r - Forecasting several time series models, dplyr -

i use dplyr forecast several models. models fitted on time series data, each hour own model. ie, hour = 1 model, , hour = 18 model.

example:

# historical data - basis 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 wind , 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)   ) 

i can fit each model, hour hour so:

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 

but awesome this:

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) 

if want pack 1 call, can bind data single data.frame , split again in do call.

df <- rbind(df.h, data.frame(df.f, price=na)) res <- group_by(df, hour) %>% do({   hist <- .[!is.na(.$price), ]   fore <- .[is.na(.$price), c('hour', 'wind', 'temp')]   fit <- arima(hist$price, xreg = hist[,3:4], order = c(1,1,0))   data.frame(fore[], price=forecast.arima(fit, xreg = fore[ ,2:3])$mean) }) res