Open AccessPeramalan Data Runtun Waktu menggunakan Model Hybrid Time Series Regression – Autoregressive Integrated Moving Average
Author(s) -
Melisa Arumsari,
Andrea Tri Rian Dani
Publication year - 2021
Publication title -
jurnal siger matematika
Language(s) - English
Resource type - Journals
eISSN - 2721-6853
pISSN - 2721-5849
DOI - 10.23960/jsm.v2i1.2736
Subject(s) - autoregressive integrated moving average , time series , autoregressive model , computer science , series (stratigraphy) , statistics , mean absolute percentage error , moving average , artificial neural network , mathematics , artificial intelligence , paleontology , biology
Forecasting is a method used to estimate or predict a value in the future using data from the past. With the development of methods in time series data analysis, a hybrid method was developed in which a combination of several models was carried out in order to produce a more accurate forecast. The purpose of this study was to determine whether the TSR-ARIMA hybrid method has a better level of accuracy than the individual TSR method so that more accurate forecasting results are obtained. The data in this study are monthly data on the number of passengers on American airlines for the period January 1949 to December 1960. Based on the analysis, the TSR-ARIMA hybrid method produces a MAPE of 3,061% and the TSR method produces an MAPE of 7,902%.