hvplot.plotting.lag_plot#
- hvplot.plotting.lag_plot(data, lag=1, **kwds)[source]#
Lag plot for time series.
A lag plot is a scatter plot of a time series against a lag of itself. It helps in visualizing the temporal dependence between observations by plotting the values at time t on the x-axis and the values at time t + lag on the y-axis.
- Parameters:
- dataSeries or DataFrame
The time series to visualize.
- lagint, optional
Lag length of the scatter plot. Default is 1.
- **kwdsoptional
hvplot.scatter options
- Returns:
- objHoloViews object
The HoloViews representation of the plot.
Examples#
Simple lag plot#
Lag plots are most commonly used to look for patterns in time series data. This example shows a lag plot constructed from a time series. The lag plot suggests that the time series exhibits positive autocorrelation at a lag of 1 (the default setting).
import hvplot.pandas # noqa
import numpy as np
import pandas as pd
np.random.seed(42)
series = pd.Series(np.cumsum(np.random.randn(100)))
series.hvplot.line(title="Time series", frame_width=300) +\
hvplot.plotting.lag_plot(series, title="Lag plot", frame_width=300)
With multiple variables#
In this example, we use a lag of 52 to compare annual patterns, as the dataset contains weekly data.
import hvplot
df = hvplot.sampledata.stocks("pandas").set_index("date")
hvplot.plotting.lag_plot(
df.loc["2020":], lag=52, data_aspect=1,
xlim=(0, 6), ylim=(0, 6)
)
Use of lag#
For this dataset built from a random walk, the autocorrelation is maximum with a lag of 1.
import hvplot
import numpy as np
import pandas as pd
np.random.seed(42)
series = pd.Series(np.cumsum(np.random.randn(300)))
plot_opts = dict(frame_width=200)
hvplot.plotting.lag_plot(series, lag=1, title="lag=1 (default)", **plot_opts) +\
hvplot.plotting.lag_plot(series, lag=5, title="lag=5", **plot_opts) +\
hvplot.plotting.lag_plot(series, lag=100, title="lag=50", **plot_opts)
Different kinds of autocorrelation#
import hvplot
import numpy as np
import pandas as pd
np.random.seed(42)
df = pd.DataFrame({
"positive": np.cumsum(np.random.randn(100)),
"noise": np.random.randn(100),
"cyclic": np.sin(np.linspace(0, 4 * np.pi, 100))
})
plot_opts = dict(frame_width=200, shared_axes=False)
hvplot.plotting.lag_plot(df["noise"], title="No autocorrelation", **plot_opts) +\
hvplot.plotting.lag_plot(df["positive"], title="Positive autocorrelation", **plot_opts) +\
hvplot.plotting.lag_plot(df["cyclic"], title="Cyclic autocorrelation", **plot_opts)