Time Series Analysis with State Space Model
Univariate AR Model Fitting
Calculate Characteristics of Scalar ARMA Model
Scalar ARMA Model Fitting
Scalar ARMA Model Fitting
Box-Cox Transformation
Cross-Covariance and Cross-Correlation
Compute a Periodogram via FFT
Kullback-Leibler Information
Decomposition of Time Interval to Stationary Subintervals
Estimation of the Change Point
The Least Squares Method via Householder Transformation
Yule-Walker Method of Fitting Multivariate AR Model
Least Squares Method for Multivariate AR Model
Cross Spectra and Power Contribution
Simulation by Non-Gaussian State Space Model
Non-Gaussian Smoothing
Probability Density Function
Compute a Periodogram
Particle Filtering and Smoothing
Particle Filtering and Smoothing for Nonlinear State-Space Model
Plot Box-Cox Transformed Data
Plot Fitted Trigonometric Polynomial
Plot Smoothed Density Function
Plot Fitted Polynomial Trend
Plot Trend, Seasonal and AR Components
Plot Simulated Data Generated by State Space Model
Plot Posterior Distribution of Smoother
Plot Smoothed Periodogram
Plot Trend and Residuals
Plot Evolutionary Power Spectra Obtained by Time Varying AR Model
Polynomial Regression Model
Seasonal Adjustment
Simulation by Gaussian State Space Model
Trend Estimation
Prediction and Interpolation of Time Series
Time Series Analysis with State Space Model
Time Varying Coefficients AR Model
Evolutionary Power Spectra by Time Varying AR Model
Time Varying Variance
Autocovariance and Autocorrelation
Functions for statistical analysis, modeling and simulation of time series with state space model, based on the methodology in Kitagawa (2020, ISBN: 978-0-367-18733-0).