R Studio - How to predict row-by-row and use previous prediction in next one - linear model

Question

Sorry if this is unclear, had trouble titling this.

Basically I have a linear model that predicts sales and one of the factors is the previous 10 days of sales. So, when predicting for the next month, I need an estimated number for what the "previous 10 days of sales" is for each day in the month.

I want to use the model to generate these numbers - so, for the first day I'm trying to predict, I have the actual number for the last 10 days of sales. For the day after that, I have 9 days of real data, plus the one predicted number generated. For the day after that, 8 days of real data and two generated, etc.

Not quite sure how to implement this and would appreciate any help. Thanks so much.

Answer 1

The first thing that came to mind would be a moving average using the predicted data. This gets hard to defend though once you're averaging only predicted data but its a place to start.

moving.average = 0

test.dat = rnorm(100, 10,2)

for(i in 1:30){
  moving.average[i] = mean(test.dat[i:i+10])
}

Hope this is helpful

Answer 2

Kathy, get your first 10 data points from... where-ever. Seed your prediction with it.

initialization <- c(9.463, 9.704, 10.475, 8.076, 8.221, 8.509, 
            10.083, 9.572, 8.447, 10.081)

prediction = initialization

Here's a silly prediction function that uses the last 10 values:

predFn <- function(vec10){
  stopifnot(length(vec10) == 10)
  round(mean(vec10) + 1 , 3)
}

Although I usually like to use the map family, this one seems like it wants to be a loop

for(i in 11:20){
  lo = i - 10
  hi = i - 1
  prediction[i] <- predFn(prediction[lo:hi])
}

What did we get?

prediction
#  [1]  9.463  9.704 10.475  8.076  8.221  8.509 10.083  9.572  8.447 10.081 10.263 10.343 10.407 10.400 10.633 10.874 11.110 11.213
# [19] 11.377 11.670