The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

rollcast

rollcast implements Rollcast, a probabilistic time-series forecasting framework based on adaptive mixtures of rolling statistical anchors.

Main ideas

The model uses rolling mean, median, minimum, maximum, regression endpoint, and configurable quantiles as predictive anchors. A proper-score softmax gate learns state-dependent mixture weights. State-conditional residuals can be added with tunable strength, and recursive simulation returns full predictive distributions.

Install from source

install.packages("Rcpp")
install.packages("rollcast_0.1.0.tar.gz", repos = NULL, type = "source")

For development from the package directory:

install.packages(c("Rcpp", "testthat", "knitr", "rmarkdown"))

Then run:

R CMD build rollcast
R CMD check --as-cran rollcast_0.1.0.tar.gz

Basic use

library(rollcast)

set.seed(1)
y <- 100 + cumsum(rnorm(400))

fit <- rollcast(
  y,
  window = 60,
  tau = 0.25,
  lambda = 0.01,
  conditional_k = 40,
  state_bw = 1,
  residual_bw = 0.35,
  error_scale = 0.25,
  residual_smoothing = 0.03,
  rho_min = 0.05,
  rho_max = 0.90,
  rho_decay = 1
)

pred <- predict(fit, horizon = 20, nsim = 3000, seed = 123)
plot(pred)

A numeric hyperparameter supplied as a scalar is fixed. A candidate vector requests causal validation-based tuning.

These 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.
Health stats visible at Monitor.