Transforming Time Series Data into Capacity Planning Information

Often an analyst has time series data available from performance monitors and needs to make statistical sense of it for capacity planning purposes. For example, a twenty-four hour column chart produced by averaging multiple days of time interval samples yields a statistically stable view of a resource’s usage characteristics across the day and clearly identifies its busy period. Since monitoring tools often provide little support for this type of analysis, what can analysts do on their own to accomplish the needed data transformation? This paper describes valuable statistical manipulations and suggests approaches for capacity planning using “home grown” methods.