金融數(shù)據(jù)挖掘

1、基于glm （廣義線性模型）的數(shù)據(jù)分析sas里的glm應(yīng)用在實(shí)際中比較廣泛，對(duì)數(shù)據(jù)的分析具有比較強(qiáng)的普適 性。趨勢(shì)面回歸分析（trend analysis）是以多元回歸分析為理論基礎(chǔ)的一 種預(yù)測(cè) 與統(tǒng)計(jì)技術(shù)。它用空間坐標(biāo)法進(jìn)行多項(xiàng)式回歸，從中估計(jì)出最佳的 回歸模型，因 此也被稱為趨勢(shì)面分析，當(dāng)不知道手中的數(shù)據(jù)呈線性還是非 線性相關(guān)時(shí)，可以采用趨勢(shì)面數(shù)據(jù)分析方法，以便找出擬合數(shù)據(jù)的最佳統(tǒng)計(jì) 預(yù)測(cè)模型。本文運(yùn)用glm對(duì)一定的數(shù)據(jù)進(jìn)行g(shù)lm分析。一、數(shù)據(jù)與要求此處選取15名吧不同程度的煙民的每日飲酒（啤酒）量與心電圖指標(biāo)（zb）的對(duì)應(yīng)數(shù)據(jù)。然后設(shè)法建立zb與日抽煙量（x） /支和日飲酒量（y） /升

2、之間的關(guān)系。序號(hào)組另ij日抽煙量（x） /支日飲酒量（y） /升心電圖指標(biāo)/ 、113010280212511260313513330414014400514514410622012270721811210822512280922513300102231329011340144101234515420133481642514350184501535519470二、運(yùn)用glm過(guò)程進(jìn)行趨勢(shì)面分析1 .趨勢(shì)分析的glm程序data beer;input obsn x y zb; cards;01 30 10 28002 25 11 26003 35 13 33004 40 14 40005 45 1

3、441006 20 12 27007 18 11 21008 25 12 28009 25 13 30010 23 13 29011 40 1441012 45 15 42013 48 16 42514 50 18 45015 55 19 470 proc glm; model zb=x y/p;proc glm;model zb=x y x*x x*y y*y/p;proc glm;model zb=x y x*x*x x*x*y x*y*y y*y*y/p;proc glm;model zb=x y x*x*x x*x*y x*y*y y*y*y x*x*x*x x*x*x*y x*x*y

4、*y x*y*y*y y*y*y*y/p; run;2.四種分析模型結(jié)果（1）一階趨勢(shì)模型dependent variable: zb源變量自由度平方和均值3sum ofsourcedfsquaresmean squaref valuepr fmodel90615.2099345307.60497127.19fx189541.5655889541.56558251.36 f114652.2435114652.2435141.13x |t|intercept64.0499938033.065399191.940.07665.383855650.839475676.41 f18666.167161

5、07.75 fx189541.5655889541.56558516.86 fx1965.2913631965.29136315.570.0426y1127.4395437127.43954370.740.4133x*x143.662297243.66229720.250.6277x*y1242.0343234242.03432341.400.2675y*y149.843031649.84303160.290.6047standardparameterestimateerrort valuepr|t|intercept-262.7664793109.1074817-2.410.0394x16.

6、06997796.80786202.360.0426y23.539132727.44498670.860.4133x*x0.06387730.12723830.500.6277x*y-1.16510160.9857119-1.180.2675y*y1.16733622.17629820.540.60476270.0000000255.125602414.8743976observation12345observed280.0000000260.0000000330.0000000400.0000000410.0000000predicted279.4168700258.6814596351.0

7、997183388.1251282414.0657505residual0.58313001.3185404-21.099718311.8748718-4.06575057210.0000000216.6773768-6.677376868280.0000000300.00000009290.000000010410.000000011420.000000012425.000000013450.00000001415470.0000000279.9417834303.5367795295.5572467388.1251282419.0280585436.4318573453.755470646

8、5.43176990.0582166-3.5367795-5.557246721.87487180.9719415-11.4318573-3.75547064.5682301-0.0000001559.164195-0.000000-0.3542052.694808dependent variable: zb 源變量自由度平方和均值f值概率值sourcedfsum ofsquaresmean squaref value pr fmodel93393.4641415565.5773683.21 fx189541.5655889541.56558478.66 fx11643.3470811643.

9、3470818.780.0180197.474017197.4740171.060.3343y10.56x*x*x1105.516422105.5164220.4741x*x*y1113.710330113.7103300.610.4580x*y*y1146.610010146.6100100.780.4018y*y*y1173.116161173.1161610.930.3642standardparameterestimateerror t valuepr|t|intercept-166.007458982.37772231-2.020.0786x11.13825983.757952332

10、.960.0180y15.778434015.357039051.030.3343x*x*x-0.01541320.02052250-0.750.4741x*x*y0.12031870.154323330.780.4580x*y*y-0.34167860.38595313-0.890.4018y*y*y0.31348940.325876140.960.364215470.0000000463.53108336.4689167observationobservedpredictedresidual1280.0000000281.0906363-1.09063632260.0000000256.0

11、4837833.95162173330.0000000351.8935219-21.89352194400.0000000390.57078969.42921045410.0000000409.23096520.76903486270.0000000257.998349012.00165107210.0000000220.0483966-10.04839668280.0000000275.01603684.98396329300.0000000299.47099730.529002710290.0000000295.8228899-5.822889911410.0000000390.57078

12、9619.429210412420.0000000420.5758580-0.575858013425.0000000437.4437284-12.443728414450.0000000455.6875798-5.6875798-0.0000001496.535862-0.000000-0.3575452.686333sum of residualssum of squared residualssum of squared residuals - error ssfirst order autocorrelationdurbin-watson d4）四階趨勢(shì)模型dependent vari

13、able: zb 源變量自由度平方和均值f值概率值sum ofsourcedfsquaresmean squaref valuepr fmodel1194480.319198589.1199362.900.0029error3409.68081136.56027corrected total1494890.00000r-squarecoeff varroot msezb mean0.9956833.367695 11.68590347.0000sourcedftype i ssmean squaref valuepr fx189541.5655889541.56558655.690.0001y

14、11073.644351073.644357.860.0676x*x*x12078.776642078.7766415.220.0299x*x*y1508.85526508.855263.730.1491x*y*y117.5061417.506140.130.7440y*y*y1173.11616173.116161.270.3421x*x*x*x152.9156652.915660.390.5777x*x*x*y1193.81980193.819801.420.3192x*x*y*y1452.42798452.427983.310.1663x*y*y*y140.3287940.328790.

15、300.6246y*y*y*y1347.36281347.362812.540.2090sourcedftype iii ssmean squaref valuepr fx153.834735453.83473540.390.5746y118.442245818.44224580.140.7376x*x*x1707.3985134707.39851345.180.1073x*x*y1688.7276032688.72760325.040.1104x*y*y1669.2155979669.21559794.900.1137y*y*y1614.9897506614.98975064.500.123

16、9x*x*x*x173.525495773.52549570.540.5162x*x*x*y121.572098721.57209870.160.7176x*x*y*y1150.8940383150.89403831.100.37040.2581x*y*y*y1264.7516451264.75164511.94y*y*y*y1347.3628138347.36281382.540.2090standardparameterestimateerrort valuepr |t|intercept-748.5352475602.9093096-1.240.3026x21.526850134.285

17、57060.630.5746y63.4532525172.66693160.370.7376x*x*x1.11290830.48897822.280.1073x*x*y-7.84664423.4939960-2.250.1104x*y*y17.69195997.99199322.210.1137y*y*y-12.81731806.0398396-2.120.1239x*x*x*x-0.00528950.0072088-0.730.5162x*x*x*y-0.03396280.0854515-0.400.7176x*x*y*y0.42181270.40127851.050.3704x*y*y*y

18、-1.09527330.7866207-1.390.2581y*y*y*y0.84110790.52737831.590.2090observation1234567891011121314observed280.0000000260.0000000330.0000000400.0000000410.0000000270.0000000210.0000000280.0000000300.0000000290.0000000410.0000000420.0000000425.0000000450.0000000predicted280.6428697254.9148649336.2353148399.8451524409.0029100265.5623644212.0079405287.4716063292.6701245295.8090433399.8451524428.1747562422.5228478450.5733972residual-0.64286975.0851351-6.23531480.15484760.99709004.4376356-2.0079405-7.47160637.3298755-5.80