Recent studies show that stock patterns might implicate useful information for stock price forecasting. The patterns underlying the price time series can not be discovered exhaustively by the pure man power in a limited time, thus the computer algorithm for stock price pattern recognition becomes more and more popular. Currently, there are mainly two kinds… (More)
Current interests in skyline computation arise due to their relation to preference queries. Since it is guaraneed that a skyline point will not lose out in all dimensions when compared to any other point in the data set, this means that for each skyline point, there exists a set of weight assignments to the dimensions such that the point will become the top… (More)
Support vector machines (SV machines, SVMs) have many merits that distinguish themselves from many other machine-learning algorithms, such as the nonexistence of local minima, the possession of the largest distance from the separating hyperplane to the SVs, and a solid theoretical foundation. However, SVM training algorithms such as the efficient sequential… (More)
Caching the results of frequent query patterns can improve the performance of query evaluation. This paper describes a 2-pass mining algorithm called 2PXMiner to discover frequent XML query patterns. We design 3 data structures to expedite the mining process. Experiments results indicate that 2PXMiner is both efficient and scalable.
We present a fast 64b adder based on Output Prediction Logic (OPL) that has a measured worst-case delay of 409ps, equivalent to 4.7 FO4 inverter delays for the TSMC 0.18um process that was used for fabrication. This normalized delay is 1.45X faster than the fastest previously reported 64b adder. The adder uses a modified radix-3 Kogge-Stone architecture and… (More)
Stock patterns are those that occur frequently in stock time series, containing valuable forecasting information. In this paper, an approach to extract patterns and features from stock price time series is introduced. Thereafter, we employ two ANN-based methods to conduct clustering analyses upon the extracted samples, which are the self-organizing map… (More)