Methods and systems for the determination of thresholds via weighted quantile analysis
Methods and systems for determination of thresholds for time-series data. Data is transformed by reducing outliers, dividing the time series data into discrete time intervals, and taking parts of the data corresponding to the range that the thresholds will bound. If data cycles are known, they may be applied to the data and the resulting sets are weighted. Thresholds are then derived from the weighted means and variances of the sets of weighted data.
1. A non-transitory computer-readable storage medium having programming for executing a method comprising:
dividing a set of time-series data into at least one time period;
creating at least one set of ranged time-series data by selecting data points from each time period corresponding to a percentage of values useful in defining a threshold;
applying cycles to the set of ranged time-series data;
applying weights to the data for each cycle of ranged time-series data; and
determining at least one threshold from the weighted time-series data;
wherein the programming further executes the method such that an upper and a lower threshold is determined from the weighted time-series data;
wherein the set of time-series data is divided into a plurality of time periods; and
wherein a percentage of the highest values from each time period is aggregated and a percentage of the lowest values from each time period is aggregated to comprise an upper set of ranged time-series data and a lower set of ranged time-series data.
2. The non-transitory computer-readable storage medium of claim 1 , wherein the programming further executes the method such that the time periods are selected from the group consisting of days, weeks, months, or years.
3. The non-transitory computer-readable storage medium of claim 1 , wherein the programming further executes the method such that each threshold is a multiple of weighted standard deviations from the weighted mean.
4. A non-transitory computer-readable storage medium having programming for executing a method comprising:
providing a system for determination of dynamic thresholds, wherein the system determines dynamic thresholds by:
dividing a set of time-series data into a plurality of time periods;
creating at least one set of ranged time-series data by selecting data points from each time period corresponding to a percentage of values useful in defining a threshold and aggregating the selected data points from more than one of the plurality of time periods;
applying cycles to the set of ranged time-series data; and
applying weights to the data for each cycle of ranged time-series data;
determining at least one threshold from the weighted time-series data;
wherein the programming further executes the method such that an upper and a lower threshold is determined from the weighted time-series data; and
wherein a percentage of the highest values from each time period is aggregated and a percentage of the lowest values from each time period is aggregated to comprise an upper set of ranged time-series data and a lower set of ranged time-series data.
5. The non-transitory computer-readable storage medium of claim 4 , wherein the programming further executes the method such that each threshold is a multiple of weighted standard deviations from the weighted mean.
6. A non-transitory machine-readable medium having program instructions stored thereon executable by a processing unit for performing the steps of:
dividing a set of time-series data into a plurality of time periods;
after dividing the set of time-series, creating at least one set of ranged time-series data by selecting data points from individual time periods corresponding to a percentage of values useful in defining a threshold, including forming each set of ranged time-series data by combining the selected data points within the same percentage of values of more than one of the time periods;
after creating the at least one set of ranged time-series data, applying cycles to each of the sets of ranged time-series data;
after applying the cycles, applying weights to the data for each cycle of ranged time-series data; and
determining at least one threshold from the weighted time-series data;
wherein an upper and a lower threshold is determined from the weighted time-series data; and
wherein a percentage of the highest values from each time period and a percentage of the lowest values from each time period comprise an upper set of ranged time-series data and a lower set of ranged time-series data.
7. The non-transitory machine-readable medium of claim 6 , wherein each threshold is a multiple of weighted standard deviations from the weighted mean.