TheAlgorithms · zain-cs · Jul 3, 2026
diff --git a/maths/autocorrelation.py b/maths/autocorrelation.py
@@ -0,0 +1,50 @@
+"""
+Autocorrelation measures the correlation of a signal with a delayed
+copy of itself. It is widely used in time series analysis, signal
+processing, and statistics.
+
+Reference: https://en.wikipedia.org/wiki/Autocorrelation
+"""
+
+
+def autocorrelation(data: list[float], lag: int) -> float:
+    """
+    Calculate the autocorrelation of a time series at a given lag.
+
+    :param data: A list of numerical values representing the time series.
+    :param lag: The number of time steps to shift the series.
+    :return: The autocorrelation coefficient at the given lag.
+
+    >>> round(autocorrelation([1, 2, 3, 4, 5], 1), 4)
+    0.4
+    >>> round(autocorrelation([1, 2, 3, 4, 5], 0), 4)
+    1.0
+    >>> autocorrelation([1, 2, 3], 5)
+    Traceback (most recent call last):
+        ...
+    ValueError: Lag must be less than the length of the data.
+    """
+    if lag >= len(data):
+        raise ValueError("Lag must be less than the length of the data.")
+
+    n = len(data)
+    mean = sum(data) / n
+    variance = sum((x - mean) ** 2 for x in data) / n
+
+    if variance == 0:
+        raise ValueError("Variance of data is zero, autocorrelation undefined.")
+
+    covariance = (
+        sum((data[i] - mean) * (data[i - lag] - mean) for i in range(lag, n)) / n
+    )
+
+    return covariance / variance
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()
+    data = [1, 2, 3, 4, 5, 4, 3, 2, 1]
+    for lag in range(5):
+        print(f"Lag {lag}: {autocorrelation(data, lag):.4f}")