Published Paper Details:

The provision of pharmaceutical drugs in quantities appropriate to consumption is an crucial point in the pharmaceutical industry and storage of medicines, as the production of massive quantities of unnecessary drugs cause to a longer storage of drugs. In the meantime most of the medicines have a short storage life. If the amount of production of purpose of time series analysis and a prediction of the required quantities drugs. A recurrent neural network with Long-Short Term Memory LSTM has been used by deep learning. The proposed methodology is based on the seasonal number of prescription required quantities with the number of quarters as indicators. The purpose of this research is to forecast the drugs amount needed for one year. The proposed method is evaluated using two types of evaluation. The first one is based on MSE and the visualization of the actual data and forecast-ed data. The proposed method has reached a low value of MSE and the visualization graph is semi-identical, whereas the second evaluation method compares the result of the proposed method with traditional forecasting method. Multiple linear regression is a traditional prediction method used with the data set, whose results are relatively good and promising compared to the results of the traditional method.