Plotting¶

dynaris provides minimalist visualization functions. All plots are accessible through the DLM.plot() method via the kind parameter.

Plot kinds¶

Overlay filtered state estimates on the observed data.

dlm.fit(y)
dlm.plot(kind="filtered")

Shows the Kalman filter’s one-step-ahead estimates with confidence intervals.

Display smoothed (retrospective) state estimates.

dlm.fit(y).smooth()
dlm.plot(kind="smoothed")

Smoothed estimates have lower variance because they use the full dataset.

Fan chart showing the forecast mean and confidence bands, with recent historical observations for context.

dlm.forecast(steps=24)
dlm.plot(kind="forecast", n_history=36)

The n_history parameter controls how many past observations appear.

Residual diagnostic panel with QQ-plot, ACF, and histogram.

dlm.plot(kind="diagnostics")

Use this to check whether the model’s assumptions hold.

Decompose the series into individual state components. Requires smoothing first and a mapping of component names to state dimensions:

dlm.smooth()
dlm.plot(kind="components", component_dims={
    "Level": 0,
    "Slope": 1,
    "Seasonal": 2,
})

A single-figure overview combining filtered, smoothed, forecast, and diagnostics:

dlm.fit(y).smooth()
dlm.forecast(steps=12)
dlm.plot(kind="panel")

All plot methods accept an optional ax parameter to draw on an existing Matplotlib axes, and a title parameter:

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
dlm.plot(kind="filtered", ax=ax, title="Nile River Flow")
plt.show()

See Plotting for the full function signatures.