Alphabetical List of Functions

This section provides details on all time series functions, including syntax, licensing prerequisites, parameter descriptions, usage, IMSL library mapping, examples, and standards/compatibility information.

• TS_ARMA_AR Function [Aggregate]
  Calculates the least-square estimates of parameters for an autoregressive moving average (ARMA) model, and returns the requested autoregressive estimate.
• TS_ARMA_CONST Function [Aggregate]
  Calculates the least-square estimates of parameters for an autoregressive moving average (ARMA) model, and returns an estimated constant.
• TS_ARMA_MA Function [Aggregate]
  Calculates the least-square estimates of parameters for an autoregressive moving average (ARMA) model, and returns the requested moving average estimate.
• TS_AUTOCORRELATION Function [Aggregate]
  Calculates the sample autocorrelation function of a stationary time series.
• TS_AUTO_ARIMA Function [Aggregate]
  Determines parameters of a multiplicative seasonal autoregressive integrated moving average (ARIMA) model, and produces forecasts that incorporate the effects of outliers that have effects that persist beyond the end of the series.
• TS_AUTO_ARIMA_OUTLIER Function [Aggregate]
  TS_AUTO_ARIMA_OUTLIER accepts an input time series and automatically determines the parameters of a multiplicative seasonal autoregressive integrated moving average (ARIMA) model. Whereas TS_AUTO_ARIMA uses the ARIMA model to forecast values beyond the set of inputs,TS_AUTO_ARIMA_OUTLIER uses the ARIMA model to identify statistical outliers in the input time series, and returns the outlier type of each one.
• TS_AUTO_ARIMA_RESULT_AIC Function [Scalar]
  A supporting function for the TS_AUTO_ARIMA function. Retrieves the Akaike's Information Criterion (AIC) output parameter produced by TS_AUTO_ARIMA.
• TS_AUTO_ARIMA_RESULT_AICC [Scalar]
  A supporting function for TS_AUTO_ARIMA. Retrieves the corrected AIC (AICC) output parameter produced by TS_AUTO_ARIMA.
• TS_AUTO_ARIMA_RESULT_BIC Function [Scalar]
  A supporting function for TS_AUTO_ARIMA. Retrieves the Bayesian Information Criterion (BIC) output parameter produced by TS_AUTO_ARIMA.
• TS_AUTO_ARIMA_RESULT_FORECAST_ERROR Function [Scalar]
  A supporting function for TS_AUTO_ARIMA. Retrieves the forecasted standard error values for the original input series produced by TS_AUTO_ARIMA.
• TS_AUTO_ARIMA_RESULT_FORECAST_VALUE Function [Scalar]
  A supporting function for TS_AUTO_ARIMA. Retrieves the forecasted values for the requested outlier free series produced by TS_AUTO_ARIMA.
• TS_AUTO_ARIMA_RESULT_MODEL_P function [Scalar]
  A supporting function for the TS_AUTO_ARIMA. Retrieves the p value produced by TS_AUTO_ARIMA when computing the ARIMA model description.
• TS_AUTO_ARIMA_RESULT_MODEL_Q Function [Scalar]
  A supporting function for the TS_AUTO_ARIMA. Retrieves the q value produced by TS_AUTO_ARIMA when computing the ARIMA model description.
• TS_AUTO_ARIMA_RESULT_MODEL_S Function [Scalar]
  A supporting function for the TS_AUTO_ARIMA. Retrieves the s value produced by TS_AUTO_ARIMA when computing the ARIMA model description.
• TS_AUTO_ARIMA_RESULT_MODEL_D Function [Scalar]
  A supporting function for the TS_AUTO_ARIMA function. Retrieves the d value produced by TS_AUTO_ARIMA when computing the ARIMA model description.
• TS_AUTO_ARIMA_RESULT_RESIDUAL_SIGMA Function [Scalar]
  A supporting function for the TS_AUTO_ARIMA. Retrieves the residual standard error of the outlier-free data points.
• TS_AUTO_UNI_AR Function [Aggregate]
  Performs automatic selection and fitting of a univariate autoregressive time series model.
• TS_BOX_COX_XFORM Function [Aggregate]
  Performs a forward or inverse Box-Cox power transformation.
• TS_DIFFERENCE Function [Aggregate]
  Differences a seasonal or nonseasonal time series.
• TS_DOUBLE_ARRAY Function [Scalar]
  A supporting function for TS_GARCH. Constructs a logical array consisting of 3 – 10 constant double-precision floating point values, and returns a single varbinary value.
• TS_ESTIMATE_MISSING Function [Aggregate]
  Estimates the missing values in a time series and returns them as a new time series, interspersed with the original time series.
• TS_GARCH Function [Aggregate]
  Used to analyze and forecast volatility in time series data. TS_GARCH computes the estimates of the parameters of a GARCH(p, q) model. GARCH (generalized autoregressive conditional heteroskedasticity) is a generalized model of ARCH; the ARCH computation relates the error variance to the square of a previous period's error.
• TS_GARCH_RESULT_A Function [Scalar]
  A supporting function for TS_GARCH. Retrieves the log-likelihood output parameter, A, produced by the TS_GARCH aggregate function.
• TS_GARCH_RESULT_AIC Function [Scalar]
  A supporting function for TS_GARCH. Retrieves the Akaike Information Criterion output parameter, AIC, produced by the TS_GARCH aggregate function.
• TS_GARCH_RESULT_USER [Scalar]
  A supporting function for TS_GARCH. Accesses each element in the logical array that describes the GARCH(p,q) model.
• TS_INT_ARRAY Function [Scalar]
  A supporting function for TS_AUTO_ARIMA and TS_AUTO_ARIMA_OUTLIER.
• TS_LACK_OF_FIT Function [Aggregate]
  Performs the lack-of-fit test for a univariate time series or transfer function, given the appropriate correlation function.
• TS_LACK_OF_FIT_P Function [Aggregate]
  Performs the lack-of-fit test for a univariate time series. This function is identical to TS_LACK_OF_FIT, except that TS_LACK_OF_FIT_P returns the p-value of q, rather than returning q.
• TS_MAX_ARMA_AR Function [Aggregate]
  Calculates the exact maximum likelihood estimation of the parameters in a univariate ARMA (autoregressive moving average) time series model, and returns the requested autoregressive estimate.
• TS_MAX_ARMA_CONST Function [Aggregate]
  Calculates the exact maximum likelihood estimation of the parameters in a univariate ARMA (autoregressive moving average) time series model, and returns the constant estimate.
• TS_MAX_ARMA_LIKELIHOOD Function [Aggregate]
  Calculates the exact maximum likelihood estimation of the parameters in a univariate ARMA (autoregressive moving average) time series model, and returns likelihood value (ln) for the fitted model.
• TS_MAX_ARMA_MA Function [Aggregate]
  Calculates the exact maximum likelihood estimation of the parameters in a univariate ARMA (autoregressive moving average) time series model, and returns the requested moving average estimate.
• TS_OUTLIER_IDENTIFICATION Function [Aggregate]
  Detects and determines outliers and simultaneously estimates the model parameters in a time series where the underlying outlier-free series follows a general seasonal or non-seasonal ARMA model.
• TS_PARTIAL_AUTOCORRELATION Function [Aggregate]
  Calculates the sample partial autocorrelation function of a stationary time series.
• TS_VWAP Function [Aggregate]
  VWAP stands for volume-weighted average price. TS_VWAP calculates the ratio of the value traded to the total volume traded over a particular time horizon. VWAP is a measure of the average price of a stock over a defined trading horizon. You can use TS_VWAP as both a simple and an OLAP-style aggregate function. Unlike the other time series functions, TS_VWAP does not call the IMSL libraries.