TimeSeries

Inherits from: gwpy.timeseries.TimeSeries

Extended TimeSeries with full gwexpy functionality.

Key Extensions

Statistics and Correlation

• correlation(other, method="pearson", ...) Compute correlation with another TimeSeries. Methods: "pearson", "kendall", "mic", "distance".

• partial_correlation(other, controls=None, ...) Compute partial correlation controlling for third-party variables.

• fastmi(other, grid_size=128) Compute mutual information using the FastMI (FFT-based) estimator.

• granger_causality(other, maxlag=5) Test for Granger causality between series.

Signal Processing

• hilbert() / envelope() Compute the analytic signal and its amplitude envelope.

• mix_down(f0) Demodulate the signal at a specific carrier frequency.

• fft(mode="steady"|"transient", ...) Enhanced FFT with zero-padding and window management options.

Modeling and Preprocessing

• arima(order=(p,d,q)) Fit an ARIMA time-series model.

• impute(method="interpolate") Handle missing values (NaNs) in the data.

• standardize(method="zscore") Rescale data to zero mean and unit variance.

Examples

from gwexpy.timeseries import TimeSeries
ts = TimeSeries.fetch_open_data('H1', 1126259446, 1126259478)

# Compute nonlinear correlation
mic_score = ts.correlation(other_ts, method="mic")

# Standardize and compute envelope
env = ts.standardize().envelope()

Pickle / shelve portability

.. warning:: Never unpickle data from untrusted sources. pickle/shelve can execute arbitrary code on load.

gwexpy pickling prioritizes portability: unpickling returns GWpy types so that loading does not require gwexpy to be installed.