RESUMO
Predicting the values of a financial time series is mainly a function of its price history, which depends on several factors, internal and external. With this history, it is possible to build an ∊-machine for predicting the financial time series. This work proposes considering the influence of a financial series through the transfer of entropy when the values of the other financial series are known. A method is proposed that considers the transfer of entropy for breaking the ties that occur when calculating the prediction with the ∊-machine. This analysis is carried out using data from six financial series: two American, the S&P 500 and the Nasdaq; two Asian, the Hang Seng and the Nikkei 225; and two European, the CAC 40 and the DAX. This work shows that it is possible to influence the prediction of the closing value of a series if the value of the influencing series is known. This work showed that the series that transfer the most information through entropy transfer are the American S&P 500 and Nasdaq, followed by the European DAX and CAC 40, and finally the Asian Nikkei 225 and Hang Seng.
RESUMO
In the present work, based on high frequency wavelet analysis of dynamic signals of mechanical systems, a multiple-resolution wavelet analysis is carried out, to the signal obtained from an accelerometer mounted on the structure of a hip prosthesis wearing test device. The prostheses employed had a femoral head made of aluminum oxide and the acetabular cup of ultra-high-molecular-weight polyethylene. The first two aluminum oxide femoral heads were coated with diamond-like carbon and a third one was tested without coating and used as a reference. The coating was carried out by triboadhesion. Tests results showed that maximum vibration amplitude reached after 32 hr for the coated prostheses was 0.2 g. The noncoated prosthesis amplitude presented was 0.75 g in the same time interval. These values were attributed to wear damage on the surface of the prostheses, indicating that thin film DLC coating caused an increase of stiffness on the surface and therefore an increase in wear resistance approximately of 314%.