Model Preparation and Parameter Estimation Functions - Teradata Vantage

Teradata® VantageCloud Lake
Deployment
VantageCloud
Edition
Lake
Product
Teradata Vantage
Published
January 2023
ft:locale
en-US
ft:lastEdition
2024-12-11
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TD_ACF
Calculates the autocorrelation or autocovariance of a time series.
TD_ARIMAESTIMATE
Estimates the coefficients corresponding to an ARIMA model, and to fit a series with an existing ARIMA model.
TD_ARIMAVALIDATE
Performs in-sample forecasting for both seasonal and non-seasonal AR, MA, and ARIMA models.
TD_ARIMAXESTIMATE
Extends the capability of TD_ARIMAESTIMATE by including external regressors or covariates to an ARIMA model.
TD_AUTOARIMA
Fits the best ARIMA model to univariate time series.
TD_DIFF
Transforms stationary, seasonal, or nonstationary time series into differenced time series.
TD_LINEAR_REGR
Fits data to a curve using a formula that defines the relationship between the explanatory variable and the response variable.
TD_MULTIVAR_REGR
Creates a model that predicts the value of the dependent variable based on the values of the independent variables.
TD_PACF
Provides insight as to whether the function being modeled is stationary or not.
TD_POWERTRANSFORM
Takes a time series or spatial series and applies a power transform equation (log, invert, and so on) to the series to produce a transformed result series.
TD_SEASONALNORMALIZE
Takes a non-stationary series and normalizes the series by first dividing the series into cycles and intervals, then averaging and normalizing with respect to each interval over all cycles.
TD_SMOOTHMA
Applies a smoothing function to series producing a result series that highlights the trend associated with the series.
TD_UNDIFF
Takes in a previously-differenced series processed by TD_DIFF, and produces the original series that existed prior to the differencing.
TD_UNNORMALIZE
Reconstructs a series previously created by TD_SEASONALNORMALIZE.