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Finding patterns in high dimensional data can be difficult because it cannot be easily visualized. Many different machine learning methods are able to fit this high dimensional data in order to predict and classify future data but there is typically a large expense on having the machine learn the fit for a certain part of the dataset. This research artical proposes a deep learning way of defining different patterns in stock market prices. Using a CNN, the pattern is found within stock market data and predictions are made from it. The stock pattern is divided in five parts decline in value of stock (Abrupt decline, smooth decline), incline in stock value (abrupt increase, smooth increase) and stable price.
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