Dimensionality reduction for financial data visualization
نویسندگان
چکیده
Various data mining methods are used for examining large financial data sets to uncover hidden and useful information. Ability to access big data sources raises new challenges related with capabilities to handle such enormous amounts of data. This research focuses on big financial data visualization that is based on dimensionality reduction methods. We use data set that contains financial ratios of stocks traded on NASDAQ stock exchange. A brief overview of the most popular dimensionality reduction and visualization methods is presented in this paper. We also show how to adjust the algorithms of these methods for parallel computing. The MPI technology is applied in computer cluster to perform dimensionality reduction. The results show that Random projection and Multidimensional scaling methods can effectively classify data and find the most promising stocks. Keywords—financial data; dimensionality reduction; visualization
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تاریخ انتشار 2017