Causality estimation of time series via supervised learning

A system for estimating aspects of time-series data, the system comprises:

• a processor;
• and
• a memory storing computer-executable instructions that when executed by the processor cause the system to:
• generating two or more sets of time-series data for machine learning based on a normalized combination of linear time-series data and non-linear time-series data;
• receive a correct label of three or more classification labels for machine learning, wherein the correct label represents a causal relation among the two or more sets of time-series data for machine learning, and the three or more classification labels comprise:
• a first set of time-series data is a cause of a second set of time-series data,
• the second set of time-series data is a cause of the first set of time-series data, or
• no causal relation between the first set of time-series data and the second set of time-series data;
• receive the two or more sets of time-series data for machine learning;
• generate a set of feature values of respective sets of the two or more sets of time-series data;
• train, based on a set of the generated feature values and the correct label, a classifier based on machine learning for classifying a causal relation of time series data, wherein a first output value of the classifier using the generated feature value is a maximum output value of the classifier using the correct label;
• and
• estimate, using the trained classifier, the causal relation among a plurality of sets of time-series data.

A computer-readable non-transitory recording medium storing computer-executable instructions that when executed by a processor cause a computer system to:

• generating two or more sets of time-series data for machine learning based on a normalized combination of linear time-series data and non-linear time-series data;
• receive a correct label of three or more classification labels for machine learning, wherein the correct label represents a causal relation among the two or more sets of time-series data for machine learning, and the three or more classification labels comprise:
• a first set of time-series data is a cause of a second set of time-series data,
• the second set of time-series data is a cause of the first set of time-series data, or
• no causal relation between the first set of time-series data and the second set of time-series data;
• receive the two or more sets of time-series data for machine learning;
• generate a set of feature values of respective sets of the two or more sets of time-series data;
• train, based on a set of the generated feature values and the correct label, a classifier based on machine learning for classifying a causal relation of time series data, wherein a first output value of the classifier using the generated feature value is a maximum output value of the classifier using the correct label;
• and
• estimate, using the trained classifier, the causal relation among a plurality of sets of time-series data.