Forecasting time-series with correlated seasonality

A new approach to forecasting seasonal data is proposed where seasonal terms can be updated using the most recent relevant information. It was developed to handle features encountered in hourly electricity load data and daily hospital admissions data. The associated state space model is estimated with methods adapted from exponential smoothing, although the Kalman filter may also be used. It nests existing seasonal models and outperforms them over a range of prediction horizons on the data. The approach is likely to be useful in a wide range of applications involving both high and low frequency data.