With the passage of time the impacts of natural hazards continue to increase around the world. The globalization and growth of human societies and their escalating complexity and river flooding will further increase the risks of natural hazards. Flood prediction and control are one of the greatest challenges facing the world today, which have become more frequent and severe due to the effects o...

In this paper a parallel and adaptive methodology for optimizing the time series prediction using System Identification is shown. In order to validate this methodology, a set of time series based on the sun activity measured during the 20th century have been used. The prediction precision for short and long term improves with this technique when it is compared with the found results using Syste...

This paper describes an application of genetic programming to forecasting financial markets that allowed the authors to rank first in a competition organized within the CEC2000 on “Dow Jones Prediction”. The approach is substantially driven by the rules of that competition, and is characterized by individuals being made up of multiple GP expressions and specific genetic operators.

In this work, we propose neural network inversion of a backward predictor as a technique for multi-step prediction of dynamic time series. It may be di cult to train a large network to capture the correlation that exists in some dynamic time series represented by small data sets. The new approach combines an estimate obtained from a forward predictor with an estimate obtained by inverting a bac...

In this paper, the problem of time series prediction is studied. A Bayesian procedure based on Gaussian process models using a nonstationary covariance function is proposed. Experiments proved the approach e4ectiveness with an excellent prediction and a good tracking. The conceptual simplicity, and good performance of Gaussian process models should make them very attractive for a wide range of ...

Autoregressive integrated moving average (ARIMA) is one of the most popular linear models for time series forecasting due to its nice statistical properties and great flexibility. However, its parameters are estimated in a batch manner and its noise terms are often assumed to be strictly bounded, which restricts its applications and makes it inefficient for handling large-scale real data. In th...

In this paper, two CI techniques, namely, single multiplicative neuron (SMN) model and adaptive neuro-fuzzy inference system (ANFIS), have been proposed for time series prediction. A variation of particle swarm optimization (PSO) with co-operative sub-swarms, called COPSO, has been used for estimation of SMN model parameters leading to COPSO-SMN. The prediction effectiveness of COPSOSMN and ANF...

A prediction scheme for spatio-temporal time series is presented that is based on reconstructed local states. As a numerical example the ev olution of a Kuramoto-Siv ashinsky equation is forecasted using previously sampled data.

We consider the problem of prediction of stationary time series, using the architecture known as mixtures of experts (MEM). Here we suggest a mixture which blends several autoregressive models. This study focuses on some theoretical foundations of the prediction problem in this context. More precisely, it is demonstrated that this model is a universal approximator, with respect to learning the ...

The paper presents basic notions of web mining, radial basis function (RBF) neural networks and -insensitive support vector machine regression ( SVR) for the prediction of a time series for the website of the University of Pardubice. The model includes pre-processing time series, design RBF neural networks and -SVR structures, comparison of the results and time series prediction. The prediction...