METHOD FOR DETECTING ANOMALIES

1 . A method to be carried out by a processor of a computing platform including a memory assembly having encoded thereon executable control-logic instructions configured to be executable by the processor, and also configured to urge the processor to carry out the method comprising:

determining whether there is an existing statistical model to be selected or whether a new statistical model is to be created.

2 . The computing platform of claim 1 , wherein the method further includes:

processing a volume of data having time-series data, where the existing statistical model is identified and selected; and

monitoring the volume of data, where the existing statistical model is identified and selected; and

detecting whether there is at least one anomaly contained in said volume of data by determining whether an actual value falls outside of a confidence interval to be generated, where the existing statistical model is identified and selected.

3 . The computing platform of claim 1 , wherein the method further includes:

checking completeness of a volume of data having time-series data, by determining whether training data includes the volume of data; and

transmitting a first alert configured to indicate that the training data is determined to be incomplete, where the training data is determined to be incomplete.

4 . The computing platform of claim 3 , wherein the method further includes:

determining whether any training data is duplicated; and

transmitting a second alert configured to indicate that the training data is determined to be duplicated, where the training data is determined to be duplicated.

5 . The computing platform of claim 4 , wherein the method further includes:

training a model based on a training period being user predetermined.

6 . The computing platform of claim 5 , wherein the method further includes:

generating confidence interval widths; and

identifying an appropriate confidence level.

7 . The computing platform of claim 6 , wherein the method further includes:

selecting the appropriate confidence level for a forecast, by checking against the confidence interval widths that were generated.

8 . The computing platform of claim 7 , wherein the method further includes:

determining appropriateness by finding a distance between an 80th percentile and a 20th percentile from the volume of data over a predefined time period; and

labeling a confidence level as APPROPRIATE for use, where all of the confidence interval widths is smaller than an 80/20 distance; and

labeling the confidence level as NOT APPROPRIATE, where any one of the confidence widths, that was generated, exceeds the 80/20 distance.

9 . The computing platform of claim 8 , wherein the method further includes:

retraining the statistical model; and

regenerating the confidence level; and

identifying the appropriate confidence level that satisfies an 80/20 distance rule over a training time window; and

iteratively testing, using a relatively smaller time window, where no appropriate confidence level is found that satisfies the 80/20 distance rule over the training time window; and

transmitting a third alert configured to indicate that there exists no appropriate confidence level for the training data, where no appropriate confidence level exists for the training data.

10 . The computing platform of claim 9 , wherein the method further includes:

forecasting values for a future time period, where a statistical model has the appropriate confidence level; and

reprocessing the volume of data having time-series data; and

monitoring the volume of data; and

detecting whether there is at least one anomaly contained in said volume of data by determining whether an actual value falls outside of a confidence interval to be generated.

11 . The computing platform of claim 2 , wherein the method further includes:

labeling the anomaly that was detected, where said at least one anomaly was detected; and

assigning a severity level for the anomaly that was labeled; and

generating an anomaly status for the anomaly that was detected by comparing an actual volume of data received against confidence intervals that were generated.

12 . The computing platform of claim 11 , wherein the method further includes:

marking the anomaly status, for a predefined time period spanning the time-series data, as NORMAL, where the actual volume of data is within the confidence interval during the predefined time period; and

marking the anomaly status, for the predefined time period spanning the time-series data, as HIGH, where the actual volume of data is above an upper bound of the confidence interval during the predefined time period; and

marking the anomaly status, for the predefined time period spanning the time-series data, as LOW, where the actual volume of data is below a lower bound of the confidence interval during the predefined time period.

13 . The computing platform of claim 12 , wherein the method further includes:

determining a severity status of the anomaly that was detected, where the anomaly status, for the predefined time period spanning the time-series data, is marked HIGH or LOW.

14 . The computing platform of claim 13 , wherein the method further includes:

utilizing an internal database of historical dates of holidays; and

checking whether a holiday occurred within a time window; and

not generating a notification of anomaly, where the anomaly status for a selected holiday is marked LOW.

15 . The computing platform of claim 14 , wherein the method further includes:

determining a change between said volume of data received and a median thereof; and

calculating the change from recent normal values; and

using the change to classify a severity score.

16 . The computing platform of claim 11 , wherein the method further includes:

transmitting a notification configured to indicate that the anomaly was detected and user-action is required.

17 . The computing platform of claim 16 , wherein the method further includes:

transmitting a fourth alert configured to indicate that the severity level cannot be determined, where a severity level cannot be determined.