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Residual Diagnostics

Evaluate model fit and performance


inferInfer ARIMA or ARIMAX model residuals or conditional variances
inferInfer innovations of regression models with ARIMA errors
inferInfer conditional variances of conditional variance models
inferInfer vector autoregression model (VAR) innovations
inferInfer vector error-correction (VEC) model innovations

Examples and How To

Box-Jenkins Model Selection

Use the Box-Jenkins methodology to select an ARIMA model.

Check Fit of Multiplicative ARIMA Model

Conduct goodness of fit checks.

Infer Residuals for Diagnostic Checking

Infer residuals from a fitted ARIMA model.

Infer Conditional Variances and Residuals

Infer conditional variances from a fitted conditional variance model.

Time Series Regression VI: Residual Diagnostics

This example shows how to evaluate model assumptions and investigate respecification opportunities by examining the series of residuals.

Assess State-Space Model Stability Using Rolling Window Analysis

Check whether state-space model is time varying with respect to parameters.


Goodness of Fit

Goodness of fit checks can help you identify areas of model inadequacy.

Residual Diagnostics

Check residuals for normality, autocorrelation, and heteroscedasticity.

Check Predictive Performance

Learn how to check the predictive accuracy of a model.

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