Processos de Séries Temporais Expandidos
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.
- Aproximação em modelos de séries temporais totalmente automatizada. »
- Seleção de modelos automática, baseada em critérios como AIC, BIC, AICc, e SBC.
- Conjunto completo de diagnósticos de ajuste, incluindo a avaliação de brancura de resíduos.
- Habilidade para especificar vários modelos diferentes para ajuste, incluindo SARIMA e GARCH.
- Novos processos de séries temporais, incluindo ARCH (autorregressivo condicionalmente heteroscedástico) e GARCH (ARCH generalizado).
- Total suporte para processos AR, MA, ARMA, ARIMA, SARMA e SARIMA vetoriais.
- Novos métodos de estimação para processos de séries temporais, incluindo estimativa espectral.
- Melhoria dos métodos de estimativa para processos de séries temporais, incluindo verossimilhança máxima condicional e verossimilhança máxima.
- Suporte para modelos de séries temporais com função média diferente de zero.
- Suporte para séries temporais com valores iniciais, incluindo não estacionários.
Related Examples
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 »
FUNÇÕES RELACIONADASTimeSeriesModelFit 
ARCHProcess GARCHProcess ARProcess MAProcess ARMAProcess SARMAProcess ARIMAProcess SARIMAProcess FARIMAProcess EstimatedProcess TimeSeriesForecast CovarianceFunction CorrelationFunction PowerSpectralDensity EstimatedProcess
ARCHProcess GARCHProcess ARProcess MAProcess ARMAProcess SARMAProcess ARIMAProcess SARIMAProcess FARIMAProcess EstimatedProcess TimeSeriesForecast CovarianceFunction CorrelationFunction PowerSpectralDensity EstimatedProcessVEJA TAMBÉM NOVO NA VERSÃO 10
