Abstract
This paper presents an ensemble approach and model; IPCBR, that leverages the capabilities of Case based Reasoning (CBR) and Inverse Problem Techniques (IPTs) to describe and model abnormal stock market fluctuations (often associated with asset bubbles) in time series datasets from historical stock market prices. The framework proposes to use a rich set of past observations and geometric pattern description and then applies a CBR to formulate the forward problem; Inverse Problem formulation is then applied to identify a set of parameters that can statistically be associated with the occurrence of the observed patterns.
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