A prediction scheme using perceptually important points and dynamic time warping

An algorithmic method for assessing statistically the efficient market hypothesis (EMH) is developed based on two data mining tools, perceptually important points (PIPs) used to dynamically segment price series into subsequences, and dynamic time warping (DTW) used to find similar historical subsequences. Then predictions are made from the mappings of the most similar subsequences, and the prediction error statistic is used for the EMH assessment. The predictions are assessed on simulated price paths composed of stochastic trend and chaotic deterministic time series, and real financial data of 18 world equity markets and the GBP/USD exchange rate. The main results establish that the proposed algorithm can capture the deterministic structure in simulated series, confirm the validity of EMH on the examined equity indices, and indicate that prediction of the exchange rates using PIPs and DTW could beat at cases the prediction of last available price

Reducing the Bias of Causality Measures

Measures of the direction and strength of the interdependence between two time series are evaluated and modified in order to reduce the bias in the estimation of the measures, so that they give zero values when there is no causal effect. For this, point shuffling is employed as used in the frame of surrogate data. This correction is not specific to a particular measure and it is implemented here on measures based on state space reconstruction and information measures. The performance of the causality measures and their modifications is evaluated on simulated uncoupled and coupled dynamical systems and for different settings of embedding dimension, time series length and noise level. The corrected measures, and particularly the suggested corrected transfer entropy, turn out to stabilize at the zero level in the absence of causal effect and detect correctly the direction of information flow when it is present. The measures are also evaluated on electroencephalograms (EEG) for the detection of the information flow in the brain of an epileptic patient. The performance of the measures on EEG is interpreted, in view of the results from the simulation study.Comment: 30 pages, 12 figures, accepted to Physical Review

RANKING OF SEISMIC ZONES IN GREECE USING MEASURES OF NETWORKS FORMED FROM EARTHQUAKE HISTORICAL DATA

Επιχειρείται διερεύνηση των αλληλεξαρτήσεων μεταξύ σεισμών μεInterdependencies in earthquakes wit

Improving the Global Fitting Method on Non-Linear Time Series Analysis

In this paper, we are concerned with improving the forecast capabilities of the Global approach to Time Series. We assume that the normal techniques of Global mapping are applied, the noise reduction is performed, etc. Then, using the mathematical foundations behind such approaches, we propose a method that, without a great computational cost, greatly increase the accuracy of the corresponding forecasting