The goal of cts is to fit continuous time autoregressive models with the Kalman filter. See Wang (2013) https://www.jstatsoft.org/article/view/v053i05.
You can install the development version of cts from GitHub with:
# install.packages("devtools")
devtools::install_github("zhuwang46/cts")This is a basic example which shows you how to solve a common problem:
library(cts)
#>
#> Attaching package: 'cts'
#> The following objects are masked from 'package:stats':
#>
#> spectrum, tsdiag
## basic example code
data(V22174)
fit <- car(V22174,scale=0.2,order=7, ctrl=car_control(trace=FALSE))
summary(fit)
#>
#> Call:
#> car(x = V22174, scale = 0.2, order = 7, ctrl = car_control(trace = FALSE))
#>
#> Order of model = 7, sigma^2 = 1.37e-09
#>
#> Estimated coefficients (standard errors):
#> phi_1 phi_2 phi_3 phi_4 phi_5 phi_6 phi_7
#> coef -0.501 0.355 0.085 -0.022 0.605 -0.371 0.483
#> S.E. 0.108 0.111 0.060 0.071 0.084 0.124 0.112
#>
#> Estimated mean (standard error):
#> [1] 0.173
#> [1] 0.022
AIC(fit)
#>
#> Call:
#> car(x = V22174, scale = 0.2, order = 7, ctrl = car_control(trace = FALSE))
#>
#> Model selection statistics
#>
#> order t.statistic AIC
#> 1 -4.77 -20.78
#> 2 -4.45 -38.57
#> 3 3.25 -47.15
#> 4 2.37 -50.76
#> 5 6.11 -86.05
#> 6 -0.76 -84.63
#> 7 4.32 -101.27
factab(fit)
#>
#> Call:
#> factab(object = fit)
#>
#> Characteristic root of original parameterization in alpha
#>
#> 1 2 3 4 5
#> -0.006+0.058i -0.006-0.058i -0.029+0.300i -0.029-0.300i -0.030+0.135i
#> 6 7
#> -0.006+0.058i -0.006-0.058i
#>
#> Frequency
#>
#> 1 2 3 4 5 6 7
#> 0.009 0.009 0.048 0.048 0.022 0.022 0.000