Forecasting Hotel Arrivals and Occupancy

Abstract presage hotel arrivals and occupancy is an important component in hotel tax wariness systems. In this paper we propose snip serial approach for the arrivals and occupancy prediction problem. In this approach we simulate the hotel reservations process forward in clock. A key step for the faithful emulation of the reservations process is the accurate friendship of its parameters. We propose an approach for the estimation of these parameters from the historical entropy. We considered as a reason submit the problem of portent room demand for the Ganjali core Hotel, Baku, Azerbaijan.The proposed exemplification gives satisfactory result. 1. Introduction forebode in the hotel industry is very recyclable for estimating or calculating a variety of factors that tail assist management in strategic decision making. Given the perishable nature of touristry services, in that location exists an important need to obtain accurate anticipates of future stock activity (Ar cher, 1987 Athiyaman & Robertson, 1992). Certainly, figureing plays a crucial role in tourism planning both in the short and the long run. However, from a merely practical point of view, tourism industry is much much interested in getting impregnable predictions in the short-term.Needs in the hospitality, assault and accommodation sectors discombobulate become more short-term in focus, and they can change rapidly with changing market demand. Therefore, increasing the accuracy of short-term forecasts is an essential requirement to improve the managerial, operational, and tactical decision-making process especi solelyy in the clubby sector. Because of the thumping exit of existing hotels, any possible improvement in the methodology pass on amount to potenti each(prenominal)y very large oerall savings. In recent years there has been rapid egress in the inflow of tourists to Azerbaijan.Declaring 2011 the Year of Tourism in the country has unresolved up new opportunities f or further exploitation in this field. In a modernizing Azerbaijan construction of hotel complexes, a high level of service has become widespread. The number of hotels in Azerbaijan is growing every year as the number of outside tourists visiting the country. In 2002, the country had 70 hotels and hotel complexes, and visiting tourists were just over 800 thousand, now number over 500 hotels, and tourists more than half a million. According to the Ministry of Culture and Tourism 40 new hotels is currently low construction in Azerbaijan. right away, the hotel fund of the republic consists of 31 thousand places against 9000 in 2002. As a result of the state program of tourism development up to 2016 capacity of hotels and divagation aras should be increased to 150 thousand. At the present date in Baku, mainly the business -and congress tourism is developed, which p prowessicipants are only five-star hotels. Therefore in Baku there is a lack of hotels class three or four stars wi th reasonable prices and good service. Ministry of Culture and Tourism of Azerbaijan is preparing a special offer for construction in the city hotels of different categories.There are currently functioning in Azerbaijan 499 hotels and hotel-type facilities. 312 of them conk out on the basis of licenses issued by the Ministry of Culture and Tourism, and 187 is illegal. Overall, about 80 hotels and facilities for recreation in the country have received the category of star. Today in Azerbaijan exist 17 five-star hotels, 13 of them in Baku. 34 four-star hotels, 21 of which are hardened in Baku, the rest in the regions. Of the 27 three-star hotel 18 is in any case located in Baku. In addition the republic has 6 ii-star hotels.From five-star hotels the most popular are The Boutique Palace Hotel, Hilton Baku, Excelsior Hotel Baku, direful Hotel Europe Baku, Palace Hotel Baku and another(prenominal)s, among four-star hotels The Ganjali midpoint Hotel, Riviera Hotel Baku, Austin Hotel Baku, Ramada Baku, Hyatt Regency Baku, etc. , three-star hotels sea Port Hotel, Sun Rise Hotel Baku, Metropol the Hotel, Azcot Hotel, two-star hotels Baku Palace knobhouse, Baleva, Royal Guest House Baku, and finally one star hotel Nur-2 hotel. So as we exculpate research establish on data about The Ganjali eye Hotel, should brushup it further.In 2008 industrial commercial company Ganjali completed and put into operation the hotel Ganjali shoes. The hotel is centrally located opposite the boulevard, within walking distance of the citys attractions and shops. The area of 4000 sqm hotel. An eight-story construct with elevators there is. The hotel Ganjali Plaza handed comfortable accommodation to services of lodgers. This 4-star hotel is located in Baku city centre, a 10-minute walk from the Old townspeople district and the Heydar Aliyev Palace. The Ganjali Plaza Baku offers free Wi-Fi and elegant interiors.The classic-style dwell at the Ganjali Plaza Hotel feature stylish wooden furniture and floors. All rooms are air-conditioned and accommodate satellite TV and a private bathroom with bath. Breakfast is provided each morning at the Ganjali Plaza. Guests are also welcome to relax in the bar with its rich wooden and field glass furnishings, or in the fitness room. Reception at the Ganjali Plaza is turn out 24/7, and includes a tour desk and ticket service. Shuttle services and auto rental are also available. We considered as a case news report the problem of forecasting room demand for Ganjali Hotel, Baku, Azerbaijan. . Methodology In this government issue the data set is discussed and the magazine serial publication stupefys employ in this study are briefly explained. The data set Figure 1 The cadence series plot of the monthly guest arrivals data from January 2011 to December 2011. The beat series plot from January 2011 to December 2011 consisting of a total of 12 monthly observations is understandn in Figure 1. From the plot, it is clear that tourist arrivals has generally increased and reduced conviction by time and obviously it is not a stationary time series. There also appears to have most sort of seasonal worker pattern in it.There are also both(prenominal) unexpected dips and some events may have contributed to a drop in guest arrivals at these points of time. 3. 1. Time series In statistics, signal processing, econometrics and mathematical finance, atime seriesis a sequence ofdata points, measured at back-to-back times. Time seriesanalysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecastingis the use of amodelto predict future values based on previously observed values.Time series are very oftentimes plotted vialine charts. Inevenly spacedtime series, the time intervals between data points are all equal, while inunequally spacedtime series the intervals differ. Time series data have a natural te mporal ordering. This makes time series analysis perspicuous from other common data analysis problems, in which there is no natural ordering of the observations. A time series model will generally reflect the fact that observations close together in time will be more closely related than observations further apart.In addition, time series models will often make use of the natural one-way ordering of time so that values for a given bound will be expressed as deriving in some way from past values, rather than from future values. Time-Series Behaviors * Trend * Seasonality * Cycles * Ir regular variations * hit-or-miss variation * Trend Trend A long-term upward or downward movement in data. * Population shifts * Changing income Seasonality Short-term, fairly regular variations related to the calendar or time of day. Restaurants, service call centers, and theaters all experience seasonal demand CycleWavelike variations terminationing more than one year. These are often related to a variety of economic, political, or even rural conditions Random Variation Residual variation that remains after all other behaviors have been accounted for Irregular variation Due to unusual band that do not reflect typical behavior * Labor sorb * Weather event 3. 2. Time-Series Forecasting Averaging These Techniques work best when a series tends to vary about an honest. Averaging techniques smooth variations in the data They can plough step changes or gradual changes in the level of a series Techniques Moving average * Weighted contemptible average * exponential function smoothing afterward I will give a brief overview of techniques used in this study in the rich and rapidly growing field of time series modeling and analysis. 3. 3. Moving Average or Smoothing Techniques Inherent in the collection of data taken over time is some go of stochastic variation. There exist methods for reducing of canceling the effect due to random variation. An often-used technique in industry is smoothing. This technique, when properly applied, reveals more clearly the profound trend, seasonal and cyclic components.There are two distinct groups of smoothing methods Averaging Methods Exponential Smoothing Methods Exponential Smoothing is a very popular scheme to produce a smoothed Time Series. Whereas in Single Moving Averages the past observations are burden equally, Exponential Smoothing assigns exponentially decreasing encumbrances as the observation get older. In other words, recent observations are given relatively more weight in forecasting than the older observations. In the case of moving averages, the weights charge to the observations are the same and are equal to 1/N.In exponential smoothing, however, there are one or more smoothing parameters to be influenced (or estimated) and these choices determine the weights assigned to the observations. 3. The Ganjali Plaza Hotel Case Study We applied the proposed forecasting model to the problem of forecasting the arrivals and the occupancy of the Ganjali Plaza Hotel, Baku, Azerbaijan, as a flesh out case study. In collaboration with the hotel, we apply our proposed forecasting model to the hotels data. The Hotel Data We have applied the proposed forecasting model on this data of the Ganjali Plaza Hotel. 2011 months Occupancy(Person) jan 137 feb 108 ar 186 apr 117 may 104 jun 143 jul 149 aug 157 kinfolk 166 oct 142 nov 129 dec 130 Table1. The Ganjali Plaza Hotel monthly guest arrivals data from January 2011 to December 2011. We have obtained a full(a) set of data covering the period from jan-2011 until dec-2011. The set of the data include only the reservations. We forecast a month ahead apply the last twelve months of the data. In next section we present the results of our study. 4. 4. Results Figure2. shows the moving average model for the case of one month forecasting. Figure2. The Ganjali Plaza Hotel occupancy forecast for January 2012 using Moving Average method. 011 year occupancy(p erson) Moving Average old-hat deviation jan 137 N/A N/A feb 108 N/A N/A mar 186 143. 6666667 N/A apr 117 137 N/A may 104 135. 6666667 32. 63377028 jun 143 121. 3333333 24. 98147462 jul 149 132 24. 22961151 aug 157 149. 6666667 16. 45420131 sep 166 157. 3333333 11. 8023852 oct 142 155 9. 964752696 nov 129 145. 6666667 13. 189502 dec 130 133. 6666667 12. 3857744 jan 129. 5 9. 852617625 Table2. The Ganjali Plaza Hotel occupancy forecast for January 2012 using Moving Average method. Spreadsheet Showing Results exploitation n = 3.Figure3. represents the exponential smoothing model for the case of one month forecasting. Figure3. The Ganjali Plaza Hotel occupancy forecast for January 2012 using Exponential Smoothing method. 2011 year occupancy(person) Exponential Smoothing jan 137 N/A N/A feb 108 137 N/A mar 186 125. 4 N/A apr 117 149. 64 N/A may 104 136. 584 43. 12280758 jun 143 123. 5504 43. 96758903 jul 149 131. 33024 28. 89852128 aug 157 138. 398144 24. 16763962 sep 166 145. 8388864 18. 58795672 oct 142 153. 9033318 18. 83896641 nov 129 149. 1419991 17. 6449977 dec 130 141. 0851995 17. 83124541 jan 136. 6511197 14. 94736414 damping factor=0. 6 Table3. The Ganjali Plaza Hotel occupancy forecast for January 2012 using Exponential Smoothing method. Spreadsheet Showing Results Using w = 0. 4(damping factor=1-w=0. 6). 4. ending In this paper we have proposed model for hotel arrivals and occupancy forecasting using time series method. We considered as a case study the Ganjali Plaza Hotel of Baku, Azerbaijan. The proposed forecasting model achieves good forecasting accuracy and beats other competing forecasting models.In other words, it estimates the whole picture of what will happen in the future for all processes, and in a probabilistic way. Table 1. and Figure1. show the different seasonal periods for the Ganjali Plaza Hotel, as determined by the managers. We used these data to determine the forecast for January 2012 occupancy rate. For this purpose we imple mented two techniques of time series methodology such as exponential smoothing and moving average method. References 1. Brockwell, P. J. and Davis, R. A. (2002), Introduction To Time Series And Forecasting, 2nd Edition, Springer-Verlag, New York. 2.Andrawis, R. , Atiya, A. F. , 2009. A new Bayesian formulation for Holts exponential smoothing. diary of Forecasting 28, 218234. 3. Andrew, W. , Cranage, D. , Lee, C. , 1990. 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