R语言神经网络模型预测车辆数量时间序列

## # A tibble: 6 x 2
##         Date Actual
##       <dttm>  <dbl>
## 1 2016-01-11     26
## 2 2016-01-18     27
## 3 2016-01-25     28
## 4 2016-02-01     22
## 5 2016-02-08     27
## 6 2016-02-15     31
 dat%>%ggplot(aes(Date,Actual))+labs(title = "耐久性车辆预测", x = "时间", y = "耐用车辆数量",subtitle = " 2016 & 2017年数据") +

#数据必须为数字，tsclean函数才能正常工作dat%>%dplyr::rename(Date=Week)%>%na.omit()dat_ts = ts(dat["实际数量"])  nn(data, lambda=0.5)Average of 20 networks, each of which isa 1-1-1 network with 4 weightsoptions were - linear output unitssigma^2 estimated as 0.2705