@inproceedings{d82b9b988da84db780cefbd83e899b0a,
title = "Ensemble method based on ANFIS-ARIMA for rainfall prediction",
abstract = "This paper proposed an ensemble method based on ANFIS (Adaptive Neuro Fuzzy Inference System) and ARIMA (Autoregressive Integrated Moving Average) for forecasting monthly rainfall data at certain area in Indonesia, namely Pujon and Wagir area. The averaging method was implemented to find an ensemble forecast from ANFIS and ARIMA models. In this study, Gaussian, Gbell, and Triangular function are used as membership function in ANFIS. The forecast accuracy is compared to the best individual ARIMA and ANFIS. Based on root of mean square errors (RMSE) at testing datasets, the results show that an individual ANFIS method yields more accurate forecast in monthly Pujon's rainfall data, whereas ARIMA model yields better forecast in monthly Wagir's rainfall data. In general, these results in line with M3 competition results that more complicated model not always yield better forecast than simpler one.",
keywords = "ANFIS, ARIMA, averaging, ensemble, rainfall",
author = "Suhartono and Ria Faulina and Lusia, {Dwi Ayu} and Otok, {Bambang W.} and Sutikno and Heri Kuswanto",
year = "2012",
doi = "10.1109/ICSSBE.2012.6396564",
language = "English",
isbn = "9781467315821",
series = "ICSSBE 2012 - Proceedings, 2012 International Conference on Statistics in Science, Business and Engineering: {"}Empowering Decision Making with Statistical Sciences{"}",
pages = "240--243",
booktitle = "ICSSBE 2012 - Proceedings, 2012 International Conference on Statistics in Science, Business and Engineering",
note = "2012 International Conference on Statistics in Science, Business and Engineering, ICSSBE 2012 ; Conference date: 10-09-2012 Through 12-09-2012",
}