Computer storage capacity forecasting system using cluster-based seasonality analysis
US7783510B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 25, 2007 |
| Grant date | Aug 24, 2010 |
| Priority date | — |
| Expiry date | Nov 11, 2028 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q30/0202
- WIPO fieldIT methods for management
- WIPO sectorElectrical engineering
Abstract
A methodology for automatic a priori data pattern analysis is provided. Described methods allow consistent and objective determination of outliers; trend; seasonality; and level shifts; and the production of better models and more accurate forecasts. In addition, a two-step way to automatically determine seasonality and locate possible events in the data set is described. Decomposition of data into seasonal, trend and level components; detection of outliers and level-shift events in the time series based on statistical analysis of the time series; detection of seasonality based on statistical analysis of clusters of data, known as cluster-based seasonality analysis, or CBSA; evaluation of the goodness of fit of a model to data, using the existing goodness of fit indicator, R2; and seasonality analysis, using a sequence of cluster-based seasonality analysis (CBSA) and Fourier analysis are described.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.