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Expanded Time Series Processes

Expanded Time Series Processes in Mathematica 10

Mathematica Version 10 now includes fully automated fitting and diagnostics across the full suite of time series processes, making time series modeling an everyday exploratory tool. Time series modeling has also been deepened to include ARCH and GARCH processes, as well as vector-valued versions of standard time series models. The full time series model framework has been greatly enhanced, including simulation, estimation, and property computations.

  • Fully automated time series model fitting. »
  • Automatic model selection, based on criteria such as AIC, BIC, AICc, and SBC.
  • Full suite of fitting diagnostics, including assessing of whiteness of residuals.
  • Ability to specify several different model families for fitting, including SARIMA and GARCH.
  • New time series processes, including ARCH (autoregressive conditionally heteroscedastic) and GARCH (generalized ARCH).
  • Full support for vector AR, MA, ARMA, ARIMA, SARMA, and SARIMA processes.
  • New estimation methods for time series processes, including spectral estimation.
  • Improved estimation methods for time series processes, including conditional maximum likelihood and maximum likelihood.
  • Support for time series models with nonzero mean function.
  • Support for time series with initial values, including nonstationary.

Decide Time Series Process Family and Order from Data Automatically »
Identify Conditional Heteroscedacity »
Airline Passengers »
Constrain the Model Selection Set »

Use Different Criteria for Model Selection »
Investigate Time Series Model Residuals »
Study Significance of Parameters in Fitted Model »
Use Fitted Model to Forecast Time Series »

Model Multiple Exchange Rates »
Vector Joint Model versus Univariate Component Models »
Use Vector Models with Multiple Strongly Correlated Time Series »
Vector Autoregressive Process as Discretized Vector Ornstein–Uhlenbeck Process »

Time Series Processes with Nonzero Mean »
Time Series Processes with Initial Conditions »
Study Non-weakly Stationary Autoregressive Process »
Compute Expectation of Autoregressive Moving-Average Process from Its Definition »

Use Spectral Estimator to Find FARIMA Parameters »
Volatility Clustering in a GARCH Process »
Slice Distribution of GARCH(1,1) »
Moments of GARCH(1,1) »

Model the Conditional Value at Risk with an ARCH Process »
Expanded Estimation Methods »
Broad Performance Improvements »