Automatic Structural Time Series Models
Create dates to interpolate
autostsm: Automatic Structural Time Series Models
Build a block diagonal matrix from two matrices
Build the date sequence as a Date type
Data check for input exo
Data check for input exo.fc
Data check for input y
Set the inequality constraints for estimation
Cox-Stuart Test
Detect Anomalies
Detect Structural Breaks
Detect cycle from the data
Detect frequency and dates from the data
Detect if log transformation is best
Detect seasonality from the data
Detect trend type
Trend cycle seasonal decomposition using the Kalman filter.
Kalman Filter
Fixed parameter setting
Kalman Filter and Forecast
Format exo
Get initial parameter estimates for estimation
Missing Value Imputation by Kalman Smoothing and State Space Models
Return a naive model prior decomposition
State space model
Automatic model selection for structural time series decomposition into trend, cycle, and seasonal components, plus optionality for structural interpolation, using the Kalman filter. Koopman, Siem Jan and Marius Ooms (2012) "Forecasting Economic Time Series Using Unobserved Components Time Series Models" <doi:10.1093/oxfordhb/9780195398649.013.0006>. Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" <doi:10.7551/mitpress/6444.001.0001><http://econ.korea.ac.kr/~cjkim/>.