Abstract: An example method comprises receiving a first data stream regarding performance of a monitored system at a first time, determining a plurality of distributions from the first data stream, identifying at least one state for each different distribution of the plurality of distributions to identify a plurality of states, classifying each of the plurality of states into classifications, identifying at least one of the plurality of states as being a problematic state, for each state recognizing one or more transitions from or to other states of the plurality of states, receiving a second data stream of the monitored system at a second time, identifying a precursor state of the plurality of states indicating at least a potential future transition to the problematic state, and generating a warning before the monitored system enters the problematic state.

Abstract: An example method comprises receiving a first data stream regarding performance of a monitored system at a first time, determining a plurality of distributions from the first data stream using a density function of a plurality of bins for the data, identifying at least one state for each different distribution of the plurality of distributions to identify a plurality of states, classifying each of the plurality of states into classifications, identifying at least one of the plurality of states as being a problematic state using a first log likelihood ratio, for each state recognizing one or more transitions from or to other states of the plurality of states, receiving a second data stream of the monitored system at a second time, identifying a precursor state indicating at least a potential future transition to the-problematic state, and generating a warning before the monitored system enters the problematic state.