A Machine Learning Approach to Predict Turning Points for Chaotic Financial Time Series


In this paper, a novel approach to predict turning points for chaotic financial time series is proposed based on chaotic theory and machine learning. The nonlinear mapping between different data points in primitive time series is derived and proven. Our definition of turning points produces an event characterization function, which can transform the profile… (More)
DOI: 10.1109/ICTAI.2007.105


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