|
| 1 | +""" |
| 2 | +Autocorrelation measures the correlation of a signal with a delayed |
| 3 | +copy of itself. It is widely used in time series analysis, signal |
| 4 | +processing, and statistics. |
| 5 | +
|
| 6 | +Reference: https://en.wikipedia.org/wiki/Autocorrelation |
| 7 | +""" |
| 8 | + |
| 9 | + |
| 10 | +def autocorrelation(data: list[float], lag: int) -> float: |
| 11 | + """ |
| 12 | + Calculate the autocorrelation of a time series at a given lag. |
| 13 | +
|
| 14 | + :param data: A list of numerical values representing the time series. |
| 15 | + :param lag: The number of time steps to shift the series. |
| 16 | + :return: The autocorrelation coefficient at the given lag. |
| 17 | +
|
| 18 | + >>> round(autocorrelation([1, 2, 3, 4, 5], 1), 4) |
| 19 | + 0.4 |
| 20 | + >>> round(autocorrelation([1, 2, 3, 4, 5], 0), 4) |
| 21 | + 1.0 |
| 22 | + >>> autocorrelation([1, 2, 3], 5) |
| 23 | + Traceback (most recent call last): |
| 24 | + ... |
| 25 | + ValueError: Lag must be less than the length of the data. |
| 26 | + """ |
| 27 | + if lag >= len(data): |
| 28 | + raise ValueError("Lag must be less than the length of the data.") |
| 29 | + |
| 30 | + n = len(data) |
| 31 | + mean = sum(data) / n |
| 32 | + variance = sum((x - mean) ** 2 for x in data) / n |
| 33 | + |
| 34 | + if variance == 0: |
| 35 | + raise ValueError("Variance of data is zero, autocorrelation undefined.") |
| 36 | + |
| 37 | + covariance = ( |
| 38 | + sum((data[i] - mean) * (data[i - lag] - mean) for i in range(lag, n)) / n |
| 39 | + ) |
| 40 | + |
| 41 | + return covariance / variance |
| 42 | + |
| 43 | + |
| 44 | +if __name__ == "__main__": |
| 45 | + import doctest |
| 46 | + |
| 47 | + doctest.testmod() |
| 48 | + data = [1, 2, 3, 4, 5, 4, 3, 2, 1] |
| 49 | + for lag in range(5): |
| 50 | + print(f"Lag {lag}: {autocorrelation(data, lag):.4f}") |
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