金融数据挖掘

1、基于GLM （广义线性模型）的数据分析SAS里的GLM应用在实际中比较广泛，对数据的分析具有比较强的普适 性。趋势面回归分析（Trend Analysis）是以多元回归分析为理论基础的一 种预测 与统计技术。它用空间坐标法进行多项式回归，从中估计出最佳的 回归模型，因 此也被称为趋势面分析，当不知道手中的数据呈线性还是非 线性相关时，可以采用趋势面数据分析方法，以便找出拟合数据的最佳统计 预测模型。本文运用GLM对一定的数据进行GLM分析。一、数据与要求此处选取15名吧不同程度的烟民的每日饮酒（啤酒）量与心电图指标（zb）的对应数据。然后设法建立zb与日抽烟量（X） /支和日饮酒量（y） /升

2、之间的关系。序号组另IJ日抽烟量（x） /支日饮酒量（y） /升心电图指标/ 、113010280212511260313513330414014400514514410622012270721811210822512280922513300102231329011340144101234515420133481642514350184501535519470二、运用GLM过程进行趋势面分析1 .趋势分析的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;2.四种分析模型结果（1）一阶趋势模型Dependent Variable: zb源变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 源变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）四阶趋势模型Dependent Vari

13、able: zb 源变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