study of the changes in the stock price in tehran stock exchange is of great importance. this is because of its application in forecasting the stock price in the stock exchange. the aim of this article is to investigate the forces and mechanisms that cause the dramatic changes in stock price and the formation of chaotic trend. to test whether the chaotic trend in the tehran stock exchange exist...

In this paper, a novel solving method for speech signal chaotic time series prediction model was proposed. A phase space was reconstructed based on speech signal’s chaotic characteristics and the genetic programming (GP) algorithm was introduced for solving the speech chaotic time series prediction models on the phase space with the embedding dimension m and time delay . And then, the speech si...

Recently there has been much attention devoted to exploring the complicated possibly chaotic dynamics in pseudoperiodic time series. Two methods [Zhang, Phys. Rev. E 73, 016216 (2006); Zhang and Small, Phys. Rev. Lett. 96, 238701 (2006)] have been forwarded to reveal the chaotic temporal and spatial correlations, respectively, among the cycles in the time series. Both these methods treat the cy...

A chaotic method is employed to forecast a near future of uncertain phenomena. The method makes it possible by restructuring an attractor of given time-series data in a multi-dimensional space through Takens' embedding theory. However, many economical time-series data are not sufficiently chaotic. In other words, it is hard to forecast the future trend of such economical data on the basis of ch...

Large computational quantity and cumulative error are main shortcomings of addweighted one-rank local-region single-step method for multi-steps prediction of chaotic time series. A local-region multi-steps forecasting model based on phase-space reconstruction is presented for chaotic time series prediction, including add-weighted one-rank local-region multisteps forecasting model and RBF neural...

In this chapter, we consider the problem of identifying an unknown parametrized family of chaotic dynamical systems from a variety of its time series data with a change in the bifurcation parameters. In an experimental situation, in which no a priori analytical knowledge of the dynamical systems is available, we present an algorithm for estimating the underlying bifurcation parameters of the ch...

Chaos theory has been hailed as a revolution of thoughts and attracting ever-increasing attention many scientists from diverse disciplines. Chaotic systems are non-linear deterministic dynamic which can behave like an erratic apparently random motion. A relevant field inside chaos is the detection chaotic behavior empirical time-series data. One main features well-known initial-value sensitivit...