@article {
author = {mahdinejad noori, mohammad mahdi and bali, aref},
title = {Seismic Data Forecasting: A Sequence Prediction or a Sequence Recognition Task},
journal = {International Journal of Engineering},
volume = {26},
number = {2},
pages = {137-142},
year = {2013},
publisher = {Materials and Energy Research Center},
issn = {1025-2495},
eissn = {1735-9244},
doi = {},
abstract = {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.},
keywords = {KEY WORDS Earthquake prediction,multivariate adaptive regression splines (MARS model),sequence learning,sequence recognition,time series analysis. },
url = {http://www.ije.ir/article_72081.html},
eprint = {http://www.ije.ir/article_72081_29049229a43d5b9a208610ce1e9f5bd5.pdf}
}