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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 0.000These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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