the future sales based on the past data. Our aim is to apply time series forecasting models to retail sales data, which contains weekly sales of 45 Walmart stores across United States from 2010 to 2012. There are other factors which effects the analysis of weekly sales - markdown, consumer per index, Is Holiday (boolean value returns whether it is holiday or not), size of the store, unemployment, store type, fuel price and temperature. The forecasting models applied for the data are Autoregressive Integrated Moving Average (ARIMA) model and Feed Forward Neural Networks (FFNN). The dataset will be divided into training and testing datasets. The predicted values will be checked with the test data and accuracy will be calculated. Based on the accuracy we conclude which of the two models will better for the sales prediction.

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 Forecasting incidence of tuberculosis cases in Brazil based on various univariate time-series models

2019 ◽  
Vol 7 (10) ◽  
pp. 894-909
Author(s):  
Rhuan Carlos Martins Ribeiro ◽  
Thaynara Araújo Quadros ◽  
John Jairo Saldarriaga Ausique ◽  
Otavio Andre Chase ◽  
Pedro Silvestre da Silva Campos ◽  
...  
Keyword(s):  
Public Health ◽  
Time Series ◽  
Mean Square Error ◽  
High Performance ◽  
Moving Average ◽  
Arima Model ◽  
Public Health Problem ◽  
Absolute Error ◽  
Exponential Smoothing ◽  
Mean Square

Tuberculosis (TB) remains the world's deadliest infectious disease and is a serious public health problem. Control for this disease still presents several difficulties, requiring strategies for the execution of immediate combat and intervention actions. Given that changes through the decision-making process are guided by current information and future prognoses, it is critical that a country's public health managers rely on accurate predictions that can detect the evolving incidence phenomena. of TB. Thus, this study aims to analyze the accuracy of predictions of three univariate models based on time series of diagnosed TB cases in Brazil, from January 2001 to June 2018, in order to establish which model presents better performance. For the second half of 2018. From this, data were collected from the Department of Informatics of the Unified Health System (DATASUS), which were submitted to the methods of Simple Exponential Smoothing (SES), Holt-Winters Exponential Smoothing (HWES) and the Integrated Autoregressive Moving Average (ARIMA) model. In the performance analysis and model selection, six criteria based on precision errors were established: Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE) and Theil's U statistic (U1 and U2). According to the results obtained, the HWES (0.2, 0.1, 0.1) presented a high performance in relation to the error metrics, consisting of the best model compared to the other two methodologies compared here.

scholarly journals Modeling and Forecasting of Covid-19 Growth Curve in India

2020 ◽  
Author(s):  
Vikas Kumar Sharma ◽  
Unnati Nigam
Keyword(s):  
Akaike Information Criterion ◽  
Regression Models ◽  
Arima Model ◽  
Information Criterion ◽  
Exponential Smoothing ◽  
Modeling And Forecasting ◽  
Best Fitting ◽  
Auto Regressive ◽  
Fitting Model ◽  
Optimal Change

AbstractIn this article, we analyze the growth pattern of Covid-19 pandemic in India from March 4th to May 15th using regression analysis (exponential and polynomial), auto-regressive integrated moving averages (ARIMA) model as well as exponential smoothing and Holt-Winters models. We found that the growth of Covid-19 cases follows a power regime of (t2, t,..) after the exponential growth. We found the optimal change points from where the Covid-19 cases shift their course of growth from exponential to quadratic and then from quadratic to linear. We have also found the best fitted regression models using the various criteria such as significant p-values, coefficients of determination and ANOVA etc. Further, we search the best fitting ARIMA model for the data using the AIC (Akaike Information Criterion) and CAIC (Consistent Akaike Information Criterion) and provide the forecast of Covid-19 cases for future days. We also use usual exponential smoothing and Holt-Winters models for forecasting purpose. We further found that the ARIMA (2,2,0) model is the best-fitting model for Covid-19 cases in India.

scholarly journals Projections of Regional Macroeconomic Conditions using the Univariate Forecasting Method

2020 ◽  
Vol 8 (3) ◽  
pp. 329-342
Author(s):  
Inayati Nuraini Dwiputri ◽  
Muhammad Syam Kusufi ◽  
Albertus Girik Allo
Keyword(s):  
Economic Growth ◽  
Income Distribution ◽  
Unemployment Rate ◽  
Arima Model ◽  
Exponential Smoothing ◽  
Jel Classification ◽  
Macroeconomic Conditions ◽  
Macroeconomic Indicators ◽  
The Government ◽  
Regional Product

The prediction of future macroeconomic conditions is needed by the government to carry out the planning and budgeting. This study predicts macro indicators in Hulu Sungai Utara Regency in the period 2017-2022. The method used is univariateforecasting, which includes the ARIMA model, exponential smoothing, and exponential smoothing with trend adjustment. The macroeconomic indicators used in this study are real Gross Domestic Regional Product (GDRP), economic growth, unemployment rate, and income distribution. The results of the analysis show that Brown's forecasting model is predicted that the real GDRP value tends to increase, forecasting results using a simple model on economic growth and the ARIMA (0.0,0) model on the unemployment rate, had predicted tends to be constant. And, the prediction of income distribution with the Holt model tends to increase. Keywords: macroeconomic, univariate, forecasting, ARIMA, exponential smoothing JEL Classification: E0, O1, C0

scholarly journals Forecasting US Tourists’ inflow to Slovenia by modified Holt-Winters Damped model: A case in the Tourism industry logistics and supply chains

2019 ◽  
Vol 10 (1) ◽  
pp. 11-30
Author(s):  
Dejan Dragan ◽  
Abolfazl Keshavarzsaleh ◽  
Tomaž Kramberger ◽  
Borut Jereb ◽  
Maja Rosi
Keyword(s):  
Supply Chains ◽  
Predictive Models ◽  
Arima Model ◽  
Exponential Smoothing ◽  
Tourism Industry ◽  
Proposed Model ◽  
The Us ◽  
Forecasting System ◽  
Exponential Smoothing Method ◽  
Practical Implications

Abstract Forecasting is important in many branches of logistics, including the logistics related to Tourism supply chains. With an increasing inflow of American tourists, planning and forecasting the US tourists’ inflow to Slovenia have gained far more importance attention amongst scholars and practitioners. This study, therefore, was conducted to forecast the American tourists’ inflow to Slovenia using one of the predictive models based on the exponential smoothing approach, namely Holt-Winters damped additive (HWDA) exponential smoothing method. The model was modified by several improvements, while the obtained results were generalized to other supply chain components. The results show that the forecasting system can predict well the observed inflow, while the methodology used to derive the model might have enriched the plethora of existing practical forecasting approaches in the tourism domain. Benchmarking demonstrates that the proposed model outperforms a competitive ARIMA model and official forecasts. The practical implications are also discussed in this paper.

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