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

Cited 1 time in webofscience Cited 0 time in scopus
  • Hit : 394
  • Download : 63
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
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
1996-07
Language
ENG
Article Type
Article
Citation

COMPUTERS INDUSTRIAL ENGINEERING, v.30, no.3, pp.365 - 373

ISSN
0360-8352
URI
http://hdl.handle.net/10203/4104
Appears in Collection
KGSM-Journal Papers(저널논문)
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 1 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0