In ahorawzy/TFTSA: Traffic Flow Time Series Analysis

library(TFTSA)
library(ggplot2)

数据准备

tmlzzloess <- read.csv("D://data//thesis//201610//tmlzzloess.csv",header = T)
tmlzznew <- read.csv("D://data//thesis//201610//tmlzznew.csv",header = T)

rownames(tmlzzloess) <- tmlzzloess[,1]
tmlzzloess <- tmlzzloess[,-1]
rownames(tmlzznew) <- tmlzznew[,1]
tmlzznew <- tmlzznew[,-1]

tmlzz和tmlzznew的第一列均为日期，需要将其换为行名。

tmlobj <- tmlzznew[6,]
tmlbase <- tmlzzloess[-6,]

选择预测序列为10月6日，其余的序列作为样本库

pre_imbalance <- flow_knn(obj = tmlobj,base = tmlbase,start = 73,k = 5,lag_duration = 30,fore_duration = 8,
                          save_detail = "D:\\交大云同步\\论文\\大论文\\实验\\结果表\\imbalance2.csv",imbalance=T)

flow_forecastplot(tmlobj,pre_imbalance)

ggsave("D:\\王致远\\论文\\大论文\\实验\\绘图\\K近邻imbalance预测.jpg",width=7.29,height=4.5,dpi=600)

ggsave(file="plot/05_imbalance_loess.jpg",width=7.29,height=4.5,dpi=600)

flow_evaluate(tmlobj,pre_imbalance)

残差分析

res_imbalance <- tmlobj[73:288] - pre_imbalance[73:288]
x <- plot(73:288,res_imbalance)
x <- abline(h=0)

对称预测与非对称预测对比图

pre1006 <- TFTSA::flow_knn(tmlobj,tmlbase,start = 73,k = 3,lag_duration = 22,fore_duration = 6)

preall <- rbind(tmlobj,pre1006,pre_imbalance)
preall <- t(preall)
preall <- as.data.frame(preall)
preall[4] <- 1:288
names(preall) <- c("real value","normal","asymmetric","timestamp")
preall <- melt(preall,id.vars = "timestamp")
ggplot(preall,aes(x=timestamp,y=value,group=variable,color=variable))+
  geom_line()+geom_point()+
  scale_colour_manual(values=c("steelblue","orange","red"))+
  xlab("Timestamp")+ylab("Traffic volume")+labs(color="Legend")+
  scale_x_continuous(breaks = seq(0,288,24))+
  scale_y_continuous(breaks = seq(0,120,20))+
  theme_bw()

ggsave("D:\\交大云同步\\论文\\小论文\\4_基于KNN方法的短时交通流序列非对称损失预测\\绘图\\K近邻全家福2V2.jpg",width=7.29,height=4.5,dpi=600)

ggsave(file="plot/05_balance_imbalance_loess.jpg",width=7.29,height=4.5,dpi=600)

ggplot(preall,aes(1:288,preall$real))+geom_point(colour="steelblue")+geom_line(colour="steelblue")+
  geom_line(aes(1:288,preall$balance),colour="orange",size=1)+
  geom_line(aes(1:288,preall$imbalance),colour="red",size=1)+
  xlab("Timestamp")+ylab("Traffic flow rate")+
  scale_x_continuous(breaks = seq(0,288,24))+
  scale_y_continuous(breaks = seq(0,120,20))

ggsave("D:\\王致远\\论文\\大论文\\实验\\绘图\\K近邻全家福.jpg",width=7.29,height=4.5,dpi=600)

ahorawzy/TFTSA documentation built on May 13, 2019, 12:18 p.m.