Seismic Data Forecasting: A Sequence Prediction or a Sequence Recognition Task

In this paper, the multivariate adaptive regression splines (MARS) is employed to predict earthquake events based on two common approaches in sequence learning. In the first scenario, the task is definedas a sequence prediction problem, and consequently the MARS model is used as a predictor. In thesecond scenario, the same task is considered as a sequence recognition problem and the model of MARS, this time, is used as a binary classifier with results that could alternatively help to predict an earthquake event. Forecasting results of applying the methods to a cluster of seismic data on pacificring of fire indicate that MARS as a binary classifier outperforms the predictor MARS. In fact, while both approaches are plausible, the best results are achieved when the earthquake prediction problem is considered as a sequence recognition task

Seismic Data Forecasting: A Sequence Prediction or a Sequence Recognition Task Keywords:

Earthquake Prediction , Multivariate Adaptive Regression Splines , (MARS Model) , Sequence Learning , Sequence Recognition , Time Series Analysis