Batch Forecasting in R

痛定思痛。 2022-08-20 13:20 9阅读 0赞

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)))
    head(ddat)
    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.