Seasonal data may display different distributions throughout the period of seasonality. We fit this type of model by determiningthe appropriate change points of the distribution and fitting parameters to each interval. This offers the added benefit of searching for disjoint regimes, which may denote the samedistribution occurring nonconsecutively. Our algorithm outperforms SARIMA for prediction.