扩展的时间序列过程
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.
- 完全自动化的时间序列模型的拟合. »
- 基于如 AIC、BIC、AICc 和 SBC 等标准的自动模型选择.
- 包括了残差白噪声评估的拟合诊断的完整套件.
- 能够对拟合指定包括 SARIMA 和 GARC 的各种不同模型系列.
- 包括了自回归条件异方差（autoregressive conditionally heteroscedastic）和广义自回归条件异方差（generalized ARCH）的新的时间序列过程.
- 对向量 AR、MA、ARMA、ARIMA、SARMA 和 SARIMA 过程的完整支持.
- 包括了谱估计的时间序列过程的新的估计方法.
- 包括了条件最大似然法和最大似然法的改良的时间序列过程估计方法.
- 支持非零均值函数的时间序列模型.
- 支持包括非平稳的初始值的时间序列.
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 »
