Abstract

Ensemble methods used for classification and regression have been shown that they are superior than other methods, teoritically and empirically. Adapting this method on time-series prediction is done by using boosting algorithm. On boosting algorithm, recurrent neural networks (RNN) are generated, each for training on a different set of examples on time-series data, then the results for each of this base learners will be combined and resulting on a final hypothesis.The difference between our algorithm and the original algorithm is the introduction of a new parameter for tuning the boosting influence on given examples. Our boosting result is then tested on real time-series forecasting, using a natural dataset and function-generated time series. On the experiment result, it can be proved that ensemble method that we used is better than standard method, backpropagation through time for one step ahead time series prediction.

Original languageEnglish
Title of host publicationInternational Conference on Instrumentation, Communication, Information Technology, and Biomedical Engineering 2009, ICICI-BME 2009
DOIs
Publication statusPublished - 2009
EventInternational Conference on Instrumentation, Communication, Information Technology, and Biomedical Engineering 2009, ICICI-BME 2009 - Bandung, Indonesia
Duration: 23 Nov 200925 Nov 2009

Publication series

NameInternational Conference on Instrumentation, Communication, Information Technology, and Biomedical Engineering 2009, ICICI-BME 2009

Conference

ConferenceInternational Conference on Instrumentation, Communication, Information Technology, and Biomedical Engineering 2009, ICICI-BME 2009
Country/TerritoryIndonesia
CityBandung
Period23/11/0925/11/09

Keywords

  • Boosting
  • L1earning algorithm
  • Recurrent neural networks
  • Time series forecasting

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