Reversal-point-based detection and ranking

A computerized method comprising:

• obtaining time series data, the time series data including a value for a first metric at each of a plurality of time points separated by time intervals;
• identifying, in the time series data, a plurality of reversal points, a reversal point being a time point at which the value for the first metric changed from positive to negative or vice-versa;
• obtaining training data from one or more databases;
• training a machine learned model, using the training data as input to a machine learning algorithm, to generate a ranking score for an input reversal point, the ranking score being based on:
• calculating an abnormality score by:
• determining a difference between a length of a trend, prior to the input reversal point, in a segment of the time series containing the input reversal point and a total number of reversal points in a plurality of reversal points in the segment, wherein a trend is a sequence of time points at which the value for the first metric maintained a same sign;
• dividing the difference by a value computed based on a total number of time points in the segment;
• calculating a significance score by:
• determining a maximum absolute value of differences between a magnitude change at the input reversal point and those magnitude changes in the trend in the segment prior to the input reversal point, applying a constant β to the maximum absolute value, β learned via the training of the machine learning model;
• applying a first weight to the abnormality score and a value related to the first weight to the significance score;
• and
• combining the weighted abnormality score and weighted significance score into the ranking score;
• evaluating one or more of the plurality of reversal points by passing each of the one or more reversal points to the machine learned model, thus outputting a ranking score for each of the one or more of the plurality of reversal points;
• ranking the one or more of the plurality of reversal points based on their corresponding ranking scores;
• and
• highlighting one or more of the reversal points in a graphical user interface based on the ranking.

A non-transitory machine-readable storage medium comprising instructions which, when implemented by one or more machines, cause the one or more machines to perform operations comprising:

• obtaining time series data, the time series data including a value for a first metric at each of a plurality of time points separated by time intervals;
• identifying, in the time series data, a plurality of reversal points, a reversal point being a time point at which the value for the first metric changed from positive to negative or vice-versa;
• obtaining training data from one or more databases;
• training a machine learned model, using the training data as input to a machine learning algorithm, to generate a ranking score for an input reversal point, the ranking score being based on:
• calculating an abnormality score by:
• determining a difference between a length of a trend, prior to the input reversal point, in a segment of the time series containing the input reversal point and a total number of reversal points in a plurality of reversal points in the segment, wherein a trend is a sequence of time points at which the value for the first metric maintained a same sign;
• dividing the difference by a value computed based on a total number of time points in the segment;
• calculating a significance score by:
• determining a maximum absolute value of differences between a magnitude change at the input reversal point and those magnitude changes in the trend in the segment prior to the input reversal point, applying a constant β to the maximum absolute value, β learned via the training of the machine learning model;
• applying a first weight to the abnormality score and a value related to the first weight to the significance score;
• and
• combining the weighted abnormality score and weighted significance score into the ranking score;
• evaluating one or more of the plurality of reversal points by passing each of the one or more reversal points to the machine learned model, thus outputting a ranking score for each of the one or more of the plurality of reversal points;
• ranking the one or more of the plurality of reversal points based on their corresponding ranking scores;
• and
• highlighting one or more of the reversal points in a graphical user interface based on the ranking.