Modeling and Forecasting of Fuel Selling Price Using Time Series Approach: Case Study

Bahi, Younes Fakhradine El; Ezzine, Latifa; Moussami, Haj El; Aman, Zineb

doi:10.1109/codit.2018.8394835

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…In our previous work, 27 we conduct a forecasting study of selling price of SSP using ARIMA model. The ARIMA model (1,1,1) is the one that provides accurate forecasts.…”

Section: Resultsmentioning

confidence: 99%

Improving the modeling and forecasting of fuel selling price using the radial basis function technique: A case study

Aman

Ezzine

Bahi

et al. 2019

Journal of Algorithms & Computational Technology

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Recently, the petroleum sector in Morocco has been liberalized which has a significant effect for petroleum product distributors. Since the beginning of December 2015, fuel prices are freely determined. This event presents many constraints affecting the balance of the sector plus the competition between its economic players. The lack of accompanying measures by the State makes this vital reform for public finances that stop subsidizing the price of gasoline vulnerable. As all fuel products are imported, we will be interested in the evolution by making forecasts of the price of fuels in the Moroccan market. In this context, our paper aims mainly to study the selling price of diesel and gasoline in order to provide precise forecasts to the company and to respect the permissible error margin of 3%. To this end, we worked with a widely used approach for price forecasting: artificial neural networks technique (radial basis function). Recently, it is suggested to work with artificial neural networks in forecasting field as an alternative to the traditional linear methods. We developed a radial basis function network to come up with conclusions in terms of the superiority in forecasting performance. Consequently, the radial basis function technique proved its strength manifested in the error that was further minimized: 1.95% instead of 2.85% for autoregressive integrated moving average (ARIMA) model used in our previous work. The error is further minimized by applying radial basis function technique.

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“…In our previous work, 27 we conduct a forecasting study of selling price of SSP using ARIMA model. The ARIMA model (1,1,1) is the one that provides accurate forecasts.…”

Section: Resultsmentioning

confidence: 99%

Improving the modeling and forecasting of fuel selling price using the radial basis function technique: A case study

Aman

Ezzine

Bahi

et al. 2019

Journal of Algorithms & Computational Technology

Self Cite

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“…where 𝑋 is the actual data, 𝑋 𝑚𝑎𝑥 is the maximum value of the input data, 𝑥 𝑚𝑖𝑛 is the minimum value of the input data (Bahi et al, 2018) .…”

Section: Data Normalizationmentioning

confidence: 99%

Forecasting Tourist Arrivals with Partial Time Series Data Using Long-Short Term Memory (LSTM)

Chaidir,

Remli,

Wong

et al. 2023

Preprint

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Tourism is a source of foreign exchange income, especially in the economic field. Increasing foreign tourist arrivals is essential in supporting the economy of Indonesia. The development of the tourism industry can be seen from the increase in tourist visits every year. Based on data obtained by the Indonesian Central Statistics Agency (BPS), there was an increase and decrease in the number of tourist visits during 2006-2019. Along with these conditions, the provision of various tourism products and services needed to support the industry must be adjusted to prevent financial losses. Unfortunately, tourism products are generally easily damaged, so it is necessary to forecast tourist arrivals. This study aims to predict the arrival of foreign tourists to Indonesia using the Long Short Term Memory (LSTM) method. This method is suitable for sequential data such as tourist arrival data. This shows the results of the evaluation using the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) of 591897.46 for RMSE and 636.2 for MAPE. Based on this research, it can be concluded that LSTM is suitable to be used as a model to predict the arrival of foreign tourists in Indonesia.

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“…In our article, we are interested the most in the time series approach: autoregressive integrated moving average (ARIMA) models, [11][12][13][14] multivariate transfer function models, 11,15 dynamic models, 11 and generalized autoregressive conditional heteroskedasticity (GARCH) models 16 have also been proposed. Certainly, ARCH and GARCH models are increasingly utilized and are considered as important tools in the analysis of time series data, especially in the case of financial applications.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of demand using ARIMA model

Fattah

Ezzine

Aman

et al. 2018

International Journal of Engineering Business Management

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286

109

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The work presented in this article constitutes a contribution to modeling and forecasting the demand in a food company, by using time series approach. Our work demonstrates how the historical demand data could be utilized to forecast future demand and how these forecasts affect the supply chain. The historical demand information was used to develop several autoregressive integrated moving average (ARIMA) models by using Box-Jenkins time series procedure and the adequate model was selected according to four performance criteria: Akaike criterion, Schwarz Bayesian criterion, maximum likelihood, and standard error. The selected model corresponded to the ARIMA (1, 0, 1) and it was validated by another historical demand information under the same conditions. The results obtained prove that the model could be utilized to model and forecast the future demand in this food manufacturing. These results will provide to managers of this manufacturing reliable guidelines in making decisions.

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Modeling and Forecasting of Fuel Selling Price Using Time Series Approach: Case Study

Cited by 8 publications

References 18 publications

Improving the modeling and forecasting of fuel selling price using the radial basis function technique: A case study

Improving the modeling and forecasting of fuel selling price using the radial basis function technique: A case study

Forecasting Tourist Arrivals with Partial Time Series Data Using Long-Short Term Memory (LSTM)

Forecasting of demand using ARIMA model

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