Forecasting a daily time series with varying seasonalities: An application to daily visitors to farmland in Korea

An accurate forecast of a daily time series often plays an important role in many managerial and industrial decisions related to production planning, scheduling and control. The fluctuations in daily time series are affected not only by the quarterly, monthly and weekly seasonalities, but also by both solar and lunar holidays in Asian culture. However, because the holidays make the seasonal factor irregular any single traditional seasonal model fails to describe the complicated relations among the various seasonalities and the changing solar and lunar holiday effects. In this study, we develop a daily index which incorporates into a single measure the effects of quarterly, monthly and weekly seasonalities and the effects of holidays. A time series model is also developed to forecast a daily time series by using the developed daily index with an application to the daily number of visitors to a large public amusement park in Korea. The results of the proposed model's application are compared with those of the ARIMA model and the regression model. Copyright (C) 1996 Elsevier Science Ltd