TOURISM DEMAND FORECASTING MODEL USING NEURAL NETWORK

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1 TOURISM DEMAND FORECASTING MODEL USING NEURAL NETWORK Han-Chen Huang and Cheng-I Hou Department of Toursm and M.I.C.E., Chung Hua Unversty, Tawan ABSTRACT Travel agences should be able to udge the maret demand for toursm to develop sales plans accordngly. However, many travel agences lac the ablty to udge the maret demand for toursm, and thus mae rsy busness decsons. Based on the above, ths study appled the Artfcal Neural Networ combned wth the Genetc Algorthm (GA) to establsh a predcton model of ar tcet sales revenue. GA was used to determne the optmum number of nput and hdden nodes of a feedforward neural networ. The emprcal results suggested that the mean absolute relatve error(mare) of the proposed hybrd model s predcted value of ar tcet sales revenue and the actual value was 10.51%and the correlaton coeffcent was The proposed model had good predctve capablty and could provde travel agency operators wth relable and hghly effcent analyss data. KEYWORDS Artfcal neural networ;genetc algorthm;ar tcet sales;predcton model 1. INTRODUCTION There are nearly 3,500 travel agences competng n the overseas toursm maret n Tawan, wth a value of about USD21bllon per year [1]. In a compettve envronment, travel agences must be able to accurately predct the maret demand to mae the rght operatonal decsons. However, toursm s not an essentalcomponent of people s lves, and toursm can be drectly affected by economc downturns. Travel agency operators should have good predctve capablty formaret demand and excellent fnancal management capablty; otherwse, t wll bedffcultto survve n a compettve maret [2]. Among the numerous busnesses of travel agences, ar tcet salesarean extremely mportant source of revenue. If atravel agency can accurately predct the maret demand for ar tcets, t can purchase a suffcent amount of ar tcetsat alow cost to have the opportunty to get hgher sales profts. In addton, t can reduce the cost accumulaton durng the purchase process or order loss due to lac of ar tcets[3,4]. Studes nthe past have developed many sales predcton models, such as qualtatve methods(the Delph method, maret research, and the group opnon method), the sequence analyss method (exponental smoothng, autoregressve models, movng average models, etc.) andeconometrc methods (dscussng the relatonshp wth external economc varables and usng statstcal theoretcal method to measure or test the relatonshps n between some varables to provde the bass for analyss). However, these models have a number of lmtatons[5]. In recent years, the prevalent predcton method has beenartfcal ntellgence. Among the varous types of artfcal ntellgence predcton models, neural networshavebeen confrmed as a very effectve tool[6]. Therefore, ths study appled anartfcal Neural Networ (ANN) combned wth the Genetc Algorthm(GA) to establsh a predcton model forar tcet sales revenue. The fndngs of ths study couldprovde the ndustry wth a more relable and effcent reference n practcal operaton. DOI: /cst

2 2. LITERATURE REVIEW 2.1. Travel Agences Travel agencesare the medators of toursm product supplers and customers, as they are responsble for plannng and mang arrangements for tours n order to wn profts[2]. In accordance wth Tawan s Statute for the Development of Toursm[7] Artcle 2 states that a travel enterprse s: also referred to as travel agency, a proft-tang enterprse lcensed by the central admnstratve authorty to provde toursts wth arranged travel schedules, board and lodgng, tour gudes, and to purchase transportaton tcets and apply for travel documents and vsas on toursts behalf, as well as to provde related servces for remuneraton. There are nearly3,500 travel agences competng n the overseas toursm maret n Tawan, wth a value of about USD21 bllon per year[1]. In such a compettve envronment, travel agences must be able to more effcently and accurately predct maret demand to mae the rght operatonal decsons. If a travel agency can understand the maret demand earler than ts compettors, purchase products, moblze manpower and adust busness operatonal drectons n advance, t can surely wn n a fercely compettve envronment Predcton Methods Stynes[8] categorzed predcton methods nto four types: 1) the Delph technque; 2) tme seres or trend extenson models; 3) structural models; and 4) system or smulaton models. The commonly-used predcton methods proposed n other related studes [9-16] nclude trend analyss, cause analyss, udgment analyss, survey analyss, and artfcal neural networs,etc Trend Analyss The trend analyss method s used to predct the trend changes n future sales of an enterprse accordng to the hstorcal sales data by usng certan calculaton methods. Such a method s sutable for enterprses wth relatvely stable product sales. It manly ncludes the smple movng average method, the movng average method, the weghted movng average method, the exponental smoothng method and the seasonal predcton method Cause Analyss Varous factors n economc actvtes are often nterrelated, mutually nfluental,and form a certan correspondng relatonshp among each other. Product sales n general are affected by varous factors. The cause analyss method s used to fnd the functon relatonshp of varous related factors that may affect product sales and sales volume, as well as to predct future sales accordng to such a causalty relatonshp. Such a method often requres the establshment of predcton mathematcal model, and thus t s often nown as the regresson analyss method. It commonly ncludes smple regresson analyss and multple regresson analyss Judgment Analyss As a qualtatve analyss method, udgment analyss s manly based on the analyss of future maret changes accordng to the experence of management personnel, personnel wth sales experence, or other experts, n order to determne the sales trends of certan products n a certan perod of tme. 20

3 Survey Analyss The survey analyss method s used to predct the sales trends of the product of an enterprse by nvestgatng the demand and supply of a certan product and the consumpton orentaton of consumers. The survey contents may nclude product surveys, customer surveys, surveys on economc development trends and ndustry surveys, etc Artfcal Neural Networ An artfcal neural networ (ANN) s a mathematcal model mtatng the structure and functon of a bologcal neural networ. The neural networ performs calculatons usng a large amount of artfcal neurons. In most cases, ANN can change the nternal structure accordng to external nformaton as an adaptve system. ANNs are a modelng tool for non-lnear statstcal data, and they are commonly used for the modelng of complex relatonshps between nput and output or data exploraton [17]. ANN constructon s generated by the nspraton of bologcal neural networs. ANN can have human-le smple determnaton capablty and udgment, whch s advantageous to formal logc reasonng.a common multlayer feedforward networ conssts of three parts (Fgure 1)[18-20]: The nput layer, n whch numerous neurons receve a large amount of nput nformaton. The output layer, n whch nformaton s transmtted and analyzed n neuron lns to form the output results. The hdden layer, whch s a layer wth numerous neurons and lns n between the nput and the output layers. It can consst of multple layers but s customarly one layer only. There s no recognzed number of neurons n the hdden layer; however, when the number of neurons s larger, the non-lnearty wll be more sgnfcant and the robustness of the neural networ wll be more sgnfcant. Fgure 1. BPNN networ archtecture[18-20] The Bac Propagaton Neural Networ (BPNN) s the most representatve and commonly used ANN[18]. BPNN apples the steepest descent method to adust the parameters of the networ and 21

4 obtan a more accurate soluton by teraton computaton. BPNN has hgh-speed computng power, a fast recall speed, hgh learnng accuracy, and fault tolerance, and thus t has been wdely appled n dfferent felds[18-20]. 3. RESEARCH METHOD 3.1. Bac Propagaton Neural Networ BPNN s a supervsed learnng algorthm consstng of ANNs.A BPNN s the combnaton of multlayer perceptrons (MLP) and error bac propagaton (EBP). The computaton process can be dvded nto the learnng process and the recall process[21] Learnng process Step1: Set the networ archtecture parameters and learnng parameters Step2: Randomly generate the weght matrx and bas vector ntal value Step 3: Input the tranng examples, ncludng the nput values (X 1,X 2,X 3 ) and target output values (T 1,T 2,T 3 ) Step4: Compute and nfer the output values (Y 1,Y 2,Y 3 ) (1) Hdden layer (H 1,H 2,H 3 )(Eq.1 and 2) = W x net θ (1) H = 1 1+ exp( net ) (2) (2) Output layer(y 1,Y 2,Y 3 ) (Eq.3 and 4) = W h net θ (3) Y = 1 1+ exp( net ) (4) Step5: Compute the gap δ(eq.5 and 6) (1)Hdden layer δ = δ W ) h (1 H ) ( (2) Output layer δ = T Y ) Y (1 Y ) ( (5) (6) Step6: Compute weght revson and bas revson (Eq.7~10) (1) Hdden layer W ( n) = ηδ x + α W ( n 1) (7) 22

5 θ ( n) = ηδ + α θ ( n 1) (8) (2) Output layer W ( n) = ηδ H + α W ( n 1) (9) θ ( n) = ηδ + α θ ( n 1) (10) Step7: Update the weght and bas(eq.11~14) (1) Hdden layer W = W + W (11) θ θ + θ = (12) (2) Output layer W = W + W (13) θ = θ + θ Step8: Repeat Step 3- Step 7 untl convergence (no sgnfcant change n error or mplementaton of certan tmes of learnng cycles) Recall process Step 1: Set networ parameters Step 2: Read n theweght matrx and bas vector Step 3: Input the unnown data vector(x 1,X 2,X 3 ) Step 4: Compute and nfer the output vector(y 1,y 2,y 3 ) (1) Hdden layer output values (H 1,H 2,H 3 ) (Eq.15 and 16) = W X net θ (15) H 1 = 1+ exp( net ) (2)Compute and nfer the output values (Y 1,Y 2,Y 3 ) (Eq.17 and 18) = W H net θ (17) Y 1 = 1+ exp( net 3.2. Forecast Model Varables ) Accordng to the relevant lterature [13-16,22-26], ths study used thentd/usd exchange rate, the number of people travelng abroad from Tawan each month, the nternatonal ol prce, the (14) (16) (18) 23

6 Tawan stoc maret weghted ndex, Tawan s monthly montor ndcator, Tawan s monthly composte leadng ndex, Tawan s monthly composte concdent ndex, and W travel agency s monthly ar tcet sales(t-1~t-18)( Table 1) as the nput varablesto predct W travel agency s ar tcet sales revenue n Month T. The selected data were the monthly data of the perod from January 2003 to December Ths study randomly selected70%as the tranng Data,15% as the cross valdaton data, and 15% as the testng data.ga mproves the performance of ANNs by selectng the optmum nput features of the neural networ. Ths study used dfferent operators for selecton and crossover operatons (Table 2)[26-30]. Table1. Forecast model varables Varable Unt NTD/USD exchange rate (T-1 month) NTD/USD Number of people travelng abroad from Tawan each month Number of people (T-1 month) Internatonal ol prce (T-1 month) USD/Barrel Input Tawan stoc maret weghted ndex (T-1 month) Pont Tawan s monthly montor ndcator (T-1 month) Score Tawan s monthly composte leadng ndex (T-1 month) Pont Tawan s monthly composte concdent ndex (T-1 month) Pont W Travel Agency s ar tcet sales revenue(t-1 month to T-18 month) NTD Output Ar tcet sales revenue (T month) NTD Table 2. Descrpton of dfferent operators for select and crossover operatons n GA[26-30] Operaton Operator Descrpton Best Selects the best chromosome. Random Randomly selects a chromosome from the populaton. Select Tournament The wnner of each tournament (the one wth the best ftness) s selected for crossover. Top percent (15) Randomly selects a chromosome from the top 15 percent of the populaton. Roulette The chance of a chromosome gettng selected s proportonal to ts ftness. Arthmetc Lnearly combnes two parent chromosome vectors to produce two new offsprng. Heurstc Use the ftness values of the two parent chromosomes to determne the drecton of the search. Decdes (wth some probablty nown as the mxng Unform rato) whch parent wll contrbute each of the gene values Crossover n the offsprng chromosomes. Randomly selects a crossover pont wthn a chromosome, One pont nterchanges the two parent chromosomes at ths pont to produce two new offsprng. Randomly selects two crossover ponts wthn a Two pont chromosome, nterchanges the two parent chromosomes between these ponts to produce two new offsprng. 24

7 3.3. Archtecture Desgn and Model Tranng ANN s nput actvaton functon uses the hyperbolc tangent, the output error functon uses the sum-of-squares andthe output actvaton functon uses logstc. GA mproves the performance of ANNs by selectng the optmum hdden nodes of the neural networ. Ths study used dfferent operators for selecton and crossover operatons (Table 2)[26-30]. Tranng Algorthm: Quc Propagaton Algorthm, Tranng Algorthm s Parameters s Quc Propagaton Coeffcent = 1.75, Learnng Rate=0.1. The overtranng control and weghts randomzaton methodwere used to ncrease the model accuracy (Fgure 2). 4. EMPIRICAL RESULTS The correlaton (r) and Mean Absolute Relatve Error (MARE) were adopted as ndcators for evaluatng the model. Correlaton (r): As r approaches 1, the model predcted value and actual value correlaton level becomes hgher. MARE(Eq. 19): The smaller the value, the smaller the error between the forecast value and the actual value: 1 ' = n Y Y MARE 100% (19) n 1 Y where n s the number of the forecastng perods, Y s the actual value for the perod, and Y' s the forecast value for the perod. Fgure 2. Tranng optons The optmal networ archtecture s (Fgure 3 and Table 3). The nput layer had 12 neurons, the hdden layer had 142 neurons, and the output layer had one neuron. The actual value and model output value dstrbuton are shown n fgure 4. It can be learnt from the fgure that the model output value was largely dstrbuted along both sdes of the dagonal lne (Output/Target=1), ndcatng the model had good predctve capablty. The trends of the actual value and model output valueare shown n Fgure 5. It can be learnt from the fgure that the establshed ar tcet sales revenuepredcton modelhad a good capablty to reflect the change n 25

8 sales of ar tcets. The predcton results of the model are shown n Table 4. The mean absolute relatve error (MARE) was10.51%, the correlaton coeffcent was 0.913, and the model had the capablty of accurately predctng the ar tcet sales revenue. Fgure 3. Best networ archtecture search results Table 3. Performance of the Proposed Model n Predcton Select operator Crossover operator Number of nputs Number of hdden nodes MARE Correlaton Best Random Tournament Top percent (15) Roulette Arthmetc Heurstc Unform One pont Two pont Arthmetc Heurstc Unform One pont Two pont Arthmetc Heurstc Unform One pont Two pont Arthmetc Heurstc Unform One pont Two pont Arthmetc heurstc Unform One pont Two pont

9 Fgure 4. Scatter plot of actual value and model output value 5. CONCLUSION Fgure 5. Tme seres of actual vs. predcted Ths study used thebac Propagaton Neural Networ and Genetc algorthm (GA) to establsh a travel agency ar tcet sales revenue predcton model. GA was used to determne the optmum number of nput and hdden nodes of a feedforward neural networ. The emprcal results suggested that the proposed predcton modelhad the capablty to accurately predct ar tcet sales revenue and reflect the change n ar tcet sales. The MARE of the model was only 10.51%, and the correlaton coeffcent was up to , whenusng the proposed predcton model,travel agency operators can predct the future demand for ar tcets and purchase suffcent ar tcets at a lower cost to wn more profts. It could reduce the loss caused by excessve purchaseor customer loss caused by lac of stoc. 27

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