14.r语言时间序列分析案例未来股价预测代码

1、Q9.19.1.1:云同步盘/R 培训及资料/R15)xt,header=T)se(E:/baiduyun/df -read.table(9.1.dapc p(df,cor=TRUE)summary(pc, loadings=TRUE)由于前三个主成分的cumulative Proportion 已达 87%，为此可用该 3 个主成分来代替 8 个指标以达到降维的目的；第一个主成分是 资源消耗成分；因为所有资源包括固定资产，职工人数等为正值，有关效率的指标为负值。也可以理解为行业规模。第二个成分是消耗成分。因为只有消耗指标为正。第三个成分为能源利用效果。9.1.2pc.sort -pc$sco

2、re,1:3; pc.dist-dist(pc.sort)cl- hclust(pc.dist) plot(cl)rect.hclust(cl,k=5)9.2df = data.frame(X1=c(82.9,88.0,99.9,105.3,117.7,131.0,148.2,161.8,174.2,184.7), X2=c(92,93,96,94,100,101,105,112,112,112),X3=c(17.1,21.3,25.1,29.0,34.0,40.0,44.0,49.0,51.0,53.0), X4=c(94,96,97,97,100,101,104,109,111,111),

3、Y=c(8.4,9.6,10.4,11.4,12.2,14.2,15.8,17.9,19.6,20.8);pc -p(X1+X2+X3+X4, data = df, cor = TRUE);pc.summ- summary(pc,loadings = TRUE);第一主成分达到 98.6%， pre-predict(pc)df$z1-pre,1ol -lm(Yz1, data=df) pc.summ- summary(ol) pc.summbeta-coef(ol)A-loadings(pc)x.bar - pc$center x.sd - pc$scalecoef-beta2*A,1/x.s

4、dbeta0 - beta1- sum(x.bar * coef) result - c(beta0, coef)result9.3x - c(1, 0.846, 0.805, 0.859, 0.473, 0.398, 0.301, 0.382, 0.846,1, 0.881, 0.826, 0.376, 0.326, 0.277, 0.277, 0.805, 0.881,1, 0.801, 0.38, 0.319, 0.237, 0.345, 0.859, 0.826, 0.801,1, 0.436, 0.329, 0.327, 0.365, 0.473, 0.376, 0.38, 0.43

5、6,1, 0.762, 0.73, 0.629, 0.398, 0.326, 0.319, 0.329, 0.762,1, 0.583, 0.577, 0.301, 0.277, 0.237, 0.327, 0.73, 0.583,1, 0.539, 0.382, 0.415, 0.345, 0.365, 0.629, 0.577, 0.539,1)names - unlist(strsplit(身高长 上肢长 下肢长 体重颈围胸宽, )(r - matrix(x, nrow = 8, dimnames = list(names, names)pc3 -p(covmat = r)summary(pc3, loadings = T)9.4df-read.table(course.data)factfact(.,factors=1,data=df)(.,factors=2,data=df)发现因子为 2 时比较合