Lag Plot#

Lag plots help reveal autocorrelation in time series data by comparing each observation with a lagged version of itself. In this example, we use a synthetic sine wave signal with added noise to illustrate the concept.

import numpy as np
import pandas as pd
import hvplot.pandas  # noqa

np.random.seed(1)
t = pd.date_range("2025-01-01", periods=200)
data = pd.Series(np.sin(np.linspace(0, 20, 200)) + np.random.normal(0, 0.2, 200), index=t)

hvplot.plotting.lag_plot(data)

import numpy as np
import pandas as pd
import hvplot.pandas  # noqa
hvplot.extension("matplotlib")

np.random.seed(1)
t = pd.date_range("2025-01-01", periods=200)
data = pd.Series(np.sin(np.linspace(0, 20, 200)) + np.random.normal(0, 0.2, 200), index=t)

hvplot.plotting.lag_plot(data)