Linear Innovations State Space Unobserved Components Model
Akaike's An Information Criterion
Transform a summary object into flextable
Bayesian Information Criterion
Bread Method
Extract Model Coefficients
Score Method
Model Estimation
Model Fitted Values
Multi-Step Ahead In-Sample Residuals
Model Specification
Model Log-Likelihood
Extract the Number of Observations
Object Plots
Model Prediction
Model Estimation Summary Print method
Model Diagnostics Print method
Objects exported from other packages
Model Residuals
The Standard Deviation of the model
Model Simulation
Model Estimation Summary
Walk Forward Model Backtest
Model Decomposition
Model Diagnostics
Ensembling of Models and Predictions
Model Equation (LaTeX)
Online Model Filtering
tsissm: Linear Innovations State Space Unobserved Components Model
Performance Metrics
Analytic Forecast Moments
Model Simulation Based Profiling
Model Specification Extractor
The Covariance Matrix of the Estimated Parameters
Unobserved components time series model using the linear innovations state space representation (single source of error) with choice of error distributions and option for dynamic variance. Methods for estimation using automatic differentiation, automatic model selection and ensembling, prediction, filtering, simulation and backtesting. Based on the model described in Hyndman et al (2012) <doi:10.1198/jasa.2011.tm09771>.