Anomaly Detection and Prediction | TO THE NEW Blog
While traditional statistical tools have always been a prior step in evaluating the data, the application of analytics and machine learning has become increasingly important to get predictions. Anomaly detection relies on series of data science algorithms that can unveil the outliers in the key KPI metrics and can alert the concerned teams to take necessary actions. The foremost issue lies in the reliability of these assets as these are production based servers and their failure can lead to loss of tremendous amount of money and low brand values. The methods used are as follows: The approach that was used to move ahead with the project was the FbProphet toolkit as this algorithm nicely handles different seasonality parameters like monthly or yearly, and it has native support for all-time series metrics. The best feature lies in that it gives us a flexibility to define customized intervals and make predictions and boundaries for future time frames that can be validated against the real-time data to generate alerts as per our needs.
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