Abstract
It is frequent that irregular-looking time series data may be caused by deterministic dynamics, and also well konwn that it is called deterministic chaos. Nowadays even if the time series data observed from a system has little noise, it is not always easy, by eye, to recognize whether or not it has some noise. To solve this issue, in general, there is a method of extracting some characteristic frequency by FFT(Fast Fourier Transformation). But chaotic time series is composed of infinite number of frequency element, and give rise to broad continuous power spectrum. In this paper, we propose a new method based on the chaos theorem, Trajectory Parallel Measure Method that measures the degree of stochastic process in the time series governed by determinism. One of the features of this method is to examine directions of trajectories sampled randomly from the attractor. And we present the result of the application of this method to 3 types of data ; the chaotic time series data, white noise and the time series data which is added some white noise on the chaotic data, and we also present its usefulness.
