msarima: Multi-Seasonal ARIMA Models

Fit, forecast and simulate autoregressive integrated moving average (ARIMA) models with more than one seasonal component, following Lisi and Grigoletto (2025) doi:10.1002/for.3283 . Series observed at high frequency often display several seasonal cycles at once, such as the daily and the weekly pattern of hourly data, which a single seasonal ARIMA operator cannot represent. Estimation is performed by conditional maximum likelihood through numerical optimisation, with support for fixed coefficients and a constant term. Multi-seasonal Ljung-Box and Monti portmanteau tests, following Grigoletto and Lisi (2026) doi:10.48550/arXiv.2609.32381 , seasonal correlograms, forecasts with prediction intervals, and simulation from a model specification or from a fitted model are also provided.

See also

Useful links:

• https://github.com/danigiro/msarima

• https://danigiro.github.io/msarima

• Report bugs at https://github.com/danigiro/msarima/issues

Author

Maintainer: Daniele Girolimetto daniele.girolimetto@unipd.it (ORCID) [copyright holder]

Authors:

• Francesco Lisi francesco.lisi@unipd.it (ORCID)

• Matteo Grigoletto matteo.grigoletto@unipd.it (ORCID)