TD_BINARYSERIESOP Function | Teradata Vantage - TD_BINARYSERIESOP - Teradata Vantage

Database Unbounded Array Framework Time Series Functions

Deployment
VantageCloud
VantageCore
Edition
Enterprise
IntelliFlex
VMware
Product
Teradata Vantage
Release Number
17.20
Published
June 2022
Language
English (United States)
Last Update
2024-10-04
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TD_BINARYSERIESOP performs a point-wise mathematical operation on two time series of equal size. The first input is referred to as the series 1 (primary input). The second input is referred to as series 2 (secondary input). The mathematical operation is addition, subtraction, multiplication, or division.

Common uses of TD_BINARYSERIESOP are:
  • Subtracting trends from a time series to create a model from it.
  • Restoring trends to a time series before using the model for forecasting.
  • Using the function as a building block to formulate more complex functions.
In time series data, a trend is a long-term pattern in the data that changes gradually over time. Time series datatrends for various reasons, including the following:
  • Seasonality: Data has periodic trends due to seasonal effects, such as sales of winter clothing during colder months.
  • Economic cycles: Data trends as the economy goes through cycles of growth and contraction. For example, the demand for certain goods and services decrease during a recession.
  • Population changes: Population changes cause trends in time series data. For example, if the population of a city is increasing, the demand for housing may also increase.
  • Natural disasters: Data changes during natural disasters. For example, a hurricane or earthquake can cause a sudden drop in sales or production, followed by a recovery period.

Unwanted trends may represent noise or other factors that interfere with the analysis of a wanted trend. Unwanted trends make it difficult to detect and analyse the underlying pattern in the data, and may need to be removed or accounted for. For example, a stock analyst may need to remove the effect of daily fluctuations in stock prices to analyse the long-term trend of the stock's performance.

Removing unwanted trends from a time series before analysis has the following advantages bu improving:
  • Forecasting accuracy
  • Pattern identification
  • Model stability
The following procedure is an example of how to use TD_BINARYSERIESOP to convolve two series with digital signal processing:
  1. Use TD_DFFT on series 1 (primary input) and series 2 (secondary input) to two ARTs named dfftRes1 and dfftRes2, respectively.
  2. Use TD_BINARYSERIESOP to do point-wise multiplication using the two result sets,dfftRes1 and dfftRes2, and place the result in an ART named freqRes.
  3. Use TD_IDFFT on freqRes to get the convolved result of the two series.