Generative Adversarial Networks Time Series Models to Forecast Medicine Daily Sales in Hospital - Husein, Amir Mahmud; Arsyal, Muhammad; Sinaga, Sutrisno; Syahputa, Hendra (Politeknik Ganesha Medan, 2019)
The success of the work of Generative Adversarial Networks (GAN) has recently achieved great success in many fields, such as stock market prediction, portfolio optimization, financial information processing and trading execution strategies, because the GAN model generates seemingly realistic data with models generator and discriminator .Planning for drug needs that are not optimal will have an impact on hospital services and economics, so it requires a reliable and accurate prediction model with the aim of minimizing the occurrence of shortages and excess stock, In this paper, we propose the GAN architecture to estimate the amount of drug sales in the next one week by using the drug usage data for the last four years (2015-2018) for training, while testing using data running in 2019 year , the classification results will be evaluated by Actual data uses indicators of Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). From the results of the experiment, seen from the value ​​of MAE, RMSE and MAPE, the proposed model has promising performance, but it still needs to be developed to explore ways to extract factors that are more valuable and influential in the trend disease progression, thus helping in the selection of optimal drugs