A time series is an ordered sequence of measurements of a variable that are arranged according to the time of occurrence. Time series are typically measured at some constant frequency and the data points are generally, but not necessarily, spaced at uniform time intervals.
- The data points are not independent of one another.
- The dispersion of data points varies as a function of time.
- The data frequently indicates trends.
- The data tends to be cyclic.
- Budgetary analysis
- Economic forecasting
- Inventory analysis
- Process control
- Quality control
- Sales forecasting
- Stock market analysis
- Workload projections
- Yield projections
The EXPAND ON clause enables various forms of time series expansion on a PERIOD column value of an input row by producing a set of value-equivalent rows, one for each granule in the specified time period. The number of granules is defined by the anchor name you specify for the clause.
You can expand sparse PERIOD representations of relational data into a dense representation of the same data. Data converted to a dense form can be more easily manipulated by complex analyses such as moving average calculations without having to write complex SQL requests to respond to business questions made against sparse relational data.
- Interval expansion, where rows are expanded by user-specified intervals.
- Anchor point expansion, where rows are expanded by user-specified anchored points.
- Anchor PERIOD expansion, where rows are expanded by user-specified anchored periods.