A method for associating time series data to pre-existing discrete data to predict future events, the method comprising: receiving at a processor a data stream transmitted from a sensor configured to measure an operating parameter of a component being monitored, wherein the data stream comprises at least time series data; identifying a sequence of interest in the time series data; correlating the identified sequence of interest from the time series data to preexisting event data to extract a time series event; identifying each occurrence of the identified sequence of interest in the data stream, extracting the identified sequence of interest and marking the identified sequence of interest as an extracted time series event; inspecting event data that chronologically follows the identified sequence of interest for each occurrence of the extracted time series event to identify positive cases indicating a predicted event occurs following the identified sequence of interest and negative cases indicating the predicted event does not occur following the identified sequence of interest to calculate a measure of predictive power of the time series sequence; training a prediction algorithm using training samples to identify the positive cases and ignore the negative cases of the identified sequence of interest; storing time series data patterns for the relevant time series sequences having a high predictive value; performing data mining on historical data within a database to create new templates for the time series sequences having high predictive value; analyzing incoming new time series data arriving in the data stream to determine pattern matches with the time series templates to predict an occurrence of an event; and assessing a likelihood of occurrence of the event by determining how closely the new time series data matches the pattern of one or more templates. receiving at a processor a data stream transmitted from a sensor configured to measure an operating parameter of a component being monitored, wherein the data stream comprises at least time series data; identifying a sequence of interest in the time series data; correlating the identified sequence of interest from the time series data to preexisting event data to extract a time series event; identifying each occurrence of the identified sequence of interest in the data stream, extracting the identified sequence of interest and marking the identified sequence of interest as an extracted time series event; inspecting event data that chronologically follows the identified sequence of interest for each occurrence of the extracted time series event to identify positive cases indicating a predicted event occurs following the identified sequence of interest and negative cases indicating the predicted event does not occur following the identified sequence of interest to calculate a measure of predictive power of the time series sequence; training a prediction algorithm using training samples to identify the positive cases and ignore the negative cases of the identified sequence of interest; storing time series data patterns for the relevant time series sequences having a high predictive value; performing data mining on historical data within a database to create new templates for the time series sequences having high predictive value; analyzing incoming new time series data arriving in the data stream to determine pattern matches with the time series templates to predict an occurrence of an event; and assessing a likelihood of occurrence of the event by determining how closely the new time series data matches the pattern of one or more templates.