NG DNG MNG NEURON NHN TO TRONG VIC D BO D LIU CHUI THI GIAN C TNH XU HNG V TNH MA

NG DNG MNG NEURON NHN TO TRONG VIC D BO D LIU CHUI THI GIAN C TNH XU HNG V TNH MA

GVHD: PGS.TS Dng Tun AnhSVTH 1: on Ngc Bo50800107SVTH 2: Ng Duy Khnh Vy50802706

Lun vn tt nghip12/2012Ni dung12/2012t vn Gii thiu chui thi gianGii thiu mng neuron nhn toGii thut lan truyn ngcGii thut RPROPp dng mng neuron vo d bo d liu chui thi gianM hnh laiM hnh kh ma, kh xu hngThc nghimKt lunQ&A2t vn Mng neuron nhn to l mt phng php mnh c p dng nhiu vo bi ton d bo chui thi gian.Nhiu kt qu nghin cu thy rng mng neuron nhn to khng c kh nng d bo tt cho cc chui thi gian c tnh xu hng v tnh maTrong lun vn ny, chng ti ci tin mng neuron nhn to c th d bo tt hn.12/2012

Gii thiu chui thi gian

Chui thi gian: d liu c thu nhp, lu tr v quan st theo thi gianTa k kiu chui thi gian l {Xt} vi t l cc s t nhin. Xt l cc bin ngu nhin (random variable) rt ra t mt phn b xc sut (probability distribution) no . Cc chui thi gian thng c biu din bng mt th vi trc honh l bin thi gian

12/2012Chui thi gian c 4 thnh phn: xu hng, ma, chu k, bt quy tcHai m hnh4

Lng khch hng t ch hng thng ca hng hng khng Pan Am t nm 1946 n nm 1960

Gii thiu chui thi gian

12/2012Gii thiu mng Neuron nhn toMng neuron nhn to (Artificial Neural Network) l mt m hnh ton hc nh ngha mt hm s t mt tp u vo n mt tp u raMng neuron nhn to l mt mng gm mt tp cc n v (unit) c kt ni vi nhau bng cc cnh c trng s. Mt n v thc hin mt cng vic rt n gin: n nhn tn hiu vo t cc n v pha trc hay mt ngun bn ngoi v s dng chng tnh tn hiu ra

12/2012Gii thiu mng Neuron nhn to7Trong mt mng neuron c ba kiu n v: Cc n v u vo, nhn tn hiu t bn ngoi.Cc n v u ra, gi d liu ra bn ngoi.Cc n v n, tn hiu vo ca n c truyn t cc n v trc n v tn hiu ra c truyn n cc n v sau n trong mng.12/2012Khi nhn c cc tn hiu u vo, mt n v s nhn mi tn hiu vi trng s tng ng ri ly tng cc gi tr va nhn c. Kt qu s c a vo mt hm s gi l hm kch hot (Activation function) tnh ra tn hiu u ra. Cc n v khc nhau c th c cc hm kch hot khc nhau

7Gii thiu mng Neuron nhn to

8Hnh 2.1: n v mng neuron12/2012Khi nhn c cc tn hiu u vo, mt n v s nhn mi tn hiu vi trng s tng ng ri ly tng cc gi tr va nhn c. Kt qu s c a vo mt hm s gi l hm kch hot (Activation function) tnh ra tn hiu u ra. Cc n v khc nhau c th c cc hm kch hot khc nhau

8Gii thiu mng Neuron nhn to

Mng neuron truyn thngMng neuron hi quy12/2012Gii thiu mng Neuron nhn toTin trnh iu chnh cc trng s mng nhn bit c quan h gia u vo v u ra mong mun c gi l hc (learning) hay hun luyn (training)

12/2012Gii thut lan truyn ngcGii thut lan truyn ngc tm tp cc trng s thch hp cho mt mng neuron truyn thng nhiu lp bng phng php gim dcHm li ca gii thut lan truyn ngc c nh ngha tng qut nh sau

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12/2012 tng chnh ca gii thut l gi tr li s c lan truyn ngc t tng xut v tng nhp. Vi mi mu trong tp hun luyn, mng neuron c p dng tnh u ra sau gi tr dc ca hm li c tnh cho tng n v ca mng. Cui cng gii thut p dng phng php gim dc cp nhp cc gi tr trng s

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Gii thut lan truyn ngc12/2012Gii thut RPROPThc hin cp nhp cc trng s wij da vo thng tin v du ca cc o hm ring phn

Vi12/2012Cc gi tr cp nhp trng s tnh nh sau

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Gii thut RPROP12/2012p dng mng neuron vo d bo d liu chui thi gian

1512/2012Qu trnh xy dng mng neuron cho bi ton d bo chui thi gian gm 8 bc:La chn cc binThu thp d liuTin x l d liuPhn chia tp d liuXy dng cu trc mngXc nh tiu chun nh giHun luyn mngD on v ci tin

16p dng mng neuron vo d bo d liu chui thi gian12/2012Mng neuron tuy c kh nng xp x tt cc hm phi tuyn nhng khng th m hnh tt cc chui thi gian c tnh xu hng v tnh map dng mng neuron vo d bo d liu chui thi gian12/2012

M hnh lai (Hybrid Model)

12/2012M hnh lai (Hybrid Model)Gm ba mun:

Mun mng Neuron nhn toMun lm trn ly thaMoun lai12/2012Mun lm trn ly tha(Exponential Smoothing)M hnh nhn12/2012Mun lm trn ly tha(Exponential Smoothing)M hnh cng12/2012Mun lm trn ly tha(Exponential Smoothing)c lng ba h s , , Vt cn (Brute Force)S dng gii thut leo i (Hill Climbing)Leo i dc nht (Steepest Ascent Hill Climbing)Ti luyn m phng (Simulated Annealing)S dng kt hp hai phng php trnS dng phn mm R (thng qua phn mm RAndFriend)

12/2012Mun mng Neuron nhn to(Neuron Network)Cu trc mng: Mng Neuron truyn thngS node nhp bng s node n v bng chu k ca chui d liuGii thut hun luyn:Gii thut lan truyn ngc (Back Propagation)Gii thut RPROP (Resilient Propagation)

12/2012Mun lai (Hybrid Module)12/2012M hnh kh xu hng, kh ma

12/2012M hnh kh xu hng, kh maGm hai mun:

Mun mng Neuron nhn to (hin thc nh m hnh lai)Mun kh ma v kh xu hng12/2012Mun kh ma v kh xu hng Hin thc cc k thut sau: Kh xu hngK thut kh xu hng tuyn tnh: ta xp x chui thi gian bng mt nng thng hi quy at + b vi t l bin thi gian. ng vi mi t, ly Yt tr i at+bK thut kh xu hng bng ly hiu: Vi chui thi gian {Yt} c tnh xu hng, t Xt = Yt+1 Yt th chui thi gian {Xt} sinh ra l mt chui khng c tnh xu hng.12/2012Mun kh ma v kh xu hngHin thc cc k thut sau:Kh maK thut kh ma bng ly hiu theo ma: K thut ny thc hin vic bin i chui thi gian {Yt} thnh chui {Xt} nh sau Xt = Yt+s - Yt, vi s l ln mt chu k ca chui thi gian12/2012Mun kh ma v kh xu hngHin thc cc k thut sau:Kh maK thut kh ma bng RTMA(ratio to moving average): Ta s c lng ch s ma (seasonal index) ca cc thi on trong mt chu k ca chui thi gian ri ly gi tr ca mi thi on chia cho ch s ma tng ng ca n12/2012Thc nghimChng trnh c hin thc bng ngn ng lp trnh C# trong mi trng .NET Framework 4.0 v c thc nghim trn my c b vi x l Core 2 Duo, RAM 3GB.S dng cc b d liu: chui hnh khch hng thng ca hng hng khng PanAm (AirPassengers), mt kh Cacbonic Hawaii (Co2), s ngi cht hng thng v bnh phi Anh (Death), doanh s bn hng mt ca hng lu nim c (Fancy), lng tiu th kh t hng qu ti Anh (Gas).

12/2012Thc nghimCch thc thc nghim: chy cc m hnh vi s thay i cc thng s cu hnh. Mng Neuron nhn to:Hai gii thut RPROP BPS lng ti a epoches (1000-1500)K thut lm trn ly thac lng: s dng R, phng php kt hp vt cn v ti luyn m phngM hnh: m hnh cng v m hnh nhn12/2012Thc nghimCch thc thc nghim: chy cc m hnh vi s thay i cc thng s cu hnh. K thut kh xu hng, kh maKh xu hng: tuyn tnh v ly hiuKh ma: ly hiu v RTMA12/2012Thc nghimS ln chy: mi cu hnh chy ba ln v ly kt qu trung bnh, cu hnh cho kt qu d on tt nht s xem l kt qu ca m hnh so snh vi cc m hnh khcD liu nh gi chnh xc d bo: chu k cui ca chui thi gian.Thng s nh gi: MAPE, MSE, MAE12/2012

AirPassengers12/2012

Kt qu d bo chui AirPassengers12/2012

Co212/2012Kt qu d bo chui Co2

12/2012Death

12/2012Kt qu d bo cho chui Death

12/2012

Fancy12/2012

Kt qu d bo cho chui Fancy12/2012

Gas12/2012

Kt qu d bo cho chui Gas12/2012Kt lunTrong qu trnh thc hin ti, chng ti lm c nhng cng vic sau:Tm hiu vic p dng mng neuron d bo i vi d liu chui thi gian.Tm hiu cc phng php kh ma v kh xu hng i vi d liu chi thi gian nh: kh xu hng bng phng php ly hiu, kh xu hng bng phng php tuyn tnh, ly hiu theo ma, kh ma bng phng php RTMA.

12/2012Kt lunTm hiu cc k thut lm trn ly tha nh: lm trn ly tha gin n, lm trn ly tha Holt, lm trn ly tha Winters. Ngoi ra, tin hnh nghin cu v tm ra phng php c lng cc h s trong k thut lm trn ly tha Winters bng vic kt hp vt cn v phng php ti luyn m phng.Tm hiu phng thc gi hm R bng R(D)COM trong chng trnh C#.NET12/2012Kt lunNghin cu vic kt hp hai k thut: kh ma, kh xu hng v lm trn ly tha vi mng neuron nhm nng cao cht lng d bo i vi d liu chui thi gian c tnh ma v xu hng.Tin hnh hin thc hai m hnh d bo t cc nghin cu trn.

12/2012Kt lunTin hnh chy thc nghim vi nm b d liu thc t v nh gi, kim chng tnh ng n ca c s l thuyt cng nh qu trnh hin thc. Kt qu hai m hnh xut cho kt qu d bo tt hn mng neuron nhn to cho chui thi gian c tnh ma v xu hng

12/2012Hng pht trini vi m hnh lai, thay th phng thc khi to cc thng s i vi m hnh lm trn ly tha ang dng bng cc phng php tin tin hn nh phng php da trn hi quy (regression-based procedure) hay phng php da trn phn gii (decomposition-based) c th a ra d bo chnh xc hn. 12/2012Hng pht trini vi m hnh kh ma, kh xu hng kt hp mng neuron, ci tin phng php kh xu hng tuyn tnh c th p dng tt cho cc chui thi gian c xu hng mang hnh dng ng cong. p dng cc k thut kh ma tin tin gn y nh k thut X-12-ARIMA vo chng trnh

12/2012Ti liu tham khoG. Zhang, M. Qi.Trend Time-Series Modeling And Forecasting With Neural Networks. IEEE Transactions on Neural Network, vol. 19, no. 5, pages 808-816, 2008.G. Zhang, D. M. Kline. Quaterly Time-Series Forecasting With Neural Networks. IEEE Transactions on Neural Network, vol. 18, no. 6, pages 1800-1814, 2007.K. Lai, L. Yu, S. Wang, W. Huang. Hybridizing Exponential Smoothing And Neural Network For Financial Time Series Predication. ICCS06 Proceedings of the 6th international conference on Computional Science, vol. 4, pages 493-500, 2006. G. Zhang, M. Qi. Neural Network Forecasting For Seasonal And Trend Time Series. European Journal of Operational Research vol. 160, pages 501-514, 2005.J. E. Hanke, D. W. Wichenrn. Business Forcasting, Pearson Prentice Hall, ISBN 0-13-141290-6, 2005.Trn c Minh. Lun vn thc s Mng Neural Truyn Thng V ng Dng Trong D Bo D Liu. i hc quc gia H Ni, 2002F. Virili, B. Freisleben. Preprocessing Seasonal Time Series For Improving Neural Network Predictions. Proceesings of CIMA 99 Computational Intelligence Methods and Applications, Rochester-NY, pages 622-628, 1999.G. Zhang, M. Y. Hu. Neural Network Forecasting Of The British Pound/US Dollar Exchange Rate. Omega, International Journal of Management Science, 26, pages 495-506, 1998.T. M. Mitchell. Machine Learning, McGraw-Hill Science/ Engineering/ Math, ISBN 0070428077, 1997.I. Kaastra, M. Boyd. Designing A Neural Network For Forecasting Financial And Economic Time Series. Neurocomputing, vol. 10, pages 215-236, 1996.M. Riedmiller. Advanced Supervised Learning In Multi-layer Perceptrons From Backpropagation To Adaptive Learning Algorithms. Int. Journal of Computer Standards and Interfaces, 1994.

12/2012Q & A12/2012Thank you!Module Kh Ma V Xu Hng

Mng Neuron nhn to

Input

D liu hc

D liu kh ma v xu hng

Output

Gi tr d on do mng neuron sinh ra

Gi tr d on sau cng

(b) Kt qu d on ca m hnh lai gia mng neuron v lm trn ly tha

(a) Kt qu d on ca mng neuron nhn to

(c ) Kt qu d on ca mng neuron kt hp vi kh ma v xu hng.

(a) Kt qu d on ca mng neuron

(b) kt qu d on ca m hnh lai gia mng neuron v lm trn ly tha

(c) Kt qu d on ca mng neuron kt hp vi kh ma v xu hng

(a) Kt qu d on ca mng neuron

(b) Kt qu d on ca m hnh lai gia mng neuron v lm trn ly tha

(c) Kt qu d on ca mng neuron kt hp vi vi kh ma

(a) Kt qu d on ca mng neuron

(b) Kt qu d on ca m hnh lai gia mng neuron v lm trn ly tha

(c) Kt qu d on ca m hnh mng neuron kt hp vi kh ma v xu hng

(a) Kt qu d bo ca mng neuron

(c) Kt qu d on ca mng neuron kt hp vi kh ma v xu hng

(b) Kt qu d on ca m hnh lai gia mng neuron v lm trn ly tha