Model preparation and parameter estimation functions help data scientists develop forecast models.
You can apply these functions to any discrete series or matrix of data collected at equally spaced points in time or space. That is, any series whose associated indexes are discrete or any matrix whose row and column indexes are discrete.
You can build an ARIMA forecasting model only with a time series that is stationary with respect to mean, covariance, and variance. See Creating an ARIMA Model.
Functions:
- TD_ACF
- Calculates autocorrelation or autocovariance of time series.
- TD_ARIMAESTIMATE
- Performs parameter estimation for seasonal and nonseasonal auto-regressive models, moving-average models, and combined ARMA models as ARIMA models.
- TD_ARIMAVALIDATE
- Performs model validation on a series that was previously processed using TD_ARIMAESTIMATE with FIT_PERCENTAGE less than 100.
- TD_DIFF
- Applies non-seasonal, seasonal or both types of differencing to the input series to transform a non-stationary series into a stationary series.
- TD_LINEAR_REGR
- Performs a multivariate linear regression using a formula defining relationship between explanatory variable and multiple response variables to fit data to multidimensional surface. This function is typically used to support the multivariate regression with an ARIMA errors modeling strategy.
- TD_MULTIVAR_REGR
- Performs a multivariate linear regression using a formula defining relationship between explanatory variable and multiple response variables to fit data to multidimensional surface. This function is typically used to support the multivariate regression with an ARIMA errors modeling strategy.
- TD_PACF
- Measures the degree of correlation between time series sample points when the effects of the time lags have been removed.
- TD_POWERTRANSFORM
- Applies power transform to time series with nonstationary variance, creating new time series with stationary variance.
- TD_SEASONALNORMALIZE
- Normalizes the cyclic, seasonal patterns in the dataset to transform a nonstationary series into a stationary series.
- TD_SMOOTHMA
- Applies smoothing function to time series with nonstationary mean, creating new time series with stationary mean.
- TD_UNDIFF
- Reconstructs a differenced time series created by the TD_DIFF function.
- TD_UNNORMALIZE
- Reverses the seasonal normalization done by the TD_SEASONALNORMALIZE function.