Applied of feed-forward neural network and facebook prophet model for train passengers forecasting
Resa Septiani Pontoh, Shafa Arrizqa Az Zahroh, H R Nurahman, R I Aprillion, Alfi Taufiq Ramdani, D I Akmal · Journal of Physics Conference Series · 2021
Abstract Train is public transportation that is widely used by people in Indonesia. Due to the high level of comfort at a low price relatively. Based on released data by PT KAI, the number of train passengers has increased in almost every holiday season, thereby, it is suspected that there are seasonal patterns with fixed and random periods. In 2013, the government issued a policy related to infrastructure development, then, it caused the number of passengers significantly increasing in the following year. Thus, we need a model that can accommodate these patterns to forecast the number of train passengers accurately. The use of neural network methods such as Feed Forward Neural Network (FFNN), nowadays, becomes popular in facing big data including unexpected fluctuation on the data. Additionally, recently, Facebook announced an accurate method of forecasting, called Prophet model, for data which have trend, seasonality, holidays, and missing data. Hence, the forecast for monthly train passengers on this research is modelled by FFNN and Facebook Prophet. The result shows that Prophet performs better than FFNN. However, the difference in the value of MAPE is not too large.