In ahorawzy/TFTSA: Traffic Flow Time Series Analysis

今天的实验主要是对10月1日交通流量进行BoxJenkins建模。

读取数据

library(TFTSA)
library(forecast)
library(ggplot2)

tml1001all <- read.csv("D:\\data\\thesis\\201610\\tml1001all.csv",header = T)
dim(tml1001all)

names(tml1001all)

tml1001 <- tml1001all[,c(5,15)]
names(tml1001) <- c("时间序号","机动车流量")
names(tml1001)

ggplot(tml1001,aes(x=tml1001$时间序号,y=tml1001$机动车流量))+
  geom_point(colour="steelblue")+
  geom_line(colour="steelblue")

flow1001 <- tml1001$机动车流量
dif1001 <- diff(flow1001)

模型识别

EACF

eacf(dif1001)

识别出差分序列似乎是MA(1)模型

forecast包自动识别

fit_auto1 <- auto.arima(dif1001)
fit_auto1

fit_auto2 <- auto.arima(flow1001)
fit_auto2

识别出差分序列是MA(1)型，原序列为IMA(1,1)型，系数显著不为0

子集ARMA(p,q)模型

set.seed(123)
fit_auto3 <- armasubsets(y=dif1001,nar=5,nma=5,y.name='test',ar.method = "ols")
plot(fit_auto3)

可以看出差分序列是MA(1)型的

参数估计

fit1 <- arima(flow1001,order=c(0,1,1),method="CSS")
fit1

预测效果

fit1res <- residuals(fit1)
fit1fit <- fitted(fit1)

length(fit1fit)
length(fit1res)

plot(x=tml1001$时间序号,y=tml1001$机动车流量,type="b")
lines(fit1fit,col="red")

fit1fit <- as.vector(fit1fit)
fit1res <- as.vector(fit1res)
fit1df <- cbind(tml1001,fit1fit,fit1res)
names(fit1df)[c(3,4)] <- c("拟合值","残差")
names(fit1df)

#write.csv(fit1df,"D:\\data\\thesis\\201610\\tmldata\\fitarima.csv")

ggplot(fit1df,aes(x=fit1df$时间序号,y=fit1df$机动车流量))+
  geom_point(colour="steelblue")+
  geom_line(colour="steelblue")+
  geom_line(aes(x=fit1df$时间序号,y=fit1df$拟合值),colour="red",size=1)+
  #xlab("时间序号")+ylab("车流量")+scale_color_hue()+
  scale_color_manual(values=c("steelblue","red"))+
  xlab("Timestamp")+ylab("Traffic volume")+labs(color="Legend")+
  scale_x_continuous(breaks = seq(0,288,24),limits = c(0,288))+
  scale_y_continuous(breaks = seq(0,120,20))

#ggsave("D:\\王致远\\论文\\大论文\\实验\\绘图\\ARIMA结果.jpg",width=7.29,height=4.5,dpi=600)

fit1df <- fit1df[,c(1,3,2)]
#fit1df <- fit1df[,1:3]
names(fit1df) <- c("Timestamp","real value","forecast value")
fit1melt <- reshape2::melt(fit1df,id.vars="Timestamp")
ggplot(fit1melt,aes(x=fit1melt$Timestamp,y=fit1melt$value,group=fit1melt$variable,color=fit1melt$variable))+
  geom_line()+geom_point()+
  scale_color_manual(values=c("steelblue","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(file="plot/05_arima.jpg",width=7.29,height=4.5,dpi=600)

TFTSA::flow_evaluate(fit1df$机动车流量,fit1df$拟合值)

残差分析

plot(1:288,fit1res)

Box.test(fit1res)

qqnorm(fit1res)
qqline(fit1res)

acf(fit1res)

pacf(fit1res)

fit1density <- density(fit1res)
plot(fit1density)

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