Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
In this paper, a novel near-lossless color filter array (CFA) image compression algorithm based on JPEG-LS is proposed for VLSI implementation. It consists of a pixel restoration, a prediction, a run mode, and entropy coding modules. According to the information of the previous research, a context table and row memory consumed more than 81% hardware cost in a JPEG-LS encoder design. Hence, in this...
This report summarizes the methodologies and techniques we developed and applied for tackling task 3 of the IEEE ICDM Contest on predicting traffic velocity based on GPS data. The major components of our solution include 1) A pre-processing procedure to map GPS data to the network, 2) A K-nearest neighbor approach for identifying the most similar training hours for every test hour, and 3) A heuristic...
Forecasting of Telecom Traffic influence directly the future development of telecommunications enterprises. According to the complexity and non-linearity of Telecom Traffic , in this paper, the hybrid of wavelet transform (WT), chaos and SVM model was established. First the chaotic feature of Telecom Traffic is verified with chaos theory. It can be seen that Telecom Traffic possessed chaotic features,...
Stock index forecasting is an important issue for investors and financial researchers as the movements of stock indices are nonlinear and subject to multiple factors. In this paper, we try to forecast the movements of Shanghai Composite Index using Generalized Autoregressive Condition Heteroskedasticity model. In order to increase accuracy, we introduced data mining technique and carried out forecast...
The movement of stock index is difficult to predict for it is non-linear and subject to many inside and outside factors. Researchers in this field have tried many methods, SVM and ANN, for example, and have achieved good results. In this paper, we select Radial Basis Functions Neural Network (RBFNN) to train data and forecast the stock index in Shanghai Stock Exchanges. In order to solve the problem...
Stock return forecast has been an important issue and difficult task for both shareholders and financial professionals. To tackle this problem, we introduce least square support vector machine (LS-SVM), an improved algorithm that regresses faster than standard SVM, and dynamic inertia weight particle swarm optimization (W-PSO), that outperform standard PSO in parameter selection. The work of this...
Stock yield forecast has been an important issue and difficult task for both shareholders and financial professionals. In this paper, we introduce least square support vector machine (LS-SVM), an improved algorithm that regresses faster than standard SVM, and the parameters of model proposed are gained in the three levels of Bayesian inference. The work of this paper is as following: First, forecast...
Stock index forecast is not an easy job as it is subject to influence of various factors. Since 1980s, many researchers have used Back Propagation Neural Network BPNN to forecast stock price fluctuations. However, there are some limitations with BPNN. With slow convergent speed and low learning efficiency, BP learning algorithm is easy to get in local minimum and is far from being perfect in stock...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.