How do I avoid time leakage in my KNN model?

Question

I am building a KNN model to predict housing prices. I'll go through my data and my model and then my problem.

Data -

# A tibble: 81,334 x 4
   latitude longitude close_date          close_price
      <dbl>     <dbl> <dttm>                    <dbl>
 1     36.4     -98.7 2014-08-05 06:34:00     147504.
 2     36.6     -97.9 2014-08-12 23:48:00     137401.
 3     36.6     -97.9 2014-08-09 04:00:40     239105.

Model -

library(caret)
training.samples <- data$close_price %>%
  createDataPartition(p = 0.8, list = FALSE)
train.data  <- data[training.samples, ]
test.data <- data[-training.samples, ]

model <- train(
  close_price~ ., data = train.data, method = "knn",
  trControl = trainControl("cv", number = 10),
  preProcess = c("center", "scale"),
  tuneLength = 10
)

My problem is time leakage. I am making predictions on a house using other houses that closed afterwards and in the real world I shouldn't have access to that information.

I want to apply a rule to the model that says, for each value y , only use houses that closed before the house for that y . I know I could split my test data and my train data on a certain date, but that doesn't quite do it.

Is it possible to prevent this time leakage, either in caret or other libraries for knn (like class and kknn )?

Answer 1

In caret , createTimeSlices implements a variation of cross-validation adapted to time series (avoiding time leakage by rolling the forecasting origin). Documentation is here .

In your case, depending on your precise needs, you could use something like this for a proper cross-validation:

your_data <- your_data %>% arrange(close_date)

tr_ctrl <- createTimeSlices(
  your_data$close_price, 
  initialWindow  = 10, 
  horizon = 1,
  fixedWindow = FALSE)

model <- train(
  close_price~ ., data = your_data, method = "knn",
  trControl = tr_ctrl,
  preProcess = c("center", "scale"),
  tuneLength = 10
)

EDIT: if you have ties in the dates and want to having deals closed on the same day in the test and train sets, you can fix tr_ctrl before using it in train :

filter_train <- function(i_tr, i_te) {
  d_tr <- as_date(your_data$close_date[i_tr]) #using package lubridate
  d_te <- as_date(your_data$close_date[i_te])
  tr_is_ok <- d_tr < min(d_te)

  i_tr[tr_is_ok]
}

tr_ctrl$train <- mapply(filter_train, tr_ctrl$train, tr_ctrl$test)