Predicting daily occupancy is extremely important for the revenue management of individual hotels. However, daily occupancy can fluctuate widely and is difficult to forecast accurately based on existing forecasting methods. In this article, ensemble empirical mode decomposition (EEMD)—a novel method—is introduced, and an individual hotel is chosen to test the effectiveness of EEMD in combination with an autoregressive integrated moving average (ARIMA). Result shows that this novel method, EEMD-ARIMA, can improve forecasting accuracy compared to the popular ARIMA method, especially for short-term forecasting.
AghazadehSM (2007) Revenue forecasting models for hotel management. Journal of Business Forecasting26: 33–37.
2.
AndrewWPCranageDALeeCK (1990) Forecasting hotel occupancy rates with time series models: An empirical analysis. Hospitality Research Journal14(2): 173–182.
3.
ArcherBH (1987) Demand forecasting and estimation. In: RitchieJRBGoeldnerCR (eds) Travel, Tourism and Hospitality Research. New York: Wiley, pp.77–85.
4.
Bangwayo-SkeetePFSkeeteRW (2015) Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach. Tourism Management46: 454–464.
5.
BashaGOuardaTBMJMarpuPR (2015) Long-term projections of temperature, precipitation and soil moisture using non-stationary oscillation processes over the UAG region. International Journal of Climatology35(15): 4606–4618.
6.
BrännäsKHellströmJNordströmJ (2002) A new approach to modelling and forecasting monthly guest nights in hotels. International Journal of Forecasting18(1): 19–30.
7.
CaoFHuangZJinC. (2016) Influence of Chinese economic fluctuations on tourism efficiency in national scenic areas. Tourism Economics22(5): 884–907.
8.
ChanCKWittSFLeeYCE. (2010) Tourism forecast combination using the CUSUM technique. Tourism Management31(6): 891–897.
9.
ChenKY (2011a) Combining linear and nonlinear model in forecasting tourism demand. Expert Systems with Applications38(8): 10368–10376.
10.
ChenMH (2011b) The response of hotel performance to international tourism development and crisis events. International Journal of Hospitality Management30(1): 200–212.
11.
ChoiJG (2003) Developing an economic indicator system (a forecasting technique) for the hotel industry. International Journal of Hospitality Management22(2): 147–159.
12.
ElleroAPellegriniP (2014) Are traditional forecasting models suitable for hotels in Italian cities?International Journal of Contemporary Hospitality Management26(3): 383–400.
GunterUÖnderI (2015) Forecasting international city tourism demand for Paris: Accuracy of uni- and multivariate models employing monthly data. Tourism Management46: 123–135.
15.
HaliciogluFBlakeACortés-JiménezI (2010) An econometric analysis of the aggregate outbound tourism demand of Turkey. Tourism Economics16(1): 83–97.
16.
HeoCYLeeSMattilaA. (2013) Restaurant revenue management: Do perceived capacity scarcity and price differences matter?International Journal of Hospitality Management35(5): 316–326.
17.
HuangNEWuZ (2008) A review on Hilbert-Huang transform: Method and its applications to geophysical studies. Reviews of Geophysics46(2): 1–23.
18.
HuangNEShenZLongSR (2003) A new view of nonlinear water waves: The Hilbert spectrum. Annual Review of Fluid Mechanics31(1): 417–457.
19.
HuangNEShenZLongSR. (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences454(1971): 903–995.
20.
HuangNEWuZLongSR. (2009) On instantaneous frequency. Advances in Adaptive Data Analysis1(2): 177–229.
21.
HyndmanRJKoehlerABOrdKJ. (2008) Forecasting with exponential smoothing: The state space approach. Springer26(1): 204–205.
22.
HyndmanRJKoehlerABSnyderRD. (2002) A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting18(3): 439–454.
23.
JeffreyDBardenRDR (2000) An analysis of daily occupancy performance: A basis for effective hotel marketing. International Journal of Contemporary Hospitality Management12(3): 179–189.
24.
JiangXZhangLChenX (2014) Short-term forecasting of high-speed rail demand: a hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China. Transportation Research Part C Emerging Technologies44: 110–127.
25.
KoupriouchinaLRestJPVDSchwartzZ (2014) On revenue management and the use of occupancy forecasting error measures. International Journal of Hospitality Management41: 104–114.
26.
LaiMCYehCCShiehLF (2013) A hybrid model by empirical mode decomposition and support vector regression for tourist arrivals forecasting. Journal of Testing & Evaluation41(3): 351–358.
27.
LawRAuN (1999) A neural network model to forecast Japanese demand for travel to Hong Kong. Tourism Management20(1): 89–97.
28.
LiGSongHWittSF (2006) Time varying parameter and fixed parameter linear aids: An application to tourism demand forecasting. International Journal of Forecasting22(1): 57–71.
29.
LiYYNiuDXQiJX. (2008) A novel hybrid power load forecasting method based on ensemble empirical mode decomposition. Power System Technology32(8): 58–62.
30.
LimCChangCMcAleerM (2009) Forecasting h(m)otel guest nights in New Zealand. International Journal of Hospitality Management28(2): 228–235.
31.
LomannoMV (1995) Examining worldwide trends. Hotel & Motel Management210: 15.
32.
MakridakisSHibonM (2000) The M3-Competition: Results, conclusions and implications. International journal of forecasting16(4): 451–476.
33.
MonjolySAndréMCalifR. (2016) Hourly forecasting of global solar radiation based on multiscale decomposition methods: A hybrid approach. Energy119: 288–298.
PanBChenguang WuDSongHY (2012) Forecasting hotel room demand using search engine data. Journal of Hospitality & Tourism Technology3(3): 196–210.
36.
PeelMCAmirthanathanGEPegramGGS. (2005) Issues with the application of empirical mode decomposition[C]. In: MODSIM 2005 international congress on modelling and simulation, modelling and simulation society of Australia and New Zealand (ed RMArgent), pp.1681–1687.
37.
PengBSongHCrouchGI (2014) A meta-analysis of international tourism demand forecasting and implications for practice. Tourism Management45: 181–193.
38.
PengZKTsePWChuFL (2005) An improved Hilbert–Huang transform and its application in vibration signal analysis. Journal of Sound & Vibration286(1–2): 187–205.
39.
PlakandarasVGuptaRGogasP. (2014) Forecasting the U.S. real house price index. Economic Modelling45: 259–267.
40.
QiOLuWXinX. (2016) Monthly rainfall forecasting using EEMD-SVR based on phase-space reconstruction. Water Resources Management30(7): 2311–2325.
41.
RajopadhyeMGhaliaM BWangPP. (1999) Forecasting uncertain hotel room demand. Information Sciences132(1–4): 1–11.
42.
SchwartzZ (1999) Monitoring the accuracy of multiple occupancy forecasts. Fiu Hospitality Review17(1–2): 29–42.
43.
SchwartzZHiemstraS (1997) Improving the accuracy of hotel reservations forecasting: Curves similarity approach. Journal of Travel Research36(1): 3–14.
ShabriA (2016) A hybrid of EEMD and LSSVM-PSO model for tourist demand forecasting. Indian Journal of Science and Technology36(9): 1–6.
46.
ShenSJLiGSongH (2011) Combination forecasts of international tourism demand. Annals of Tourism Research38(1): 72–89.
47.
SongHLiG (2008) Tourism demand modelling and forecasting—a review of recent research. Tourism Management29(2): 203–220.
48.
SongHDwyerLLiG. (2012) Tourism economics research: A review and assessment. Annals of Tourism Research39(3): 1653–1682.
49.
SongHGaoBZLinVS (2013) Combining statistical and judgmental forecasts via a web-based tourism demand forecasting system. International Journal of Forecasting29(2): 295–310.
50.
SongHLiGWittSFAthanasopoulosG (2011a) Forecasting tourist arrivals using time-varying parameter structural time series models. International Journal of Forecasting27(3): 855–869.
51.
SongHLinSWittSF. (2011b) Impact of financial/economic crisis on demand for hotel rooms in Hong Kong. Tourism Management32(1): 172–186.
52.
TalluriKTVan RyzinGJ (2004) The Theory and Practice of Revenue Management. the Netherlands: Springer.
53.
TangLYuLWangS. (2012) A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting. Applied Energy93(5): 432–443.
54.
WangY (2015) The tourism demand of nonlinear combination forecasting based on time series method and WNN. International Journal of u- and e- Service, Science and Technology8(3): 301–310.
55.
WeatherfordLRKimesSE (2003) A comparison of forecasting methods for hotel revenue management. International Journal of Forecasting19(3): 401–415.
56.
WeatherfordLRKimesSEScottDA (2001) Forecasting for hotel revenue management: Testing aggregation against disaggregation. Cornell Hotel & Restaurant Administration Quarterly42(4): 53–64.
57.
WittSFWittCA (1995) Forecasting tourism demand: A review of empirical research. International Journal of Forecasting11(3): 447–475.
58.
WongKKFSongHWittSF. (2007) Tourism forecasting: To combine or not to combine?Tourism Management28(4): 1068–1078.
59.
WuZHuangNE (2004) A study of the characteristics of white noise using the empirical mode decomposition method. Proceedings of the Royal Society a Mathematical Physical & Engineering Sciences460: 1597–1611.
60.
WuZHuangNE (2009) Ensemble empirical mode decomposition: A noise-assisted data analysis method. Advances in Adaptive Data Analysis1(1): 1–41.
61.
WuZHuangNEChenXY (2009) The multi-dimensional ensemble empirical mode decomposition method. Advances in Adaptive Data Analysis1(3): 339–372.
62.
WuZHuangNELongSR. (2007) On the trend, detrending, and variability of nonlinear and nonstationary time series. Proceedings of the National Academy of Sciences of the United States of America104(38): 14889–14894.
YangYPanBSongH (2014) Predicting hotel demand using destination marketing organization’s web traffic data. Journal of Travel Research53: 433–447.
65.
YükselS (2007) An integrated forecasting approach to hotel demand. Mathematical & Computer Modelling46(7–8): 1063–1070.
66.
ZakharyAAtiyaAFEl-ShishinyH. (2011) Forecasting hotel arrivals and occupancy using Monte Carlo simulation. Journal of Revenue & Pricing Management10(4): 344–366.
67.
ZangHFanLGuoM. (2016) Short-term wind power interval forecasting based on an EEMD-RT-RVM model. Advances in Meteorology2016(3): 1–10.
