Abstract

Ensemble forecasting is one of relatively new modern methods for time series forecasting that employs averaging or stacking from the results of several methods. This paper focuses on the development of ensemble ARIMA-FFNN for climate forecasting by using averaging method. Two data about monthly rainfall in Indonesia, i.e. Wagir and Pujon region, are used as case study. Root mean of squares errors in training and testing datasets are used for evaluating the forecast accuracy. The results of ensemble ARIMA-FFNN are compared to one classical statistical method, i.e. individual ARIMA, and two modern statistical methods, namely individual FFNN and ensemble FFNN. The results show that ARIMA yields more accurate forecast in training datasets than other methods, whereas in testing datasets show that FFNN is the best method. Additionally, this conclusion in line with the results of M3 competition, i.e. modern methods or complex methods do not necessarily produce more accurate forecast than simpler one.

Original languageEnglish
Title of host publicationICSSBE 2012 - Proceedings, 2012 International Conference on Statistics in Science, Business and Engineering
Subtitle of host publication"Empowering Decision Making with Statistical Sciences"
Pages244-247
Number of pages4
DOIs
Publication statusPublished - 2012
Event2012 International Conference on Statistics in Science, Business and Engineering, ICSSBE 2012 - Langkawi, Kedah, Malaysia
Duration: 10 Sept 201212 Sept 2012

Publication series

NameICSSBE 2012 - Proceedings, 2012 International Conference on Statistics in Science, Business and Engineering: "Empowering Decision Making with Statistical Sciences"

Conference

Conference2012 International Conference on Statistics in Science, Business and Engineering, ICSSBE 2012
Country/TerritoryMalaysia
CityLangkawi, Kedah
Period10/09/1212/09/12

Keywords

  • ARIMA
  • FFNN
  • climate
  • ensemble
  • forecasting

Fingerprint

Dive into the research topics of 'Ensemble method based on ARIMA-FFNN for climate forecasting'. Together they form a unique fingerprint.

Cite this