Anlysis and forecasting of multiple seasonal time series models

Time series may contain multiple seasonal cycles of different lengths. There are several notable features in Figure 1, reference to the hourly electricity demand in Sri Lanka, data are given in the Table lFirst, we observe that the daily cycles are not all the same, although it may reasonably be claimed that the cycles for Monday through Sunday are similar. A second feature of the data is that the underlying levels of the daily cycles may change from one week to the next, yet be highly correlated with the levels for the days immediately preceding. Thus, an effective time series model must be sufficiently flexible to capture these principal features without imposing too heavy computational or inferential burdens. The goal of this paper is to introduce a new procedure that uses innovation ARIMA models to forecast time series with multiple seasonal patterns