我希望使用dplyr来预测多个模型。这些模型是基于时间序列数据拟合的,因此每个小时都有自己的模型。即,hour = 1是一个模型,而hour = 18是另一个模型。
示例:
我可以逐小时地安排每个模型,如下所示:
但如果能做到这样就太棒了:
示例:
# Historical data - Basis for the models:
df.h <- data.frame(
hour = factor(rep(1:24, each = 100)),
price = runif(2400, min = -10, max = 125),
wind = runif(2400, min = 0, max = 2500),
temp = runif(2400, min = - 10, max = 25)
)
# Forecasted data for wind and temp:
df.f <- data.frame(
hour = factor(rep(1:24, each = 10)),
wind = runif(240, min = 0, max = 2500),
temp = runif(240, min = - 10, max = 25)
)
我可以逐小时地安排每个模型,如下所示:
df.h.1 <- filter(df.h, hour == 1)
fit = Arima(df.h.1$price, xreg = df.h.1[, 3:4], order = c(1,1,0))
df.f.1 <- filter(df.f, hour == 1)
forecast.Arima(fit, xreg = df.f.1[ ,2:3])$mean
但如果能做到这样就太棒了:
fits <- group_by(df.h, hour) %>%
do(fit = Arima(df.h$price, order= c(1, 1, 0), xreg = df.h[, 3:4]))
df.f %>% group_by(hour)%>% do(forecast.Arima(fits, xreg = .[, 2:3])$mean)