Model Preparation and Parameter Estimation Functions

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.

Model Preparation and Parameter Estimation Functions - Teradata Vantage

Teradata® VantageCloud Lake