split a time series data to windows of length 10. then for each window, find the closest 5 preceding neighbors using weighted euclidean distance in r

main.r
library(zoo)

# create a time series data (assuming it's stored in a vector called "data")
data <- rnorm(100)

# split the data into windows of length 10
windows <- rollapply(data, width = 10, align = "right", FUN = identity)
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main.r
library(proxy)

# define a function to find the closest 5 neighbors for a given window
find_neighbors <- function(window) {
  # create a matrix of the weighted data within the window
  window_weighted <- as.matrix(weighted_dist(window, diag = TRUE))

  # calculate the distances from each row to the last row (i.e. the "target")
  distances <- as.vector(Dist(window_weighted[-nrow(window_weighted),], window_weighted[nrow(window_weighted),], method = "euclidean"))

  # return the indices of the 5 closest neighbors
  return(tail(order(distances), 5))
}

# loop through each window and find the closest neighbors
for (i in seq_along(windows)) {
  neighbors <- find_neighbors(windows[[i]])
  print(paste("Window", i, ": Closest neighbors are", neighbors))
}
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