%0 Journal Article
%T Seismic Data Forecasting: A Sequence Prediction or a Sequence Recognition Task
%J International Journal of Engineering
%I Materials and Energy Research Center
%Z 1025-2495
%A mahdinejad noori, mohammad mahdi
%A bali, aref
%D 2013
%\ 02/01/2013
%V 26
%N 2
%P 137-142
%! Seismic Data Forecasting: A Sequence Prediction or a Sequence Recognition Task
%K KEY WORDS Earthquake prediction
%K multivariate adaptive regression splines (MARS model)
%K sequence learning
%K sequence recognition
%K time series analysis.
%R
%X In this paper, we have tried to predict earthquake events in a cluster of seismic data on pacific ring of fire, using multivariate adaptive regression splines (MARS). The model is employed as either a predictor for a sequence prediction task, or a binary classifier for a sequence recognition problem, which could alternatively help to predict an event. Here, we explain that sequence prediction/recognition, as two aspects of sequence learning, are not the same in general. We show that while both these approaches are plausible for earthquake prediction, the forecasting results indicate that MARS as a binary classifier outperforms the predictor MARS. The results clearly show how it is important to challenge a single earthquake forecasting problem from an appropriate point of view.
%U http://www.ije.ir/article_72081_29049229a43d5b9a208610ce1e9f5bd5.pdf