muse - Multiple Unobserved Sources of Error

R

Python

muse is an R package implementing the PTS (Power / Trend / Seasonal) state-space family of models for time-series analysis and for forecasting. The estimation engine is written in C++ (Rcpp & RcppArmadillo), wrapping a Kalman filter/smoother around a Multiple Source of Error (MSOE) model whose components are selected analogously to the ETS taxonomy.

The package shares conventions with smooth and greybox: the returned object inherits from c("pts", "smooth") so generics such as forecast(), accuracy(), plot(), AIC(), and BIC() work out of the box.

Installation

The package is not yet on CRAN. Install the development version directly from GitHub:

# install.packages("remotes")
remotes::install_github("config-i1/muse")

Once released on CRAN:

install.packages("muse")

What's inside

The user-facing entry point is pts(). In a single call it can:

• Specify a PTS model with a compact three-character string "PTS" — Power / Trend / Seasonal in that order (e.g. "ZZZ", "0LT", "0.5GD")
  • P — power (Box-Cox λ): a numeric value or "Z" to estimate it jointly with the state-space parameters.
  • T — trend: N none, L local, D damped, G global, or Z for automatic selection.
  • S — seasonal: N none, D discrete, T trigonometric (harmonic seasonality), or Z for automatic selection.
• Auto-select the best model by AIC / BIC / AICc / BICc on the irregular component (select = TRUE or Z letters in the model string).
• Estimate jointly with ARMA / SARMA noise on the irregular component (arma = c(p, q) or a list).
• Handle missing values in the response series — they are filtered through the Kalman recursion natively, no imputation required.
• Detect outliers of type AO (additive), LS (level shift), and SC (slope change) during estimation via outliers = "use" with a user-supplied confidence level (e.g. 0.99). Detected outliers are reported in the fitted object and added as dummy regressors.
• Take external regressors through the data argument (a data.frame / ts / matrix whose first column is the response).
• Produce forecasts with prediction, confidence, or simulated intervals through forecast().
• Simulate trajectories from the fitted model with simulate() (in-sample replay from the initial state, forward simulation for forecasts).
• Hold out and score automatically via holdout = TRUE plus h = ....

The full method list includes print, summary, plot, coef, vcov, confint, sigma, nobs, nparam, logLik, AIC, BIC, fitted, residuals, rstandard, rstudent, pointLik, accuracy, actuals, modelType, lags, orders, errorType, outlierdummy, and update.

Quick start

library(muse)

# Fit a model with automatic Box-Cox, trend, seasonal,
# holding out the last 12 observations and forecasting them back.
model <- pts(AirPassengers, model = "ZZZ", h = 12,
             holdout = TRUE, ic = "AICc")
summary(model)
plot(forecast(model, h = 12))

# With engine-side outlier detection at the 99% confidence level
model_out <- pts(AirPassengers, model = "ZZZ", h = 12,
                 outliers = "use", level = 0.99)
model_out$outliersDetected

Reporting issues

Bug reports and feature requests are welcome at the issue tracker.

Acknowledgements

Parts of the C++ engine refactoring and the Python translation of the package were developed with the assistance of Anthropic's Claude. Responsibility for the code and its correctness rests with the package authors.