Model Preparation and Parameter Estimation Functions - Teradata VantageCloud Lake
Lake - Analyze Your Data with ClearScape Analytics™
- Deployment
- VantageCloud
- Edition
- Lake
- Product
- Teradata VantageCloud Lake
- Release Number
- Published
- February 2025
- ft:locale
- en-US
- ft:lastEdition
- 2026-02-20
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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_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.