Continually evaluating similarity-based pattern queries on a streaming time series
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Continuous Similarity-Based Queries on Streaming Time Series
IEEE Transactions on Knowledge and Data Engineering
Pattern Recognition for Conditionally Independent Data
The Journal of Machine Learning Research
Efficient Similarity Search over Future Stream Time Series
IEEE Transactions on Knowledge and Data Engineering
Intermittent estimation for Gaussian processes
IEEE Transactions on Information Theory
Long-term prediction intervals of time series
IEEE Transactions on Information Theory
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We present a simple randomized procedure for the prediction of a binary sequence. The algorithm uses ideas from previous developments of the theory of the prediction of individual sequences. We show that if the sequence is a realization of a stationary and ergodic random process then the average number of mistakes converges, almost surely, to that of the optimum, given by the Bayes predictor. The desirable finite-sample properties of the predictor are illustrated by its performance for Markov processes. In such cases the predictor exhibits near-optimal behavior even without knowing the order of the Markov process. Prediction with side information is also considered