Algorithm 1.

Fourier transform based imputation

Input:
	Data matrix, Y = {V1, V2, …, VN}, is a set of variables, where each Vi = {v1, v2, …, vT}, and vj is the jth data point;
Output:
	Data matrix, Y with imputed values
1:	for each V in Y do
2:	ts = min(j), where vj is missing, 1 ≤ j ≤ T;
3:	while ts ≠ ∅ do
4:	te = min(j), where vj is non-missing, ts ≤ j ≤ T;
5:	F = DFT(v1, v2, …, v(ts−1));
6:	u = IDFT(F, te);
7:	vj = uj, where ts ≤ j ≤ te;
8:	ts = min(j), where vj is missing and 1 ≤ j ≤ T;
9:	end while
10:	end for
11:	return Y