Open AccessFuzzy Time Series for Forecasting Railway Passengers in Indonesia
Author(s) -
Muhammad Fatih Rizqon,
Handaru Jati
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2111/1/012013
Subject(s) - mean absolute percentage error , chen , series (stratigraphy) , fuzzy logic , mean absolute error , time series , computer science , approximation error , econometrics , statistics , operations research , data mining , mean squared error , mathematics , artificial intelligence , machine learning , artificial neural network , algorithm , paleontology , biology
Some fuzzy time series models have their own advantages and disadvantages. In addition, these models sometimes are complex and claimed to have better forecasting result than each other. The suitable model for forecasting depends on a wide variety of considerations. The models proposed by Chen (1996) applied simplified arithmetic operations and claimed more efficiency than before. The model proposed by Chen was introduced in 1996 and still exists in several previous studies. This research aims to forecast the number of railway passengers in Indonesia using the fuzzy time series. In addition, this research also evaluates the forecasting results based on mean absolute error (MAE) and mean absolute percentage error (MAPE). The results showed the forecasting results in this research has accuracy for 86.6%.