|Title:||Selective Prediction with Hidden Markov Models
|Abstract:||Focusing on short term trend prediction in a financial context we consider the problem of selective prediction whereby the predictor can abstain from prediction in order to improve its performance. The main characteristic of selective predictors is the trade-off they exhibit between error and coverage rates. In the context of classification selective prediction is termed ‘classification with a reject option', and there the main idea for implementing rejection is Chow’s ambiguity principle. In this paper we examine two types of selective HMM predictors. The first is an ambiguity-based rejection in the spirit of Chow. The second is a specialized mechanism for HMMs that identifies low quality HMM states and abstains from prediction in those states. We call this model selective HMM (sHMM). In both approaches we can trade-off prediction coverage to gain better accuracy in a controlled manner. We compare the performance of the ambiguity-based HMM rejection technique to that of the sHMM approach, demonstrate the effectiveness of both methods and the superiority of the sHMM model.|
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