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In this paper, a spiking neural network architecture is proposed to be used for control charts pattern recognition (CCPR).
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Finally, on the basis of these charts, patterns and connections could be described through an iterative and comparative process of searching, reviewing, and comparing the data.
Formation: A recognized, technically significant chart pattern.
Therefore, control chart pattern recognition is important in SPC.
Control Chart Pattern Recognition (CCPR) is a critical task in Statistical Process Control (SPC).
This chapter proposes the application of SNN techniques to control chart pattern recognition.
The chart pattern for Mexican stocks looks unsurprisingly like that of the S&P 500 index.
While every chart pattern is unique, each general type tends to yield similar results.
Line Formation: A sideways chart pattern that unfolds within a very narrow price range.
Spike: A chart pattern revealing a sudden or extreme move to a new high or low.
This can be applied to other feature selection problems besides SPC chart pattern recognition.
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