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Given a data frame with multiple columns which contain time series data, let’s say that we are interested in executing an automatic forecasting algorithm on a number of columns. Furthermore, we want to train the model on a particular number of observations and assess how well they forecast future values. Based upon those testing procedures, we will estimate the full model. This is a fairly simple undertaking, but let’s walk through this task. My preference for such procedures is to loop through each column and append the results into a nested list.

First, let’s create some data.

ddat <- data.frame(date = c(seq(as.Date("2010/01/01"), as.Date("2010/03/02"), by=1)),
value1 = abs(round(rnorm(61), 2)),
value2 = abs(round(rnorm(61), 2)),
value3 = abs(round(rnorm(61), 2)))
tail(ddat)

We want to forecast future values of the three columns. Because we want to save the results of these models into a list, lets begin by creating a list that contains the same number of elements as our data frame.

lst.names <- c(colnames(data))
lst <- vector("list", length(lst.names))
names(lst) <- lst.names
lst

I’ve gone ahead and written a user defined function that handles the batch forecasting process. It takes two arguments, a data frame and default argument which specifies the number of observations that will be used in the training set. The model estimates, forecasts, and diagnostic measures will be saved as a nested list and categorized under the appropriate variable name.

batch <- function(data, n_train=55){

lst.names <- c(colnames(data))
lst <- vector("list", length(lst.names))
names(lst) <- lst.names

for( i in 2:ncol(data) ){

lst[[1]][["train_dates"]] <- data[1:(n_train),1]
lst[[1]][["test_dates"]] <- data[(n_train+1):nrow(data),1]

est <- auto.arima(data[1:n_train,i])
fcas <- forecast(est, h=6)$mean acc <- accuracy(fcas, data[(n_train+1):nrow(data),i]) fcas_upd <- data.frame(date=data[(n_train+1):nrow(data),1], forecast=fcas, actual=data[(n_train+1):nrow(data),i]) lst[[i]][["estimates"]] <- est lst[[i]][["forecast"]] <- fcas lst[[i]][["forecast_f"]] <- fcas_upd lst[[i]][["accuracy"]] <- acc cond1 = diff(range(fcas[1], fcas[length(fcas)])) == 0 cond2 = acc[,3] >= 0.025 if(cond1|cond2){ mfcas = forecast(ma(data[,i], order=3), h=5) lst[[i]][["moving_average"]] <- mfcas } else { est2 <- auto.arima(data[,i]) fcas2 <- forecast(est, h=5)$mean

lst[[i]][["estimates_full"]] <- est2
lst[[i]][["forecast_full"]] <- fcas2

}
}
return(lst)
}

batch(ddat)

This isn’t the prettiest code, but it gets the job done. Note that lst was populated within a function and won’t be available in the global environment. Instead, I chose to simply print out the contents of the list after the function is evaluated.