Spike detection anomalies
Given a numeric stream and a window size, flag anomaly indices. For each index `i` with at least `window` prior values, compute the mean and population standard deviation of the immediately preceding `window` values (indices `i-window .. i-1`); index `i` is a spike if `stream[i] > mean + 2*std`. Return the list of spike indices in increasing order. Indices before a full trailing window exists are never flagged.
Implement
detect_spikes(stream: list[int], window: int) → list[int]Examples
in
[[1,1,1,1,50],4]out[4]What a strong answer looks like
State your approach and its time/space complexity out loud before you optimize. Handle the edge cases (empty input, duplicates, overflow), and say why you chose this over the brute force. Green tests are the floor, not the grade.
0:00 of about 25 min
solution.py
InputExpectedGot
[[1,1,1,1,50],4][4]not run yetsample