Continuously variable duration hidden Markov models for automatic speech recognition
Computer Speech and Language
Variational Bayes for Continuous Hidden Markov Models and Its Application to Active Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Change detection in rainfall and temperature patterns over India
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
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Intra-seasonal changes in the Indian summer monsoon are generally characterized by its active and break (A&B) states. Existing methods for identifying the A&B states using rainfall data rely on subjective thresholds, ignore temporal dependence in the data, and disregard inherent uncertainty in their identification. This paper develops a method to identify intra-seasonal changes in the monsoon using a hidden Markov model (HMM) that allows objective classification of the monsoon states. The method facilitates probabilistic interpretation which is especially useful during the transition period between the two monsoon states. The developed method can also be used to - (i) identify monsoon states in real time, (ii) forecast rainfall values, and (iii) generate synthetic data. Comparisons of the results from the proposed model with those from existing methods suggest that the new method is a promising for detecting intra-seasonal changes in the Indian summer monsoon.