Time series convolutions

convolutions.R

# convolutions
convolve <- function(x, kernel) {
  size  <- length(kernel)
  x_pad <- c(rep(0, size - 1), x)
  out   <- rep(0, length(x))
  for (i in 1:length(x)) {
    out[i] <- x_pad[i:(i + (size - 1))] %*% kernel
  }
  return(out)
}


p <- 100
x <- numeric(P)
for (i in 2:p) {
  x[i] <- x[i-1] + rnorm(1)
}

x <- rnorm(100) + 2
dropkernel <- c(.75, .25)
growkernel <- c(.25, .75)
smoothing  <- exp(1:10) / sum(exp(1:10))
smoothing2 <- 20:1 / sum(20:1)

plot(x, type = "b")
lines(convolve(x, smoothing), col = "blue")
lines(convolve(x, smoothing2), col = "orange")


lines(convolve(x, dropkernel), col = "blue")
lines(convolve(x, growkernel), col = "red")