Forecasting Rail Transport Petroleum Consumption Using an Integrated Model of Autocorrelation Functions-Artificial Neural Network

Medicines are widely used to prevent or cure illness. One of the medicines, which often used to relieve stomach pain, is a medicine that contains digestive enzymes. Hospitals and other health institutions much need this type of medicines. Hospitals and other health institutions should ensure the availability of medications for patients. This situation forces health institutions to deal with the uncertainty of medicine usage. Hospitals as one of the health institutions have some challenges. One of the challenges that must be faced is to ensure the availability of medicines for patients. The ability to predict can help ensure medicines availability in hospital. In this study will presents the forecasting model using Long Term Short Memory (LSTM) method to predict the need for medicines that contain digestive enzymes in the hospital. This method is chosen because it is known to have high accuracy to predict stationary data. One of the methods used in input identification for the LSTM method is by using the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF). The results of this study indicate that the use of LSTM method suitable for time series forecasting in the historical dataset, with 12.733 Root Mean Square Error (RMSE) value.

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Predicting the Amount of Digestive Enzymes Medicine Usage with LSTM

Permanasari

Zaky

Fauziati

et al. 2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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Statistical Methods for Modelling Petroleum Products Consumption in Iraq

Dawood¹,

Kareem²

2019

University of Thi-Qar Journal

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In this research there are models developed to consumption for three major petroleum products; Fuel oil, Gas oil and Gasoline for five years (2016-2020) based on two statistical methods; Autoregressive Integrated Moving Average (ARIMA) and Multi Linear Regression (MLR). The data collected for actual demand and consumption from 2005-2015 years, from different sources are used to estimate petroleum products consumption for Baghdad governorate (as a case study).These predicted models are affected by factors as population, urbanization rate, number of vehicles and electricity sector. The generated results by (ARIMA) are suitable to forecasting and more accurate, since it depends on Mean Absolute Percentage Error (MAPE) to determine forecasting accuracy the results showed that the error was equal to 8.1%, 15.93% and 10.57% for Gasoline, Gas oil and Fuel oil respectively. SPSS program version ( 23) is used to reveal these results, showing an increase in consumption of Fuel Oil and the stability of consumption to Gasoline and Fuel oil, in the same level of consumption in the past years resulting an increase in the gap between demand and produced quantity when compared to Doura refinery production quantity of these fuels. While the produced quantity of Fuel oil is greater than the predicted consumption required. These results are valuable to decision makers to select between different alternatives

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Forecasting Rail Transport Petroleum Consumption Using an Integrated Model of Autocorrelation Functions-Artificial Neural Network

Cited by 2 publications

References 13 publications

Predicting the Amount of Digestive Enzymes Medicine Usage with LSTM

Predicting the Amount of Digestive Enzymes Medicine Usage with LSTM

Statistical Methods for Modelling Petroleum Products Consumption in Iraq

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