Time Series, Dense Representations, and Sparse Representations of Temporal Data - Analytics Database - Teradata Vantage

SQL Data Manipulation Language

Deployment
VantageCloud
VantageCore
Edition
Enterprise
IntelliFlex
VMware
Product
Analytics Database
Teradata Vantage
Release Number
17.20
Published
June 2022
Language
English (United States)
Last Update
2024-04-05
dita:mapPath
pon1628111750298.ditamap
dita:ditavalPath
qkf1628213546010.ditaval
dita:id
esx1472246586715
lifecycle
latest
Product Category
Teradata Vantage™

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 characteristic properties of a time series include the following:
  • 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.
Typical business applications for time series analysis include the following:
  • 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.

The available forms of time series expansion for the EXPAND ON clause are the following.
  • 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.