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Anomaly Detection with Rolling Windows
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Anomaly Detection with Rolling Windows

HardPython

Problem

Given a stream of financial transactions, write an efficient algorithm to detect anomalies based on rolling statistical windows.

Implement detect_anomalies(transactions, window_size, z_threshold). For each transaction after the first complete preceding window, calculate the window's mean and sample standard deviation. Mark the transaction as anomalous when its absolute z-score is strictly greater than z_threshold. Return anomalous zero-based indices. If the window standard deviation is zero, mark the value anomalous only when it differs from the window mean. Do not evaluate transactions before a complete window exists.

Input values are numeric, window_size is an integer of at least 2, and z_threshold is nonnegative.

Constraints

  • 2 <= window_size <= len(transactions) or len(transactions) may be smaller than window_size
  • 0 <= len(transactions) <= 100000
  • Each transaction is an integer or floating-point number
  • 0 <= z_threshold
  • Use the sample standard deviation for each preceding window

Function Signature

def detect_anomalies(transactions, window_size, z_threshold):
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