Forecasting time-series which contain multiple seasonal patterns requires flexible modelling approaches, and the need for continuously updating models emphasises the importance of fast model estimation. In response to shortcomings in current models, a new model is proposed which brings the desirable qualities of speed, flexibility and support for exogenous regressors into a state space model. This proposed model also introduces state switching, which captures groups of irregular multiple seasonality by switching between states. The functionality of the proposed model extends beyond forecasting, by allowing for model based time-series decomposition, imputation of missing values, and support for streaming data.This model is available as an R package (mitchelloharawild/fasster), which provides formula based model specification, and uses tidy data structures (tsibble) and APIs which will later become familiar in forecast's next iteration: tidyforecast.