Developing tourism demand forecasting models using machine learning techniques with trend, seasonal, and cyclic components

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1 BALKAN JOURNAL OF ELECTRICAL & COMPUTER ENGINEERING, 05, Vol.3, No. 4 Developg toursm demad forecastg models usg mache learg techques wth tred, seasoal, ad cyclc compoets S. Cakurt ad A. Subas Abstract Ths paper proposes the determstc geerato of auxlary varables, whch outle the seasoal, cyclc ad tred compoets of the tme seres assocated wth toursm demad for the mache learg models. To test the cotrbuto of the determstcally geerated auxlary varables, we have employed multlayer perceptro (MLP) regresso, ad support vector regresso (SVR) models, whch are the well-kow stateof- art mache learg models. These models are used to make multvarate toursm forecastg for Turkey respected to two data sets: raw data set ad data set wth determstcally geerated auxlary varables. The forecastg performaces are compared regards to these two data sets. I terms of relatve absolute error (RAE) ad root relatve squared error (RRSE) measuremets, the proposed mache learg models have acheved sgfcatly better forecastg accuracy whe the auxlary varables have bee employed. Idex Terms Toursm Demad Forecastg, Multvarate Forecastg, Seasoal Tme Seres, Auxlary Varable, Artfcal Neural Network, Multlayer Perceptro, Support Vector Regresso. I. INTRODUCTION N toursm demad forecastg, the quattatve forecastg I models ca be classfed to three broad categores: tmeseres, ecoometrc techques, ad artfcal tellgece (AI) methods ad ts sub or terrelated dscples lke soft computg, mache learg, ad data mg. Itellgece techques whch are predomatly derved from artfcal eural etwork (ANN), support vector mache, fuzzy logc, geetc algorthms, swarm tellgece, have emerged the toursm forecastg lterature []. Forecastg stuatos vary wdely ther tme horzos volved, o how well we selected the factors that cotrbute S. CANKURT s wth the Departmet of Iformato Techologes, Iteratoal Burch Uversty, Sarajevo, Bosa ad Herzegova (e-mal: [email protected]). A. SUBASI s wth the Departmet of Iformato Techologes, Iteratoal Burch Uversty, Sarajevo, Bosa ad Herzegova (e-mal: [email protected]). the relatoshp the model, types of data patters, ad may other aspects []. Most of the toursm demad tme seres very ofte exhbt patters terms of the seasoal, cyclc ad tred compoets. Seasoalty s oe of the most mportat patters, whch s mostly observed toursm demad tme seres. Therefore, t s oe of the cosderatos to develop tme seres models. Some of these tme seres models are autoregressve tegrated movg average (ARIMA), seasoal ARIMA (SARIMA), Holt-Wters models. These tradtoal statstcal models requre elmatg the effect of seasoalty from a tme seres before makg forecastg [3]. For example, Holt's model s developed for the tme seres whch there s ether tred or seasoalty, Brow's expoetal smoothg model s sutable for tme seres whch there s a lear tred but o seasoalty ad Wters addtve ad multplcatve models work well for tme seres both wth a lear tred ad a seasoal effect. I toursm demad aalyss, dealg wth the seasoal fluctuatos of toursm data has always bee a mportat ad complex ssue. I the toursm demad forecastg taxoomy, seasoalty ca be faced ether as a determstc compoet or a stochastc compoet the tme seres. If seasoalty s cosdered as stochastc, we may hadle seasoalty by usg deseasoalzato or dfferecg approaches to make the seasoal adjustmet ad to remove the seasoal effect from the data. The, we are able to apply these tradtoal statstcal models to tme seres wth seasoal compoet. If the seasoalty s regarded as determstc, troducg seasoal dummy varables to the tme-seres models would be suffcet to deal wth the seasoal varatos []. O the other had, mache learg, whch s a mportat area of artfcal tellgece, has bee successfully appled to may forecastg applcatos cludg the toursm demad forecastg. Artfcal eural etwork (ANN) ad support vector mache (SVM) are the most wdely used state-of-art mache learg methods for forecastg. These models have bee mmerged the feld of the toursm demad forecastg. I dfferet studes [3], ablty of mache learg models recogzg ad learg the seasoal patters wthout Copyrght BAJECE ISSN: 47-84X February 05 Vol:3 No:

2 BALKAN JOURNAL OF ELECTRICAL & COMPUTER ENGINEERING, 05, Vol.3, No. 43 removg them from the raw data s show. However, the lterature, effects of the seasoal, tred ad cyclc patters are ot dscussed for the mache learg models as much vestgated as for the tme seres models. Nevertheless, there are some attempts to develop techques to deal wth the patters the use of mache learg models order to mprove the accuracy of models. For example, the study held by Hll et al. [4], a cocept of deseasoalzg seasoal tme seres s used. They have used the deseasoalzed data geerated by the movg-to-rato-average method to put a eural etwork model. Wag ad Leu [5] proposed a hybrd model, whch s a recurret eural etwork traed by features extracted from ARIMA aalyses, to forecast the md-term prce tred of the Tawa stock exchage. That emprcal study showed that the eural etwork traed by dffereced data acheved better predctos tha oe traed by raw data [6]. Tseg et al. proposed a hybrd forecastg model, whch combes the seasoal tme seres ARIMA (SARIMA) ad the eural etwork back propagato (BP) models to forecast tme seres wth seasoalty [6]. They have used Hll et al. [4] s cocept of deseasoalzg a seasoal tme seres to formulate a eural etwork model ad Wag ad Leu [5] s approach of takg the dffereces of a seasoal tme seres data to buld aother eural etwork model. I ther approaches, they have traed a eural etwork wth deseasoalzed data (put the deseasoalzed data geerated by the movg-to-rato-average method to the put layer) ad dffereced data (put the dffereced data geerated by the SARIMA model to the put layer). I aother study [7], the performaces of seve well kow mache learg methods the toursm predcto problem are vestgated. Furthermore, they vestgate the effect of cludg the tme dex as a put varable. Durg the last two decades, ANN ad support vector models [4,3], some others [4,6] are cocluded that they ca effectvely deal wth those patters wthout seasoal adjustmet of the raw data [3]. The most commoly used tme seres models for forecastg are tradtoal statstcal methods. The drawback of these models s that they are mostly lear models. The relatoshp betwee the varables s ot lear for most problems real lfe [5] ad usg lear models for such problems s ot effcet. The statstcal methods such as lear regresso are sutable for data havg seasoal or tred patters, whle artfcal eural etwork (ANN) techques are also effcet for data whch are flueced by the specal case, lke promoto or extreme crss [6]. The ma purpose of ths study s to determstcally geerate the auxlary varables order to troduce the seasoal, cyclc ad tred compoets of a seasoal tme seres to the multvarate mache learg models the cotext of teratoal toursm demad. II. DATA ad METHODOLOGY I ths study, dataset s composed of the mothly tme seres a rage of 996 ad 03 years assocated wth Turkey ad ts top 0 raked toursm clets. The umber of toursts s selected as a metrc to measure the toursm demad to Turkey. Explaatory varables are: wholesale prces dex, US Dollar sellg, oe os gold Lodo sellg prce USD, hotel bed capacty of turkey, umber of toursm agecy Turkey, harmoc cosumer prce dex (HCPI) of Turkey ad HCPIs of leadg clets (amely Frace, Italy, Netherlads, Uted Kgdom, Uted States, umber of the toursts comg from the top 0 leadg clets of Turkey (amely Germay, Russa, Eglad, Ira, Bulgara, Georga, Hollad, Frace, USA, Italy), exchage rate of the leadg coutres of Turkey (Brtsh Poud, Russa Rouble, Bulgara regresso (SVR) techques have bee started to mmerge the feld of the toursm demad forecastg [8,9,0,8,,,]. Because of ther self-adaptve ad olear characterstc, mache learg techques are becomg sgfcat alteratve tools for the seasoal tme seres forecastg agast the tradtoal statstcal methods. Whle some researchers have bee suggestg that removg the seasoal patters wll crease the performace of the mache learg Fg.. Graphcal represetato of seasoal toursm demad data. Lev), exchage rate of Turksh Lra, ad the umber of former toursts comg. Mothly tme seres data were collected from the Mstry of the Toursm of the Republc of Turkey (webste: State Isttute of Statstcs of Turkey (webste: Databak of the Cetral Bak of the Republc of Turkey (webste: TÜRSAB (webste: Copyrght BAJECE ISSN: 47-84X February 05 Vol:3 No:

3 Toursm demad x 0000 BALKAN JOURNAL OF ELECTRICAL & COMPUTER ENGINEERING, 05, Vol.3, No. 44 Fg.. Hstogram of toursm demad data wth ormal curve. Next to these real 9 tme seres cludg the target, determstcally four more auxlary varables called year dex (labelled A_cycle), moth dex (labelled M_cycle), aual average (labelled A_AVG) ad mothly average (labelled M_AVG) are geerated, whch wll be explaed the ext secto detals. A. Aalyss of Toursm Demad Data The rus test used to detect whether the order of occurrece of two values of a varable s radom. I rus test, a ru represets a sequece of detcal cases. If we apply ths test to the cotuous data, t also ca dcate the exstece of the tred the data. We apply oe-sample rus test o toursm demad data by settg the cut pot to the meda value of the toursm demad data seres, so that values less tha the mea would be oe group ad the others the secod group. Number of the cases each group s observed as 0. We see totally 04 observatos wth 30 rus. A sample wth too may or too few rus dcates that the sample s ot radom. Here umber of the rus (30), whch s smlar occurrece of the observatos based o the cut pot, s smaller tha the umber of each group (0). But sce ths s a large sample case, we cosder the results of the test statstc (Z value = ) ad two taled p value = 0.00 (sgfcace level of 0.05) rather tha the umber of the rus. We ca coclude that toursm demad data seres has very strog evdece for tred. The Kolmogorov-Smrov oe-sample test s oe of the wdely used tools to test seasoalty ay aually cyclc pheomea. We have used The Kolmogorov-Smrov oesample test to detect the presece of ay sgfcat seasoal compoet toursm demad tme seres based o the yearly perod. I the oe sample Kolmogorov-Smrov Test, the "Asymp. Sg." (-taled) (also called as the p - value) s observed as 0.00 whch s smaller tha the sgfcace level of 0.05, there s suffcet evdece to suggest the toursm demad seres does ot exhbt seasoal patters. I addto to the formal tests, we ca also exame data graphcally for tred, seasoal ad cyclc compoets. From the graphcal represetato of seasoal toursm demad data (Fg. ), the curve of the hstogram s shape (Fg. ) ad mothly seasoal subseres plot (Fg. 3) [7], t s see that there are strog evdeces for exstg of the tred ad seasoal compoets the toursm demad tme seres. Oe of the most coveet ways s subseres plots wth categores represetg the moths o the x-axs ad the usage of les to dsplay mothly toursm demad patters as see Fg. 3. B. Geerato of the Auxlary varables We have comprehesvely aalysed the put data regards to the seasoal, tred ad cyclc compoets. Sce we have employed the multvarate data set, these compoets had bee traced based o the toursm demad tme seres, whch s assocated wth the tme seres of the total umber of tourst comg to Turkey. The tme seres data for the total umber of toursts comg to Turkey for the perod from Jauary 996 to December 03 exhbts very clear seasoalty, cyclcal effect ad growth tred, as show Fg.s,, ad 3 ad Table. We have regarded these patters as determstc compoets ad added them as auxlary varables to the raw data set. I the frst step, we have computed the aual ad mothly averages of toursm demad tme seres assocated wth tme seres of the total umber of tourst comg to Turkey. To justfy the dataset respect to the aual tred ad mothly seasoal effects, we have added these averages to dataset by usg two ew varables called A_AVG (Aual average) ad M_AVG (Mothly average). Geerato of these varables s Mothly subseres plots Ja Feb Mar Apr May Ju Jul Aug Sep Oct Nov Dec Fg. 3. Mothly subseres plots of toursm demad. Copyrght BAJECE ISSN: 47-84X February 05 Vol:3 No:

4 BALKAN JOURNAL OF ELECTRICAL & COMPUTER ENGINEERING, 05, Vol.3, No. 45 show the Table ad employmets of them are llustrated Table. TABLE I TOURISM DEMAND TIME SERIES WITH ANNUAL AND MONTHLY AVERAGES I the secod step, to troduce the cyclc patters whch are exhbted as yearly ad mothly by the tme seres of toursm demad (labelled Y the table ), we have added two varables labelled A_cycle (Aual cycle) ad M_cycle (Mothly cycle). These varables cota the year ad moth formato respectvely. These varables are llustrated Table. Oe of the smlar studes s [7], whch vestgates the effect of cludg the tme dex as a put varable. C. Artfcal Neural Networks Approach ANNs are computg structures spred from the bologcal eural etworks. ANN s made of the tercoected processg uts (usually called euros). They have the ablty of learg by adjustg the stregth of the tercoectos whch ca be acheved by alterg the values called weghts through the put data [8]. ANN s costructed wth euros. Each euro has oe or more weghted puts (dedrtes) ad oe or more outputs (axos) that are weghted whe coectg to other euros. Processg ut (Neuro) sums the weghted puts ad coveys the et put through a actvato fucto order to ormalze ad produce a result [9]. The equato of a smple euro s gve as y j f N w x b j j The multlayer etwork archtecture cossts of a put layer, two or more hdde layers, ad oe output layer. Whle the sgmod fucto s used for the er odes, lear actvato fucto s used output layer. BP s oe of the most popular approxmato approache for trag the multlayer feedforward eural etworks based o the Wdrow Hoff trag rule [0,8]. Whle the put sgals propagate forward, error sgals propagate backward layer-by-layer through the etwork []. There are two types of error fuctos for BP. The frst error fucto (Eq. ) s used for output cells, ad the secod s used oly for hdde cells (Eq. 3). E E a y gy 0 () h w E gy h, 0 h where y s the output of the gve cell. a s the expected or correct result. w represets all the weghts (from to ) coectg the hdde cell to all put cells ( a fully coected etwork). Whle (g) s the actvato, or trasfer fucto, fucto represets the frst dervatve of the actvato fucto. The ext step s to adjust the correspodg weghts for the ode by usg ths error. We ll use Eq. 4 for ths purpose, whch utlzes the error prevously calculated for the ode (whether hdde or output) [8]. (4) w j w Ey j g For the gve error (E) ad actvato (or cell output, we multply by a learg rate ( ) ad add ths to the curret () y (3) ), TABLE II DATASET Copyrght BAJECE ISSN: 47-84X February 05 Vol:3 No:

5 BALKAN JOURNAL OF ELECTRICAL & COMPUTER ENGINEERING, 05, Vol.3, No. 46 weght. The result s a mmzato of the error at ths cell, whle movg the output cell actvato closer to the expected output [9]. D. Support vector regresso SVMs are a ew type of supervsed learg methodology developed by Vapk ad hs co-workers []. It s curretly the most popular learg algorthm for supervsed learg. SVMs buld a lear separatg hyperplae, by mappg data to the feature space wth the hgher-dmesoal, usg the so-called kerel trck. Eve samples are ot learly separable the orgal put space they ca easly separate the hgher-dmesoal space by usg a lear separator. The hgh-dmesoal lear separator s actually olear the orgal space [3]. Suppose we are gve trag data, where deotes the space of the { x, y,..., x, y } put patters. I fucto f x obtaed targets x SV regresso [], the goal s to fd a that has at most ε devato from the actually y for all the trag data, ad at the same tme s as flat as possble. I other words, we do ot care about errors as log as they are less tha ε, but wll ot accept ay devato larger tha ths. The case of lear fucto has bee descrbed the form as s x f w, x b, wth where,, deotes the dot product. f x b (5) w, x s called feature, whch s olear mapped from the put space x. The w ad b are coeffcets, whch are estmated by mmzg the regularzed rsk fucto. Flatess the case of Eq. 8 meas that oe seeks a small w. The learg procedure of a SVM ca be show as follows. The mmzato of the complexty term ca be acheved by mmzg the quatty w (6) Oe way to esure ths optmzg by the way: mmze w subject to y w, x b w, x b y The w ad b are coeffcets, whch are estmated by mmzg the regularzed rsk fucto (7) L d, y L d, y 0 d y d y otherwse, (9) s called the e-sestve loss fucto. C ad ε are user-defed parameters. I the emprcal aalyss, C ad ε are the parameters selected by users. The parameter ε s the dfferece betwee actual values ad values calculated from the regresso fucto. Ths dfferece ca be treated as a tube aroud the regresso fucto. The pots outsde the tube are the trag errors. The loss equals zero f the forecasted value s wth the e-tube [4,5]. E. Predcto Performace Metrcs There are a umber of possble measures used for comparg the performaces of MLP regresso, ad SVR models: root relatve squared error (RRSE), relatve absolute error (RAE), ad correlato coeffcet (R, sometmes also deoted r), respectvely. These measures are defed the followg formulas, where the s the umber of test cases, s the actual (observed) value, value for the test case [3]: a S S A PA a, p p, p p pa a a a, S P a s the predcted (estmated) Root relatve squared error (RRSE) RRSE a a p a Relatve absolute error (RAE) RAE p p, (0) () p a () a a Correlato coeffcet (R) SPA R S S P A (3) C N L d, y, R C where, N w (8) III. RESULTS ad DISCUSSION I ths study, we have developed the MLP regresso ad SVR models to forecast the toursm demad to Turkey wth respect to ts te major clets by usg a wde varety of tme seres for a perod cocerg the years betwee 996 ad 03 wth mothly collected hstorcal data. Copyrght BAJECE ISSN: 47-84X February 05 Vol:3 No:

6 BALKAN JOURNAL OF ELECTRICAL & COMPUTER ENGINEERING, 05, Vol.3, No. 47 I the expermets, the several dfferet cofguratos of the MLP regresso ad SVR models are mplemeted. Ther forecastg performaces are examed ad compared. I our mplemetatos, we have used WEKA [6] data mg software, whch s ope source software ssued uder the GNU Geeral Publc Lcese. A. Expermetal Results I ths study, ANN ad SVR models are developed ad tested o the toursm data set wth 8 features for the forecast horzos of moths ahead. All the 8 tme seres are accumulated from the statstcal sttutos. The selecto of the put varables the developmet of the model sgfcatly flueces the model performace. Regressoal RelefF [7] data mg aalyss was employed to determe the set of the put varables. Thus, the umber of the toursts comg from the top te clet coutres, exchage rates, harmoc cosumer prce dexes, wholesale prces dex, gold prce, hotel bed capacty, umber of toursm ageces destato place were cosstetly used as the put varables for trag the mache learg techques throughout the modellg phase. Arragemet of the frst dataset s composed of the 8 real tme seres, whch are gathered from the statstcal sttutos, ad the pars of correspodg output wth moths horzo. Arragemet of the secod dataset s composed of the 8 real tme seres, whch are the same varables the frst dataset, ad four more auxlary varables, whch are determstcally geerated ad ther pars of correspodg output wth moths horzo (Table ). Istaces of these datasets are respectvely defed by [x(t,), x(t,),, x(t, 8); y(t+)] ad [x(t,), x(t,),, x(t, 3); y(t+)]. O the bass of MLP regresso ad SVR models ad selecto of ther correspodg parameters, several models are mplemeted ad examed. But because of space restrcto, oly the best of them those combatos are reported Table 3. Those models were evaluated wth 0- folds cross valdato by usg three forecastg accuracy measures: correlato coeffcet (R), relatve absolute error (RAE) ad root relatve squared error (RRSE). The MLP regresso models developed ths study are costructed as two hdde layers wth the sgmod actvato fucto ad a output layer wth a lear actvato fucto. The frst MLP regresso model s mplemeted wth the cofgurato of 8 (whch s the umber of the put varables the frst dataset) ad8 odes the hdde layers, ad traed by usg back-propagato algorthm wth the settgs of the learg rate 0., mometum 0.7, ad epoch 500. The secod MLP regresso model s mplemeted wth the cofgurato of 3 odes (whch s the umber of the put varables the secod dataset) ad 4 odes the hdde layers, ad traed by usg back-propagato algorthm wth the settgs of the learg rate 0., mometum 0.7, ad epoch 500. The frst SVR model s mplemeted by usg the PUK kerel ad wth the settgs of the complexty parameter c=50, omega parameter ad sgma parameter ad the secod SVR model s mplemeted by usg the PUK kerel ad wth the settgs of the complexty parameter c=50, omega parameter ad sgma parameter Forecastg results of the MLP ad SVR models are gve Table 3 by meas of relatve absolute error (RAE), root relatve squared error (RRSE) ad the correlato coeffcet (R). Ivestgato ad evaluato of those models (Table 3) showed that () ANN model usg the secod dataset wth R=0.9879, RAE=4.4% ad RRSE=6.7% values has better accuracy tha ANN model usg the frst dataset wth R=0.98, RAE=6.96% ad RRSE=9.%, () SVR model usg the secod dataset wth R=0.993, RAE=0.36% ad RRSE=.66% values has better accuracy tha SVR model usg the frst dataset wth R=0.9875, RAE=4.57% ad RRSE=5.84%, (3) the SVR model usg the secod dataset has the best accuracy wth R=0.993, RAE=0.36% ad RRSE=.66% values. Lke pror studes, whch are cocluded that mache learg models lke ANN ad SVR ca effectvely deal wth the datasets havg seasoal, tred ad cycler patters [3] ad those techques are also effcet for data, whch are flueced by the specal case, lke promoto or extreme crss [6], ths study s also showed that atural performace of MLP regresso ad SVR models appears to be satsfactory (whch ca be see table 3, model ad 3). However, for the better forecastg performace, the further vestgato has doe based o the tred, seasoal, ad cyclc compoets of the toursm demad tme seres (whch ca be see table 3, model ad 4). We have erched the raw data set, whch s called dataset I ad cotas 8 real data seres by addg determstcally geerated four more auxlary varables by cosderg the date formato ad patters volved toursm demad tme seres ad we have obtaed a ew data set, whch s called dataset II wth 8 real attrbutes ad four artfcal attrbutes. The performaces of the ANN ad SVR models are evaluated for each datasets. Choosg the large sze of dataset may requre the cosderato of the trade-offs betwee the speed ad accuracy. Larger dataset takes loger to tra ad to geerate forecast. If the cosderato s ot oly accuracy of the forecastg model, the smaller umber of put varables could be preferred. I the case of more accuracy, losg some performace, whch s a few secods for ths study, ca be eglected. The purpose of ths study was to perform a comprehesve aalyss for a toursm demad tme seres to vestgate whether t has tred, seasoal ad cyclc compoets. Ad the we have developed determstc techques order to outle ad troduce these patters to the mache learg models Copyrght BAJECE ISSN: 47-84X February 05 Vol:3 No:

7 BALKAN JOURNAL OF ELECTRICAL & COMPUTER ENGINEERING, 05, Vol.3, No. 48 Model No Dataset We have vestgated the applcablty of these techques mache learg area by employg the ANN ad SVR models, whch are well-kow mache learg models the toursm demad forecastg. IV. CONCLUSIONS We have aalyzed the toursm demad seres by cosderg the certa possble patters. The purpose of aalyzg the tme seres s to troduce the dyamc relatoshps amog the tme ad the toursm demad seres ad mprove the accuracy of forecasts by extractg the addtoal formato avalable from the assocated data seres at had the toursm demad forecastg. I toursm demad forecastg, the quattatve forecastg models are maly studed ad examed three geeral categores: tme-seres, ecoometrc techques, ad artfcal tellgece (AI) methods. The seasoal, cyclc ad tred compoets of the seasoal tme seres are tesvely vestgated ad cosdered toursm demad modellg ad forecastg for tme-seres ad ecoometrc models. To deal wth these patters, may techques such as lagged explaatory varables, dummy varables, seasoal dexes, deseasoalzato ad dfferecg are developed. However, lttle emprcal research has bee udertake o corporatg of these patters whe the mache learg models are used the modellg of the toursm demad. Ths emprcal study uses the data seres wth mothly data pots, ad t tests the approprateess of the two proposed techques. These proposed techques am to outle the tred, seasoal ad cyclc compoets ad troduce them to the mache learg models the cotets of toursm demad modellg ad forecastg. The cocluso draw from ths study s that two proposed techques, whch are cluso of two date dmesos regards to the yearly ad mothly cycles, ad two justfcatos of data regards to tred outled by the aual averages ad seasoal patter outled by the mothly averages, sgfcatly mprove the accuracy of the mache learg models the cotext of the toursm demad forecastg. Ths outstadg mprovemet s obtaed based o a partcular data set related to the toursm demad for Turkey. Therefore, ths study ca be exteded o the other tme seres whch exhbts the cyclc, seasoal ad tred patters the use of mache learg models. Oe of the defceces of usg these techques s a requremet of exploratory data aalyss to extract formato for cyclc, seasoal ad tred compoets, whch s tmecosumg ad requres doma experts. However, the ma TABLE III FORECASTING PERFORMANCES Model Type Cofgurato R RAE (%) RRSE % Dataset I SVR PUK kerel, C:50, O/S:7/ Dataset II SVR PUK kerel, C:50, O/S:7/ Dataset I ANN ANN(8,8), M:0.7, LR: Dataset II ANN ANN(3,4), M:0.7, LR: motvato of the developg mache learg algorthms s to troduce the tools that automate the formato dscovery process. But the sgfcat mprovemet of the performace regards to accuracy obtaed by the use of these methods shows that these ad other smlar statstcal techques are challegg pre-processg cosderatos the developmet of the mache learg models. 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8 BALKAN JOURNAL OF ELECTRICAL & COMPUTER ENGINEERING, 05, Vol.3, No. 49 [8] S. Hayk, Neural Networks: a comprehesve foudato., Secod Edto ed.: Pretce Hall, 999, 84p. [9] M. Tm Joes, Artfcal Itellgece: A Systems Approach.: Ifty Scece Press LLC, 008. [0] Chrstopher M. Bshop, Neural Networks for Patter Recogto.: Oxford Uversty Press, 995. [] Atlla Aslaargu, Mammadagha Mammadov, Bera Yazc, ad Seay Yolaca, "Comparso of ARIMA, eural etworks ad hybrd models tme seres: tourst arrval forecastg," Joural of Statstcal Computato ad Smulato Vol. 77, No., Jauary, 9 53, 007. [] V. Vapk, The ature of statstcal learg theory. New York: Sprger, 995. [3] Ia H. Wtte ad Ebe Frak, Data Mg: Practcal Mache Learg Tools ad Techques, d ed.: Morga Kaufma, 005. [4] Harrs Drucker, Chrs J.C. Burges, Lda Kaufma, Alex Smola, ad Vladmr Vapk, "Support vector regresso maches," Adv. Neural Iform. Process. Syst. 9 (997) 55 6., 997. [5] Vojslav Kecma, Learg ad Soft Computg:Support Vector Maches, Neural Networks ad Fuzzy Logc Models.: A Bradford Book edto ISBN-0: 06558, 00. [6] Mark Hall et al., "The WEKA data mg software: a update," ACM SIGKDD Exploratos Newsletter, vol., o., pp. Volume Issue ACM New York NY USA ISSN: , 009. [7] Marko Robk-Škoja ad Igor Kooeko, "A adaptato of Relef for attrbute estmato regresso," 997. BIOGRAPHIES Selcuk CANKURT s graduated from the Uversty of Marmara, Istabul, Turkey 997. He receved the M.S. degree formato techologes from Iteratoal Burch Uversty, Sarajevo, Bosa ad Herzegova 0. Sce 03, he has appoted as seor teachg assstat Iteratoal Burch Uversty. Hs research terest s multvarate forecastg the areas of mache learg, data mg ad soft computg. Abdulhamt SUBASI receved the B.S. degree electrcal ad electrocs egeerg from Hacettepe Uversty, Akara, Turkey, 990 ad the M.S. degree electrcal ad electrocs egeerg from Mddle East Techcal Uversty, Akara, Turkey, 993. He receved hs Ph.D. degree electrcal ad electrocs egeerg from Sakarya Uversty, Sakarya, Turkey, 00. I 006, he was seor researcher at Georga Isttute of Techology, School of Electrcal ad Computer Egeerg, USA. Sce 0, he s appoted as Professor of Electrcal ad Electrocs Egeerg at Iteratoal Burch Uversty, Sarajevo, Bosa ad Herzegova. Hs areas of terest are data mg, mache learg, patter recogto ad bomedcal sgal processg. He has worked o several projects related wth bomedcal sgal processg, patter recogto ad computer etwork securty. Moreover, he s volutarly servg as a techcal publcato revewer for may respected scetfc jourals ad cofereces. Copyrght BAJECE ISSN: 47-84X February 05 Vol:3 No: