Online autoregressive prediction in time series with delayed disclosure

We propose a supervised machine learning method to automate the classification of events within time series in a monitoring context. It is based on a generative stochastic model of the time series which combines a probabilistic autoregressive classifier to determine the class label of each event, and a hidden Markov model to capture the production of the events. Events can be described by arbitrary combinations of discrete and continuous features. While at training time (offline), it is assumed that the class labels of all the events are known, at inference time (online), when a prediction is to be made for an event, it is not assumed that the class labels of the preceding events are known. This makes prediction more complex due to the autoregressive nature of the model. Instead, we make and exploit a “delayed disclosure” assumption, namely that the class labels of all the events are eventually revealed, but the occurrence of an event and the revelation of its class are asynchronous. We report experimental results obtained by application of this approach to the monitoring of a fleet of distributed devices.