Abstract
Effective analysis and processing of stock trading data to find the internal relations is of great significance to the guidance on investment decisions. With the stock trading data as research object, this chapter proposes a stock forecasting parallel method with Partition Speed Method and Multithreading parallel computation method. This method conducts acceleration computation with Partition Speed Method of apriority algorithm, equally distributes the computation quantity to all participant computers with Multithreading parallel computation method, and recovers the results in a real time and synchronous manner with network programming technology. The experimental results show that this method can effectively shorten the computation time for stock association rules, improve mining efficiency and further offer powerful help for stock investment.
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Chen, G., Luo, N. (2013). Study of Partition Speed Method in Stock Forecasting. In: Yang, Y., Ma, M. (eds) Proceedings of the 2nd International Conference on Green Communications and Networks 2012 (GCN 2012): Volume 1. Lecture Notes in Electrical Engineering, vol 223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35419-9_15
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DOI: https://doi.org/10.1007/978-3-642-35419-9_15
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