Currency Exchange Rates Prediction based on Linear Regression Analysis Using Cloud Computing

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1 Ieraoal Joural of Grd ad Dsrbued Copug Vol. 6, No., Aprl, 03 Currey Exhage Raes Predo based o Lear Regresso Aalyss Usg Cloud Copug Szu-Y L, Ch-Hua Che,3,* ad Ch-Chu Lo Depare of Iforao Maagee, Chug Yua Chrsa Uversy, Taoyua, Taa, R.O.C. Isue of Iforao Maagee, Naoal Chao Tug Uversy, Hshu, Taa, R.O.C. 3 Teleouao Laboraores, Chugha Teleo Co., Ld, Taoyua, Taa, R.O.C. szuyl@gal.o; hhua086@gal.o; lo@fauly.u.edu. Absra I global ope eooy, he sudy of urrey exhage raes predo h aepable auray uder floag exhage raes evroe beoes a pora ssue. Exhage raes a affe a large uber of eoo deso-akgs ad parpas behavors. Due o he rapd dya daa hages ad reasg large aou of daa, aurae ad effeve urrey exhage raes predo s a raher hallegg ask. I hs paper, e proposed a ovel loud opug approah o do lear regresso predo for dya urrey exhage raes. We adop a Iellge Exhage Raes Predo Syse (IERPS) based o loud opug o olle real-e exhage raes forao ad pred he fuure exhage raes effe opug e. The syse a proess large aous of hsoral ad dya daa ore effely ad auraely. The expereal resuls shoed ha he average error raos of usg lear regresso are 94.6%, hh s a very good perforae. Keyords: Currey Exhage Raes Predo, Lear Regresso, Cloud Copug, Hadoop, MapRedue. Iroduo Predo s a key elee of faal deso akg. I ree years, he predo of exhage raes has beoe a pora ssue, beause he foreg exhage arke s he larges ad os lurave faal arkes [, 4], ad foreg exhage raes are oe of he os pora eoo des he eraoal faal ad oeary arke. Early exhage raes syse adoped fxed exhage raes rege produg less rsk, bu se he ollapse of he Breo-Woods syse 973, he Ued Saes ad oher radg parers abadoed he fxed exhage raes syse ad adoped he floag exhage raes syse. Mos oures also have folloed su afer ha. Beause he lberalzao of global rade ad eraoal apal flos, he floag exhage raes syse brgs grea ueray. If e a pred exhage raes edey effevely, he ueray of rade vese a be redued, so ha eraoal rade s ore soohly. Moreover, he vesor's profs a also be proved. The pas exhage raes deso odels are offered by radoal eoos heory, suh as: () Theory of Purhasg Poer Pary, () Theory of Ieres Rae Pary, ad (3) Balae Theory of Ieraoal Paye []. They esablshed a se of sple ad ovee ehod for exhage raes hages alulao. Neverheless, afer he floag exhage raes

2 Ieraoal Joural of Grd ad Dsrbued Copug Vol. 6, No., Aprl, 03 syse has be arred ou, he red of exhage raes beae ore dfful o pred. Ths has proped eooss o ry o fd a ore reasoable ad effeve foreasg odel o exhage raes hages. Hoever, beause of os aos have experees of draa hages he exhage values of her urrey, aurae urrey exhage raes predo s a raher hallegg ask. Meese ad Rogoff proposed a rado alk odel 983 ad argued ha all exhage rae odels do less ell ou-of-saple foreasg exerses ha a sple drfless rado alk []. Furherore, os of researhers foud ha radoal eooer ad e seres ehques ould o relably ouperfor he sples rado alk. The reaso s parly he ureals assupos ha are appled o hese lassal ehods [9]. For sae, AuoRegressve Movg Average (ARMA) odel s sube o he odo of saoary of he e seres. Auoregressve Codoal Heeroskedas (ARCH) odel [6] ad Geeralzed Auoregressve Codoal Heeroskedasy (GARCH) odel [] are prove ore effeve odelg dya foreg exhage raes. Hoever, hey have o served as a ovg ool praes ad real-e radg. Reely, here are also ay sudes ulzg olear odels or arfal ellgee ehques aeps o solve he proble of urrey exhage raes predo. The olear odels are suh as Arfal Neural Neork (ANN), Gee Algorh (GA), Bayesa odel, Sgular Speru Aalyss (SSA), ad so o. Fro hese exsg olear odels o urrey exhage raes predo, hey a sho he predo resuls beer ha rado alk, bu hey have he probles of poor effey ad lak of he ably o proess real e ad dya daa. Therefore, order o hadle large aou of dya ad real-e daa effely, e use loud opug ehology hs sudy. I 008, for assve daa proessg, Google proposed a geeral odel, MapRedue, hh hadles he daa dsrbuo, parallelzao, faul olerae ad load balag o suppor he dvde-ad-oquer ehque. Wh hs absrao odel, users do o oly a akle he oplexy of dsrbued syse, bu also a fous o he busess ellgee desg [3, 7, 4]. Frs, he prograer has o defe he Map fuos ad Redue fuos hh are boh defed h key/value pars. The Map fuo akes a pu par ad produes a se of eredae key/value pars. The he Map fuo s appled loud opug o every e he pu daases. Afer ha, he MapRedue fraeork olles all pars h he sae eredae key fro all lss ad groups he ogeher, hus reag oe group for eah oe of he dffere eredae keys. The Redue fuo s he appled loud opug o eah group ad reurs of all resposes hh are olleed as he desred resul ls. Aog he varous beefs of MapRedue over oveoal daa proessg ehques, e ls he esseal faors as follos [3]. () I s easy o use hou pror experee h dsrbued syses for prograers. () I eables he salably of applaos aross large lusers of heap opuers o solve a proble. (3) I a auoaally hadle falures o suppor faul olerae. I hs paper, a lear regresso predo approah s appled o pred he dya urrey exhage raes usg loud opug. We adop a Iellge Exhage Raes Predo Syse (IERPS) based o loud opug o olle real-e exhage raes ad pred he fuure exhage raes. Thus, our researh obeves are as follog key feaures. () Proess large aous of hsoral ad dya real-e daa ore effely by loud opug, ad () Redue he error rae of exhage raes predo hrough dya lear regresso predo approah. The reader of hs paper s orgazed as follos. The ex seo preses he proposed syse arheure ad approah of Iellge Exhage Raes Predo Syse based o

3 Ieraoal Joural of Grd ad Dsrbued Copug Vol. 6, No., Aprl, 03 Cloud Copug. The, Seo 3 explas he expereal resuls. Fally, e gve soe oluso rearks ad fuure orks Seo 4.. Syse Arheure Ths seo desrbes he proposed syse arheure, deals of s hree a opoes. As sho Fgure, here are hree opoes (pars) IERPS based o loud opug: () The frs par s he Iellge Exhage Raes Predo Syse. I does o oly olle dya faal daa or real-e forao fro evroe o daabase servers, bu also ouae h loud server plafor o do alulao effely. () The seod par s he Daabase Servers. Hsoral urrey exhage raes daa are sored hs par. (3) The hrd par s he Cloud Server Plafor. We adops Hadoop plafor fro Naoal Ceer for Hgh-Perforae Copug, Naoal Appled Researh Laboraores [8] o plee our Cloud Server Plafor. I a be dvded o o por odules: Faal Predo Module ad Cloud Copug Module, e ll desrbe deal o he he follog sub-seos. Daabase Servers Hsoral Currey Exhage Raes Daa Iellge Exhage Raes Predo Syse Dya Daa Updae Module Cloud Couao Module Cloud Server Plafor Faal Predo Module Te-Shf Lear Regresso Model L a b Weghg Cofgurao Exhage Raes Prdo ' L User Cle Cloud Copug Module Fgure. The syse arheure of Iellge Exhage Raes Predo Syse based o Cloud Copug. Iellge Exhage Raes Predo Syse The Iellge Exhage Raes Predo Syse (IERPS) osss of o odules. Oe s Dya Daa Updae Module, hh s resposble for gaherg ad arragg he dya exhage raes daa, updag forao fro real ord ad sorg he Daabase Servers. The oher s Cloud Couao Module, hh a aqure he relaed hsoral urrey exhage raes daa fro Daabase Servers ad ouae h Faal Predo Module ad Cloud Copug Module Cloud Server Plafor. The predo resuls are geeraed fro hs IERPS syse. 3

4 Ieraoal Joural of Grd ad Dsrbued Copug Vol. 6, No., Aprl, 03. Cloud Server Plafor There are o aor odules Cloud Server Plafor: () Faal Predo Module ad () Cloud Copug Module. They are desrbed he follog sub-seos... Faal Predo Module I hs paper, he IERPS olles he urrey exhage raes fro Dya Daa Updae Module (DDUM) ad uses regresso ehod o pred he fuure urrey exhage raes aordg o he urre fae forao. For urrey exhage rae predo, IERPS olles he urrey exhage rae beee Ued Saes Dollar (USD) ad -h urrey ( ) a yle e ad sores as hsoral reord. There are k hsoral reords sored daabase. The e use lear regresso odel L o deere he relao of. Ths lear regresso odel a be esae he auray rao as ad for he eghed averages ehod. Moreover, he sok pre volaly s alays relaed o varous fae forao (e.g., he urrey exhage raes of oher urrees). Therefore, e sele he urrey exhage raes of relaed urrees ad use Eq. () o pred he urrey exhage rae beee USD ad -h urrey ( ') a ex yle e (+). ' here b L a b ', a a, ad ().. Cloud Copug Module For urrey exhage rae predo, Cloud Copug Module (CCM) uses he lear L ad MapRedue Progra [3, 5, 7, 0, 3, 4] o deere he regresso odel urrey exhage rae beee USD ad -h urrey ( ) a yle e ad he urrey exhage rae beee USD ad -h urrey ( ' ) a ex yle e (+). The Map ad Redue fuos of MapRedue are boh defed h respe o daa sruured <key, value> pars. We se urrey ID as key ad urrey exhage rae ad as value sho Fgure. 4

5 Ieraoal Joural of Grd ad Dsrbued Copug Vol. 6, No., Aprl, 03 For exaple, he DDUM rereves he urrey exhage rae beee USD ad s urrey ( ) a he s yle e ad he urrey exhage rae beee USD ad -h urrey ( ' ) a he d yle e. The urrey exhage rae par <,, > ll be reorded ad apped. I Reduer progra, he paraeers a ad b of he lear regresso odel MapRedue progra o alulae he lear regresso odel L ll be alulaed by Eq. (). For hs reaso, e a use he urrey exhage rae beee USD ad -h urrey ( L o pred he pre of ' ) a ex yle e (+). Daabase Currey Currey Currey 3 <key, value > <,, > <,, > 3 <,, > 3 4 <,, > <, a b >, <, a b >, <3, a b >, 3 3 Currey <, a, b > Seup Mapper Reduer..3 Daabase Servers Fgure. The proedure of MapRedue appled CCM All large aous of dealed hsoral urrey exhage raes daa are sored Daabase Servers. The daa a o oly be dsplayed dffere e perod, urrey oposo, or query paraeers for user query, bu also a be ulzed o aalyze Cloud Server Plafor. 3. Experes I experes, e sele he urrees (e.g., Brsh Poud (GBP), Euro (EUR), Ausrala Dollar (AUD), ad Japaese Ye (JPY)) hh are relaed h Taa Dollar (TWD) as a ase sudy o prove he auray of urrey exhage rae predo. The four urrey exhage raes are olleed fro Ja o Apr-9-0. The real-e urrey exhage rae a be obaed fro DDUM. The auray of he urrey exhage rae predo (A) s expressed as Eq. (). ' A () For urrey exhage rae predo, e use he Daase (fro Ja o Mar-3-0) ad he Daase (fro Apr-0-00 o Mar-3-0) as rag daases by applyg hese daa o Eq. () o deere eah, a, ad b. We he apply he hrd daase 5

6 GBP/TWD Exhage Rae a + Ieraoal Joural of Grd ad Dsrbued Copug Vol. 6, No., Aprl, 03 (fro Apr-0-0 o Aprl-9-0) as esg daa o Eq. () o pred he urrey exhage rae ( ' ) ad opare he preded urrey exhage rae ( ' ) h real urrey exhage rae ( TWD ). For exaple, GBP/TWD exhage rae ( ' ) a yle e L, (+) s preded by regresso based ehods aordg o he relao urre urrey exhage rae ( ). Fgure 3 llusraes a exaple of TWD h he, a,,, ad b h he GBP/TWD exhage rae,.e., , a ,, TWD, GBP TWD, ad b by usg Daase. We he use Eq. () o pred he urrey TWD, GBP exhage rae ( TWD ' ) h eah relaed urrees, ad he resul shos ha he average auray s 89.05%. Moreover, Fgure 4 llusraes a exaple of,, a, GBP, ad b, h he GBP/TWD exhage rae by usg Daase ad shos ha he average auray s 97.47%. Fally, Table shos he auray oparso of he relaed urrey exhage raes. Table.. The auray oparso of he relaed urrey exhage raes Currey Daase Daase GBP/TWD 89.05% 97.47% EUR/TWD 90.67% 98.6% AUD/TWD 95.0% 98.93% JPY/TWD 9.69% 94.6% Average 9.86% 97.3% (+,)' = * (,) = GBP/USD Exhage Rae a e Fgure 3. The relao of urrey exhage rae beee TWD ad GBP (Fro Ja o Mar-3-0) 6

7 GBP/TWD Exhage Rae a e + Ieraoal Joural of Grd ad Dsrbued Copug Vol. 6, No., Aprl, (+,)' = * (,) = GBP/USD Exhage Rae a e Fgure 4. The relao of urrey exhage rae beee TWD ad GBP (Fro Apr-0-00 o Mar-3-0) 4. Colusos Dya urrey exhage raes predo s sgfa forao for faal deso akg. Tradoal approahes aalyze large pas sasal faal ad urrey forao over varous perods. I fa, he urrey exhage raes hage rapdly ad frequely so ha he radoal sasal ehods are o effeve ad effe o pred he urrey exhage raes real e. Therefore, hs paper, a lear regresso predo approah based o loud opug s appled o pred he dya urrey exhage raes. We adop a IERPS based o loud opug o olle real-e exhage raes forao ad pred he fuure exhage raes effely. Thus, he proposed approah a proess large aous of hsoral ad dya real-e daa effevely ad effely by rodug loud opug ehology. I he fuure, e ll arry ou furher aalyss ad o buld e-shf daa orrelao ad e-shf lear regresso predo approah. The, he ehology of Dya Daa-Drve Applao Syse (DDDAS) a be appled o hs urrey exhage raes predo proble real e ad large sale daa evroe. Akoledges The researh s suppored by he Naoal See Coul of Taa uder he gra Nos. NSC 00-8-H-009-0, NSC 00-6-H CC3, ad NSC 0-40-H DR. Referees [] R. Balle ad P. C. MMaho, The foreg exhage arke : heory ad eooer evdee, Cabrdge Cabrdgeshre ; Ne York: Cabrdge Uversy Press, (989). [] T. Bollerslev, Geeralzed Auoregressve Codoal Heeroskedasy, Joural of Eooers, vol. 3, o. 3, (986), pp

8 Ieraoal Joural of Grd ad Dsrbued Copug Vol. 6, No., Aprl, 03 [3] C. H. Che, B. Y. L, H. C. Chag ad C. C. Lo, The Novel Posog Algorh Based o Cloud Copug - A Case Sudy of Iellge Trasporao Syses, Iforao-A Ieraoal Ierdsplary Joural, vol. 5, o. (A), (0), pp [4] C. H. Che, S. Y. L, H. C. Chag ad C. C. Lo, O he Desg ad Develope of a Novel Real-Te Trasao Pre Esao Syse, Advaed Maerals Researh, vol , (0), pp [5] C. H. Che, Y. C. Yag, P. L. Shh, F. Y. Lee ad C. C. Lo, A Cloud-Based Reoeder Syse - A Case Sudy of Delay Reoedao, Proeda Egeerg, vol. 5, (0), pp [6] R. F. Egle, Auoregressve odoal heerosedasy h esaes of he varae of Ued Kgdo flao, Eooera: Joural of he Eooer Soey, (98), pp [7] I. Gul ad M. Hussa, Dsrbued Cloud Iruso Deeo Model, Ieraoal Joural of Advaed See ad Tehology, vol. 34, (0), pp [8] Hadoop Plafor of Naoal Ceer for Hgh-Perforae Copug, Naoal Appled Researh Laboraores, hp://hadoop.h.org./. [9] L. Kla ad M. P. Taylor, Why s so dfful o bea he rado alk foreas of exhage raes?, Joural of Ieraoal Eoos, vol. 60, o., (003), pp [0] B. Y. L, C. H. Che, H. C. Chag ad C. C. Lo, A Neork Behavor Aalyss Syse for Cloud Copug Serve, Iforao-A Ieraoal Ierdsplary Joural, vol. 4, o. 3, (0), pp [] R. Madoald ad M. P. Taylor, Exhage-Rae Eoos - a Survey, Ieraoal Moeary Fud Saff Papers, vol. 39, o., (99), pp [] R. A. Meese ad K. Rogoff, Epral Exhage-Rae Models of he Sevees - Do They F ou of Saple, Joural of Ieraoal Eoos, vol. 4, o. -, (983), pp [3] M. O. Naafabad, S. M. Mrdaad ad A. T. P. Naafabad, Ordal Logs Regresso o Desg a Effe Moble Trag Syse fro Iraa Expers' Po of Ve, Ieraoal Joural of Advaed See ad Tehology, vol. 34, (0), pp [4] A. Reha ad M. Hussa, Effe Cloud Daa Cofdealy for DaaS, Ieraoal Joural of Advaed See ad Tehology, vol. 35, (0), pp. -0. Auhors Szu-Y L Szu-Y L reeved hs M.S. ad Ph.D. degree fro Naoal Chao- Tug Uversy 0. He s urrely a Asssa Professor a he Depare of Iforao Maagee Chug Yua Chrsa Uversy Taa. Hs researh eress are serve-oreed arheure ad opug, ellge daa aalyss, ul-age syses, ad Iere ehology. Ch-Hua Che Ch-Hua Che reeved a B.S. degree fro Naoal Pgug Uversy of See ad Tehology, Taa, 007, ad a M.S. degree fro Naoal Chao Tug Uversy, Taa, 009, all forao Maagee. He s urrely orkg oard he Ph.D. degree h he Isue of Iforao Maagee, Naoal Chao Tug Uversy, Hshu, Taa. He has publshed over 80 oural ad oferee papers. Currely, he s servg as a Edor--Chef for IEEE Tehology ad Egeerg Eduao. He also served as a Gues Edor--Chef of Speal Issue o "Cloud Copug Tehology ad Applaos" for IEEE 8

9 Ieraoal Joural of Grd ad Dsrbued Copug Vol. 6, No., Aprl, 03 Muldsplary Egeerg Eduao Magaze, a Area Edor/Assoae Edor for Ieraoal Joural of Couao Neorks ad Iforao Seury, a Asssa Edor-I-Chef for Ieraoal Joural of Desg, Aalyss ad Tools for Iegraed Crus ad Syses, ad a eber of edoral board of several eraoal ourals suh as Ieraoal Joural of Researh ad Reves Ad ho Neorks, Ieraoal Joural of Researh ad Reves Sof ad Iellge Copug, Ieraoal Joural of Researh ad Reves Iforao Sees, Ieraoal Joural of Researh ad Reves Wreless Couaos, Ieraoal Joural of Copuer See ad Teleouaos, Ieraoal Joural of Copuer Treds ad Tehology ad Ieraoal Joural of Egeerg Treds ad Tehology. Hs ree researh eress are Cloud Copug, Cellular Neork, Daa Mg, Iellge Trasporao Syse, Neork Seury, Healhare Syse, ad E- Learg Syse. Ch-Chu Lo Ch-Chu Lo reeved hs Ph.D. degree Copuer See fro Brookly Polyeh Uversy, USA 987. He s a Professor he Isue of Iforao Maagee a Naoal Chao Tug Uversy Taa. Hs researh eress lude eork aagee, eork seury, eork arheure, ad reless ouaos. 9

10 Ieraoal Joural of Grd ad Dsrbued Copug Vol. 6, No., Aprl, 03 0