Combinatorial time series forecasting based on clustering algorithms and neural networks
Neural Computing and Applications
Partitioning-clustering techniques applied to the electricity price time series
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Clustering preprocessing to improve time series forecasting
AI Communications
Computational intelligence techniques for predicting earthquakes
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Neural networks to predict earthquakes in Chile
Applied Soft Computing
A fast partitioning algorithm and its application to earthquake investigation
Computers & Geosciences
| Hi-index | 12.07 |
Earthquakes arrive without previous warning and can destroy a whole city in a few seconds, causing numerous deaths and economical losses. Nowadays, a great effort is being made to develop techniques that forecast these unpredictable natural disasters in order to take precautionary measures. In this paper, clustering techniques are used to obtain patterns which model the behavior of seismic temporal data and can help to predict medium-large earthquakes. First, earthquakes are classified into different groups and the optimal number of groups, a priori unknown, is determined. Then, patterns are discovered when medium-large earthquakes happen. Results from the Spanish seismic temporal data provided by the Spanish Geographical Institute and non-parametric statistical tests are presented and discussed, showing a remarkable performance and the significance of the obtained results.