The Sparsity-Ranked Lasso (SRL) for Time Series implemented in srlTS efficiently fits long, high-frequency time series with complex seasonality, even with a high-dimensional exogenous feature set.
Originally described in Peterson and Cavanaugh (2022) in the context of variable selection with interactions and/or polynomials, ranked sparsity is a philosophy of variable selection in the presence of prior informational asymmetry.
In time series data with complex seasonality or exogenous features; see Peterson and Cavanaugh (2023+), which also describes this package in greater detail. The basic premise is to utilize the sparsity-ranked lasso to be less skeptical of more recent lags, and suspected seasonal relationships.
You can install the development version of srlTS like so:
Or, install from CRAN with:
This is a basic example.
library(srlTS)
y <- cumsum(rnorm(100))
fit <- srlTS(y, gamma = c(0, .5))
fit
#> PF_gamma best_AICc best_BIC
#> 0.0 209.9610 216.3429
#> 0.5 208.1509 214.5327
#>
#> Test-set prediction accuracy
#> rmse rsq mae
#> AIC 1.518106 0.9478941 1.286608
#> BIC 1.518106 0.9478941 1.286608To learn more and to see this methodology in action, see: