Correlation and annotation of time series data sequences to extracted or existing discrete data

A system for associating time series data to template time series data patterns to detect current predict future events, the system comprising:

• at least one processing unit and at least one database;
• a plurality of sensors in communication with the at least one processing unit;
• wherein the at least one processing unit is configured to:
• receive 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;
• analyze the data stream to identify a sequence of interest in the time series data having predictive values by matching patterns in the time series data to a template pattern of a known event;
• extract the identified sequence of interest from the time series data;
• mark the identified sequence of interest as an extracted event;
• store the extracted event in a database to indicate a possible detection or prediction of an occurrence of an event;
• specify a relationship between the time series data of the extracted event and the template pattern of the known event in quantifiable terms;
• and
• quantify the relationship between the time series data of the extracted event and the known event by calculating a confidence level to denote a statistical probability of occurrence of the event by comparing data patterns of the extracted event with data of the template pattern associated with the known event.

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;
• correlating relevant time series data to pre-existing event data to detect extracted time series events;
• identifying each occurrence of the relevant time series data in the data stream, extracting the identified relevant time series data and marking the relevant time series data as an extracted time series event;
• inspecting event data that chronologically follows the relevant time series sequence for each occurrence of the extracted time series event to identify positive cases and negative cases 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 relevant time series sequence;
• storing time series data patterns for the relevant time series sequences having a high predictive value;
• and
• performing data mining on historical data within a database to create new templates for the time series sequences having high predictive value.

A system for associating time series data to pre-existing discrete data to predict future events, the system comprising:

• at least one processing unit and at least one database;
• a plurality of sensors in communication with the at least one processing unit;
• wherein the at least one processing unit is configured to:
• receive 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;
• correlate relevant time series data to pre-existing event data to detect extracted time series events;
• identify each occurrence of the relevant time series data in the data stream, extract the identified relevant time series data and mark the relevant time series data as an extracted time series event;
• inspect event data that chronologically follows the relevant time series sequence for each occurrence of the extracted time series event to identify positive cases and negative cases to calculate a measure of predictive power of the time series sequence;
• train a prediction algorithm using training samples to identify the positive cases and ignore the negative cases of the relevant time series sequence;
• store time series data patterns for the relevant time series sequences having a high predictive value;
• and
• perform data mining on historical data within a database to create new templates for the time series sequences having high predictive value.