The intent of this paper is to find the most suited technique to bring forth the prognosis of demand of ecotourism in Ba'Kelalan country in Sarawak. The manners understudied are based on Univariate Modeling Techniques such as NaA?ve with Trend Model, Average Change Model, Exponential Smoothing and Holt 's Method Model. These theoretical accounts are normally used to find the short-run prognosis for a short-run information. The public presentations of the theoretical accounts are validated by retaining the part of the yearly observation of the tourer reachings. For growing, the touristry industry requires comprehensive planning. Demand analysis could supply a utile tool in planning and in supplying guidelines for the development of the industry. The one-year information set includes the entire figure of stability of consumer reachings to the finish from twelvemonth 2000 until 2010 and it is divided quarterly.
Keywords: Demand of ecotourism ; Univariate Modeling Techniques ; Forecasting Model, Root Mean Square Error ( RMSE )
Tourism industry is really of import to the economic system and it has been identified as one of the of import sector in Malaysia which contributes to our economic growing. Therefore, serious attending should be given in analyzing the factor that affect tourer reachings particularly international tourer in our state. Because touristry is one of the major beginnings to our economic system, The Ministry of Culture, Arts and Tourism was established in the twelvemonth 1987 and has been upgraded into the Ministry of Tourism in 2004.
he Malayan authorities besides allocated an sum of fund to the touristry industry every bit good as supplying sufficient basic substructure. In the twelvemonth 2006, touristry Malaysia has received 30 % more support for advertisement and other publicities in readying for the Visit Malaysia Year which was held in 2007 ( Government Malaysia, 2006 ) .
As a effect to this issue, ecotourism has become more popular in our state hence, the thought to advance Ba'Kelalan as one of the tourer finish in Malaysia. The thought to do Ba'Kelalan as the finish for tourers is because of the scenery and the beauty of the topographic point itself and as one manner to back up the Visit Malaysia Year which is imposed by our authorities under the touristry ministry to derive more for our economic system. In twelvemonth 2007, Borneo Jungle Safari ( BJS ) which is a touristry Centre owned by one of the Ba'Kelalan indigen has promoted Ba'Kelalan as one of the tourer finish through their one-year event, Apple Fiesta and it has been broadcasted in our mass media and telecasting channel such as Majalah 3 ( TV3 ) , Panorama ( TV1 ) , Talian Hayat ( NTV7 ) and Sarapan Dimana ( Astro Prima ) .
Located in the north-east backwoods of Sarawak which is known as the Northern Highlands in Limbang Division, Ba'Kelalan belongs to the Lun Bawang folk which is categorized in the Orang Ulu group in Sarawak. Ba'Kelalan is a group of nine small towns located about 3000 pess above sea degree and 4 kilometres from the boundary line of East Kalimantan, Indonesia. The small towns are named as Buduk Nur, Long Langai, Long Lemutut,
Long Ritan, Long Rusu, Buduk Bui, Long Rangat, Buduk Aru, and Pa'Tawing.
The name of Ba'Kelalan is derived from the Kelalan River and Ba ' which brings the significance moisture land in the Lun Bawang languages. Its population is about 1500 nevertheless it is home to 8000 peoples as they have fallen in love with the topographic point and be given to come and see this topographic point many times. In the cool mountain clime which is an norm of 24A°C, temperate fruits such as apples, Citrus reticulata oranges, strawberry, grape, and vanilla are grown. Ba'Kelalan besides produces rice and mountain salt is obtained from the nearby hills.
Tourist reaching to this topographic point has increased in recent old ages. Nowadays, Ba'Kelalan is available with a 9-hole golf class, and the colony is besides the easiest point to entree for sing Kayan Mentarang National Park ( KMNP ) which is located in Krayan, East-Kaliamantan, Indonesia. Ba'Kelalan itself is located near the country of Pulong Tau National Park ( PTNP ) .
The chief activity of the people of Ba'Kelalan is farming. They works moisture Paddy as their primary agriculture activities. However, Ba'Kelalan is good known as the lone topographic point in Sarawak which produces apples. Ba'Kelalan apples has become the icon of Ba'Kelalan and the the Lun Bawang who live in that country today.
What makes this topographic point go more familiar among the tourer is the civilization of the Lun Bawang people and the one-year Apple Feast which is held in May every twelvemonth. Another event is the Escaped of The Hearth of Borneo, 4WD, and the Home stay plan. These events attract more tourers to come and see this topographic point. Hence, this will assist to increase the income of the people in Ba'Kelalan.
Definition of Forecasting
Prediction is defined as the anticipation of future events based on known past values of relevant variables ( Makridakis, S. , Wheelright, S. C. & A ; Hyndman, R. J. , 1998 ) . In other words, prediction is conveying the significance of the procedure of analysing current and historical informations to find future tendencies. Normally, economic experts use calculating techniques in order to find future economic tendencies. Univariate Modelling Techniques are methods for analysing informations on a individual variable at a clip. Examples of Univariate Modelling Techniques are the NaA?ve Models, Methods of Average, the Exponential Smoothing Techniques and the Box-Jenkins Methodology. Both Double Exponential Smoothing and Holt 's Method illustrated in this survey are classified in the Exponential Smoothing Techniques.
Tourism is one of the major contributing sectors for Malaysia 's economic growing for several old ages. Year by twelvemonth, the figure of international tourer reachings to Malaysia shows an upward tendency which supports the state 's political stableness, aside from several plan has and bundles introduced by the Malayan authorities to promote international tourer to see Malaysia.
This subdivision explains the most relevant features on the demand of ecotourism in Ba'Kelalan, Sarawak. The volume, composing and recent development of tourer flows are investigated by utilizing the figure of reachings of tourers for the period between 2000-2010. The purpose of this work is to look into
the tourer demand which includes the international and local tourer. Based on the information collected it was observed that the reaching of international tourers from United Kingdom, Indonesia, Brunei, Australia and Hong Kong are ranked top in sing Ba'Kelalan during the forecasted clip period.
Table 1: Tourist reachings to Ba'Kelalan ( 2000-2010 )
No. Of Tourist
Beginnings: Visitor Books of Ba'Kelalan and Borneo Jungle Safari ( BJS )
Table 1 shows the tourer reachings to Ba'Kelalan during the period of 2000 until 2010, it can be seen that the figure of tourers rose annually from 2000 until 2010. In fact, tourers increased between 2000 and 2002 but there was a 2-year diminution is observed in 2003 and 2004. The world-wide SARS epidemic in twelvemonth 2003 may explicate the lessening of tourer reaching to the survey country.
Over the past 3 decennaries, we have seen many surveies in calculating international touristry demand done by both touristry research workers and practicians. Basically, the literature on patterning and calculating touristry demand is immense with assorted types of empirical analysis. Some of the research workers apply cross-sectional informations. However, most of them use the method of calculating to analyse touristry demand.
The increasing involvement in surveies on touristry demand has been motivated by the rapid growing of the touristry industry across the universe as the economic system tends to be stable in these decennaries. The last four decennaries have seen great developments in touristry demand analysis, in footings of the diverseness of research involvements, the deepness of theoretical foundations, and progresss in research methodological analysiss. Modeling of touristry demand in order to analyse the effects of assorted determiners, and accurate prediction of future touristry demand, are fundamentally two major focal points of touristry demand surveies.
A Time-series theoretical account as we know explains a variable with respect to its ain yesteryear and a random perturbation term. Particular attending should be paid to researching the historic tendencies and forms such as seasonality of the clip series involved, and to foretell the hereafter of this series based on the tendencies and forms identified in the theoretical account. Since clip series theoretical accounts merely require historical observations of a variable, it is less dearly-won in informations aggregation and theoretical account appraisal.
The major constructs and step of touristry demand or touristry visits can take topographic point for assorted grounds such as vacations, concern trips, visits to friends and relations ( VFR ) , conferences, and pilgrim's journeies and so on. In other words, the term 'tourism demand ' can be defined for a peculiar finish as the measure of the touristry merchandise ( Hong Kong Tourism Demand Forecasting System ) . It is refers to the combination of touristry goods and services that the consumer is willing to pay during the specific clip under the given set of conditions. The clip period can be a month, a one-fourth or a twelvemonth. Hence, the conditions that relate to the measure of touristry demanded is including the touristry monetary values for the finish such as the tourers ' cost of populating in their finish and travel costs to the finish. Besides that, the handiness of the
touristry monetary values for viing or utility finishs, possible consumers ' incomes, advertisement outgo, gustatory sensations of consumers in the beginning states, and other societal, cultural, geographical and political factors can be taken into history in order to mensurate the value of touristry demand.
It is due to being one of the of import countries in touristry research that touristry demand patterning and prediction has attracted much attending of both faculty members and practicians as mentioned before. Harmonizing to a comprehensive reappraisal by Li et Al ( 2005 ) in his diary, 420 surveies on these subjects were published during the period of 1960-2002. The bulks of these surveies focus on the application of different techniques, both qualitative and quantitative, to pattern and calculate the demand for touristry in assorted finishs. These surveies besides have attempted to set up prediction rules that could be used to steer the practicians in choosing prediction techniques. However, this attempt failed. As Witt and Song ( 2000 ) and Li et Al ( 2005 ) concluded that the public presentation of the prediction theoretical accounts varies harmonizing to the information frequences used in the theoretical account appraisal, the destination-origin state or part braces under consideration and the length of the prediction skylines concerned. There has non been a solution for touristry demand prediction.
Several Numberss of articles reappraisals on touristry demand calculating have been published over the last few decennaries. These include Crouch ( 1994 ) , Li et Al ( 2005 ) , Lim ( 1997a, 1997b and 1999 ) and Witt and Witt ( 1995 ) . These reappraisals cover the surveies published largely during the period of 1960-2000. Although a few surveies published between 2000 and 2004 are included in the latest reappraisal of Li et Al ( 2005 ) , the focal point of that reappraisal was on the econometric attack merely. This paper does non try to double the attempts made by old research workers. This paper will supply demand patterning and prediction, including clip series theoretical accounts utilizing the univariate mold such as naA?ve prognosis, mean alteration theoretical account, exponential smoothing and Holt 's Method which take into history to happen the smallest RMSE. This is as the consequence to happen which theoretical account is accurate to calculate the demand of touristry.
To measure the best theoretical account, it will take into history the comparing of consequences of each method through the possible standards such as the six common 1s are the average square mistake ( ME ) , the average absolute mistake ( MAE ) , the average per centum mistake ( MPE ) , the average absolute mistake ( MAE ) , the average square mistake ( MSE ) , and the root mean square mistake ( RMSE ) . Basically, ME and MPE are non frequently used as steps of prognosis truth because of big positive mistakes which can offsetted by big negative mistake. The other steps, MAE, MAPE, MSE, and RMSE are the best comparison option to compare the prediction theoretical accounts for a given series ( J. Holton, Barry Keating, John Galt 2009 ) . However,
usually we tend to concentrate on RMSE to measure the truth of assorted calculating method because it is easy for most people to construe due to its similarity to the basic statistical construct of standard divergence. Hence, it is one of the most normally used steps of prognosis truth.
Data and Methodology
This subdivision describes briefly on the statistical techniques applied to analyse the informations collected from the Visitor Book in Ba'Kelalan and Borneo Jungle Safari. Univariate Modelling Technique was applied to foretell the hereafter values of demand on ecotourism based on past observations given in the times series, by suiting a theoretical account to the informations.
The sum of tourer reaching to the survey country is collected from the twelvemonth 2000 until 2010. These informations were used to find the suited theoretical account to suit the information. Time series calculating analysis and prognosis theoretical accounts were applied to foretell the demand of ecotourism in Ba'Kelalan by utilizing these four types of prognosis theoretical accounts such as NaA?ve with Trend Mode, Average Change Model, Exponential Smoothing and Holt 's Method.
NaA?ve with Trend Model
This theoretical account implies that all future prognosis can be set to be the existent observed value in the most recent clip period. NaA?ve prognosiss assume that recent periods are the best forecasters of the hereafter ( John E. Hanke and Dean W.Wichern, 2009 ) . The value Yt/Yt-1 measures the tendency. If Yt is greater than Yt-1 so the tendency is upward and likewise if Yt is the existent value in clip t1 and Yt-1 is the existent value in the preceding clip period. The one measure in front to calculate is represented as,
( 1 )
Yt is the existent value in clip T, and
Yt-1 is the existent value in the preceding clip period.
This theoretical account is extremely sensitive to the alterations in the existent values. A crisp addition or sudden bead in the values will badly impact the prognosis. In add-on, suiting this theoretical account type will ensue in the loss of the first two observations in the series. Besides, this theoretical account is merely suited to be used for the short clip series.
Average Change Model
The mean alteration theoretical account or usually known as moving norm is based on the premiss that the prognosis value is equal to the existent value in the current period plus the norm of the absolute alterations experienced up to that point in clip. The one measure in front prognosis is given as ;
( 2 )
This theoretical account is utile when the historical informations that we are traveling to analyse are characterized by period to period alterations that are about of the same size. However, this theoretical account tends to dawdle behind turning points and that all the period is weighted every bit, irrespective of their importance, when deducing the prognosis value.
Equation below gives the simple moving mean prognosis. A moving norm of order K, MA ( K ) , is computed by ;
( 3 )
Ft+1 = the prognosis value for the following period
Crosstalk = the existent value at period T
K = the figure of footings in the moving norm
Simple exponential smoothing every bit
good as traveling mean utilizations merely the past values of a clip series to calculate future values of the same series and is decently employed when there is no tendency or seasonality nowadays in the information. The purpose is to gauge the current degree whereby this degree estimation is so used as the prognosis of future values. Exponential smoothing continually revises the estimation in the visible radiation of more-recent experiences. This method is based on averaging or smoothed the past values of a series in the diminishing exponential mode. This means that the most recent observation receives the largest weight, I± ( where 0 & lt ; I± & lt ; 1 ) , the following observation by ( 1 - I± ) 2I± , and so on. The figure we choose for I± is called the degree of smoothing changeless. Therefore,
( 4 )
More officially, the equation is as below ;
( 5 )
= Forecast value for period t+1
I± = Smoothing invariable ( 0 & lt ; I± & lt ; 1 )
Crosstalk = Actual value now ( in period T )
Ft = Forecast ( eg: smoothed ) value for period T
As a usher in taking I± , select value which is close to 0 if the series has a great trade of random fluctuation. If we wish the prognosis values to depend strongly on recent alterations in the existent values we should choose the value which is close to 1. The root mean squared mistake ( RMSE ) is frequently used as the standard for delegating an appropriate smoothing invariable ; the smoothing invariables which give the smallest RMSE would be selected as the theoretical account which provides the smallest mistake in bring forthing the extra prognosiss ( Wilson, Barry and John Galt 2009 ) . Basically, the little values of I± by and large work best when simple exponential smoothing is the most appropriate theoretical account.
Holt 's Method
This theoretical account is the extension of the smoothing theoretical accounts. Holt 's Method sometimes called dual exponential smoothing, is a technique to smooth the tendency and the incline straight by utilizing the different smoothing invariables. It besides provides more flexibleness in choosing the parametric quantity value which the tendency and the inclines are tracked. Three equations and two smoothing invariable are used in this theoretical account. The Holt 's Method equations are represented as follows:
Exponential smoothed series:
( 6 )
( 7 )
Therefore, the one measure in front prognosis is:
( 8 )
= Smoothed value for period t+1
I± = Smoothing invariable for the degree ( 0 & lt ; I± & lt ; 1 )
Crosstalk = Actual value now ( in period T )
Ft = Forecast ( eg. Smoothed ) value for the period T
= Trend estimation
I? = Smoothing invariable for the tendency estimation ( 0 & lt ; I? & lt ; 1 )
m = Number of periods in front to be forecast
= Holt 's prognosis value for period T + m
Analysis and Consequences
Univariate prediction theoretical account was used to foretell the demand of ecotourism for the first one-fourth of 2011. The appraisal was done utilizing the first one-fourth of 2000 to
the 4th one-fourth of 2010. Measure of Root Mean Square Error ( RMSE ) is used to mensurate the difference between the prognosis and the existent values in the station estimated period. ( Mentzer and Kahn, 1995 ; Witt and Witt 1992 ; Armstrong 2001 ) . We chose to compare the value of RMSE among the prediction theoretical account because measures the differences between values predicted by a theoretical account or an calculator and the values really observed from the point being modeled or estimated. Since the RMSE is a good step of truth, it is ideal if it is little.
The truth of the prediction theoretical accounts over figure periods is evaluated so that we can place which theoretical account by and large works the best. Among the standards which were taken into history were average mistake ( ME ) , the average absolute per centum mistake ( MAE ) , the average per centum mistake ( MPE ) , the average absolute mistake ( MAE ) , the average absolute per centum mistake ( MAPE ) , the average square mistake ( MSE ) , and the root mean square mistake ( RMSE ) .
Throughout this survey, we focused on root mean square mistake ( RMSE ) to measure the comparative truth of assorted calculating method. The RMSE is calculated as follows:
( 9 )
At = Actual value in period T
Ft = Forecast value in period T
n = Number of periods used in the computation
Choice of Model
Table 3 represents the sum-ups and comparing on root mean square mistake ( RMSE ) figures for NaA?ve with Forecast, Average Change Model, Exponential Smoothing, and Holt 's Method. On the footing of the size of RMSE calculated over the clip period, it can be concluded that the most suited theoretical account to calculate the demand of ecotourism is Average Change Model since it has the smallest value of RMSE ( 29.85 ) compared to other prediction technique.
The Average Change Model is the best theoretical account to calculate this information because of several grounds. Average Change Model is used to calculate as it reduces the consequence of impermanent fluctuations in informations, better the tantrum of informations to a line whereby it is called a smoothing procedure to demo the information 's tendency more clearly, and highlight any value above or below the tendency.
Table 3: RMSE Valuess by type of theoretical account
Type of theoretical account
Average Change Model
Holt 's Method
I± =0.11, I?=1.00
The first method which is NaA?ve with Trend Model is done utilizing Microsoft Excel whereby all the informations are analyzed through appraisal of the prognosis value. The simplest naA?ve prediction theoretical account, in which the prognosis value is equal to the old ascertained value, can be described in algebraic signifier as follows:
Ft = At-1 ( 10 )
Where Ft represents the prognosis value for clip period T and At-1 represent the ascertained value on period earlier ( t-1 ) . However, we might reason that in add-on to sing merely the most recent observation, it would do sense to see the way from which we arrived at the latest observation. If the
series is dropped to the latest point, possibly it is sensible to presume some farther bead. Alternatively, if we merely observed an addition, it may do sense to factor into our prognosis some farther addition. This can be adjusted or can be made in the 2nd naA?ve prediction theoretical account, which includes some proportion of the most late observed rate of alteration in the series. In general algebraic footings the theoretical account is as follows:
Ft = At-1 + ( At-1 - At-2 ) ( 11 )
Where Ft is the prognosis for period T, At-1 is the existent observation at period t-1, At-2 is the ascertained value at period t-2.
So, to calculate the demand of ecotourism in Ba'Kelalan for the first one-fourth of 2011 ( denoted as BKM0111 ) , we take the ascertained value for the 4th one-fourth of 2010 ( BKM0410 ) and adjust it by including some information from the most recent tendency. Therefore, the prognosis for BKM0111 harmonizing to the equation ( 11 ) is:
BKM0111 = BKM0410 + ( BKM0410 - BKM0310 )
Table 4: NaA?ve Forecast 2 Method
No. Of Arrival
NaA?ve Trend Forecast
% Absolute Error
The computation is as follows:
BKM0111 = 532 + ( 532-743 )
= 532 + ( -211 )
In this survey, NaA?ve Forecast 1 and NaA?ve Forecast 2 have been obtained to happen the better value of RMSE. As a consequence, it is found that NaA?ve Forecast 1 is better comparison to NaA?ve Forecast 2 as the RMSE is lower which is 123.60 and 196.65 severally.
The mean alteration theoretical account is another method that has applied in this survey which represents the mean of all the informations to calculate. The term traveling norm is used to depict this attack. Basically, three- and five one-fourth traveling mean are normally applied in this attack. As to calculate the demand of ecotourism in Ba'Kelalan, three- and five-moving norm has been done to place which tendency is the best to foretell the flow of tourer to this topographic point in the hereafter. The application is similar to the naA?ve prognosis measure nevertheless we have to cipher the mean of the norm as follows harmonizing to the equation ( 2 ) .
Table 5: Average Change Model
5 Qtr MA
Forecast 5 Qtr MA
Absolute % Mistake
By utilizing the 5 one-fourth traveling norm, the prognosis for the tourer demand for the one-fourth for 2011 ( BKM0111 ) is:
BKM0111 = 400 + 708 + 767 + 743 + 532 = 3150 = 630
In this survey, five-quarter moving norm is the best compared to the three-quarter moving mean whereby the RMSE for three-quarter moving norm is 49.52 compared to the five-quarter moving norm which has the smallest RMSE with 29.85.
For the exponential smoothing method, it can be done by utilizing Microsoft Excel every bit good as the naA?ve and mean alteration theoretical account. However, it is easier to analyse it utilizing the Eviews. We can analyse the information utilizing this method by ciphering or calculate it utilizing the equation ( 5 ) . Below is the consequence RMSE of the Exponential Smoothing Method utilizing Eviews:
Table 6: Exponential Smoothing
Sum of Squared Residuals
Root Mean Squared Error
By utilizing the degree smoothing changeless which is alpha ( I± ) we can continue to calculate utilizing this method. With exponential smoothing, the prognosis value at any clip is a leaden norm of all the available old values. We found that the RMSE of this method is 110.51.
As for the Holt 's method, it can besides be done utilizing the Eviews which consequence is as follows:
Table 7: Holt 's Method
Method: Holt-Winter No Seasonal
Sum of Squared Residuals
Root Mean Squared Error
From the consequence of analysis, Holt 's method with I± = 0.11 and I? = 1, it ensuing RMSE with 97.50. Advantage by utilizing this method is that the recent observations are given comparatively more weight in calculating the than the older observations.
Based on the one measure in front forecast analysis, Average Change theoretical account is the most suited method for calculating the quarterly demand of ecotourism. Each theoretical account type has a alone feature which fits to a peculiar information series. More forecasting techniques should be explored to guarantee the fittingness to longer series of demand on ecotourism. Univariate Modelling Technique is fundamentally a individual variable theoretical account that uses past information as the footing to bring forth the prognosis values. This is made on the premise that the prognosis values are dependent entirely on the past form of the information series.
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