Interdisciplinary application of nonlinear time series methods

Interdisciplinary application of nonlinear time series methods

This paper reports on the application to field measurements of time series methods developed on the basis of the theory of deterministic chaos. The major difficulties are pointed out that arise when the data cannot be assumed to be purely deterministic and the potential that remains in this situation is discussed. For signals with weakly nonlinear structure, the presence of nonlinearity in a ge...

Nonlinear Time Series: Nonparametric and Parametric Methods

Some New Methods for Prediction of Time Series by Wavelets

Extended Abstract. Forecasting is one of the most important purposes of time series analysis. For many years, classical methods were used for this aim. But these methods do not give good performance results for real time series due to non-linearity and non-stationarity of these data sets. On one hand, most of real world time series data display a time-varying second order structure. On th...

Application of Geostatistics in Time Series: Mashhad Annual Rainfall

There are numerous methods for data fillingin hydrology. Most, however, are based oncorrelations with nearby stations in a generalscheme of regionalization. These methods,though robust, fail to function when andwhere all the near stations are missed-datatoo. The Mashhad synoptic station has annualrainfall data over a 50 year period from 1951to 2000 and historic rainfall data from 1893 to1940, j...

SVM-Based Time Series Prediction with Nonlinear Dynamics Methods

A key problem in time series prediction using autoregressive models is to fix the model order, namely the number of past samples required to model the time series adequately. The estimation of the model order using cross-validation is a long process. In this paper we explore faster alternative to cross-validation, based on nonlinear dynamics methods, namely Grassberger-Procaccia, Kégl and False...