ANOMALY DETECTION

1 . A computing platform, comprising:

a processor; and

a memory assembly being configured to be in signal communication with the processor, and the memory assembly having encoded thereon executable control-logic instructions being configured to be executable by the processor, and the executable control-logic instructions also being configured to urge the processor to carry out a method comprising:

receiving data; and

detecting at least one anomaly contained in the data that was received.

2 . The computing platform of claim 1 , wherein the data includes time-series data.

3 . The computing platform of claim 1 , wherein the method further includes identifying, selecting, and training a statistical model, without any additional user input.

4 . The computing platform of claim 3 , wherein the statistical model includes a statistical algorithm configured to learn patterns in the data without relying on hard-coded rules for learning the patterns.

5 . The computing platform of claim 3 , wherein the method further includes altering input parameters, without user input, in an attempt to find, select and train an alternate model, where the statistical model is not identified, selected, or trained.

6 . The computing platform of claim 3 , wherein the method further includes evaluating performance of the statistical model by comparing predicted values and actual values of data.

7 . The computing platform of claim 6 , wherein the method further includes evaluating performance by comparing the predicted values and the actual values of data.

8 . The computing platform of claim 6 , wherein the method further includes using a level of uncertainty, for the statistical model, to generate a confidence interval for a prediction used for determining whether an actual value in the data is, or is not, an anomaly.

9 . The computing platform of claim 6 , wherein the method further includes determining a level of confidence by utilizing a dynamic process for selecting a level of uncertainty based on volatility in the data within a training time window.

10 . The computing platform of claim 6 , wherein the method further includes detecting anomalies in training data, in which the training data includes historical data.

11 . The computing platform of claim 10 , wherein the method further includes assisting selection of said training data.

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

detecting data anomalies in volumes of data; and

generating a model; and

determining confidence levels; and

using the statistical model to forecast volumes of the data.

13 . The computing platform of claim 10 , wherein:

the training data includes actual volumes of data extending over a training period; and

the actual volume of data, which has been received, extends during a selected time span; and

the method further includes:

determining a confidence interval; and

determining whether or not the actual volume of data is within the confidence interval.