如何预测和拟合多个时间序列的最优 model？

Question

I want to do batch forecasting among multiple series, for example, if I want to forecast time series with IDs that end with 1(1,11,21,31...), how can I do that?

Answer 1

Since you did not provide detailed information, I was not sure which forecasting method you want to use hence I give here an example of a univariate time series model:

Load required packages:

library(forecast)
library(dplyr)

We use example data from Rob Hyndman:

dta <- read.csv("https://robjhyndman.com/data/ausretail.csv")

Now change the column names:

colnames(dta) <- c("date", paste0("tsname_", seq_len(ncol(dta[,-1]))))

Select timeseries which end with 1:

dta_ends_with1 <- dplyr::select(dta, dplyr::ends_with("1"))

Create a ts object:

dta_ends_with1 <- ts(dta_ends_with1, start = c(1982,5), frequency = 12)

Specify how many steps ahead you want to forecast, here I set it to 6 steps ahead,

h <- 6

Now we prepare a matrix to save the forecast:

fc <- matrix(NA, ncol = ncol(dta_ends_with1), nrow = h)

Forecasting loop.

for (i in seq_len(ncol(dta_ends_with1))) {
  
fc[,i]  <- forecast::forecast(forecast::auto.arima(dta_ends_with1[,i]), 
                              h = h)$mean
  
}

Set the column names:

colnames(fc) <- colnames(dta_ends_with1)

head(fc)