pycaret.time_series.plot_model#

This function analyzes the performance of a trained model on holdout set. When used without any estimator, this function generates plots on the original data set. When used with an estimator, it will generate plots on the model residuals.

Example

>>> from pycaret.datasets import get_data
>>> airline = get_data('airline')
>>> from pycaret.time_series import *
>>> exp_name = setup(data = airline,  fh = 12)
>>> plot_model(plot="diff", data_kwargs={"order_list": [1, 2], "acf": True, "pacf": True})
>>> plot_model(plot="diff", data_kwargs={"lags_list": [[1], [1, 12]], "acf": True, "pacf": True})
>>> arima = create_model('arima')
>>> plot_model(plot = 'ts')
>>> plot_model(plot = 'decomp', data_kwargs = {'type' : 'multiplicative'})
>>> plot_model(plot = 'decomp', data_kwargs = {'seasonal_period': 24})
>>> plot_model(estimator = arima, plot = 'forecast', data_kwargs = {'fh' : 24})
>>> tuned_arima = tune_model(arima)
>>> plot_model([arima, tuned_arima], data_kwargs={"labels": ["Baseline", "Tuned"]})

estimator: sktime compatible object, default = None

Trained model object

plot: str, default = None

Default is ‘ts’ when estimator is None, When estimator is not None, default is changed to ‘forecast’. List of available plots (ID - Name):

‘ts’ - Time Series Plot
‘train_test_split’ - Train Test Split
‘cv’ - Cross Validation
‘acf’ - Auto Correlation (ACF)
‘pacf’ - Partial Auto Correlation (PACF)
‘decomp’ - Classical Decomposition
‘decomp_stl’ - STL Decomposition
‘diagnostics’ - Diagnostics Plot
‘diff’ - Difference Plot
‘periodogram’ - Frequency Components (Periodogram)
‘fft’ - Frequency Components (FFT)
‘ccf’ - Cross Correlation (CCF)
‘forecast’ - “Out-of-Sample” Forecast Plot
‘insample’ - “In-Sample” Forecast Plot
‘residuals’ - Residuals Plot

return_fig: bool, default = False

When set to True, it returns the figure used for plotting. When set to False (the default), it will print the plot, but not return it.

return_data: bool, default = False

When set to True, it returns the data for plotting. If both return_fig and return_data is set to True, order of return is figure then data.

verbose: bool, default = True

Unused for now

display_format: str, default = None

To display plots in Streamlit (https://www.streamlit.io/), set this to ‘streamlit’. Currently, not all plots are supported.

data_kwargs: dict, default = None

Dictionary of arguments passed to the data for plotting.

Available keys are:

nlags: The number of lags to use when plotting correlation plots, e.g.

ACF, PACF, CCF. If not provided, default internally calculated values are used.

seasonal_period: The seasonal period to use for decomposition plots.

If not provided, the default internally detected seasonal period is used.

type: The type of seasonal decomposition to perform. Options are:

[“additive”, “multiplicative”]

order_list: The differencing orders to use for difference plots. e.g.

[1, 2] will plot first and second order differences (corresponding to d = 1 and 2 in ARIMA models).

lags_list: An alternate and more explicit alternate to “order_list”

allowing users to specify the exact lags to plot. e.g. [1, [1, 12]] will plot first difference and a second plot with first difference (d = 1 in ARIMA) and seasonal 12th difference (D=1, s=12 in ARIMA models). Also note that “order_list” = [2] can be alternately specified as lags_list = [[1, 1]] i.e. successive differencing twice.

acf: True/False

When specified in difference plots and set to True, this will plot the ACF of the differenced data as well.

pacf: True/False

When specified in difference plots and set to True, this will plot the PACF of the differenced data as well.

periodogram: True/False

When specified in difference plots and set to True, this will plot the Periodogram of the differenced data as well.

fft: True/False

When specified in difference plots and set to True, this will plot the FFT of the differenced data as well.

labels: When estimator(s) are provided, the corresponding labels to

use for the plots. If not provided, the model class is used to derive the labels.

include: When data contains exogenous variables, then only specific

exogenous variables can be plotted using this key. e.g. include = [“col1”, “col2”]

exclude: When data contains exogenous variables, specific exogenous

variables can be excluded from the plots using this key. e.g. exclude = [“col1”, “col2”]

alpha: The quantile value to use for point prediction. If not provided,

then the value specified during setup is used.

coverage: The coverage value to use for prediction intervals. If not

provided, then the value specified during setup is used.

fh: The forecast horizon to use for forecasting. If not provided, then

the one used during model training is used.

X: When a model trained with exogenous variables has been finalized,

user can provide the future values of the exogenous variables to make future target time series predictions using this key.

plot_data_type: When plotting the data used for modeling, user may

wish to see plots with the original data set provided, the imputed dataset (if imputation is set) or the transformed dataset (which includes any imputation and transformation set by the user). This keyword can be used to specify which data type to use.

NOTE: (1) If no imputation is specified, then plotting the “imputed”

data type will produce the same results as the “original” data type.

If no transformations are specified, then plotting the “transformed” data type will produce the same results as the “imputed” data type.

Allowed values are (if not specified, defaults to the first one in the list):

“ts”: [“original”, “imputed”, “transformed”] “train_test_split”: [“original”, “imputed”, “transformed”] “cv”: [“original”] “acf”: [“transformed”, “imputed”, “original”] “pacf”: [“transformed”, “imputed”, “original”] “decomp”: [“transformed”, “imputed”, “original”] “decomp_stl”: [“transformed”, “imputed”, “original”] “diagnostics”: [“transformed”, “imputed”, “original”] “diff”: [“transformed”, “imputed”, “original”] “forecast”: [“original”, “imputed”] “insample”: [“original”, “imputed”] “residuals”: [“original”, “imputed”] “periodogram”: [“transformed”, “imputed”, “original”] “fft”: [“transformed”, “imputed”, “original”] “ccf”: [“transformed”, “imputed”, “original”]

Some plots (marked as True below) will also allow specifying multiple of data types at once.

“ts”: True “train_test_split”: True “cv”: False “acf”: True “pacf”: True “decomp”: True “decomp_stl”: True “diagnostics”: True “diff”: False “forecast”: False “insample”: False “residuals”: False “periodogram”: True “fft”: True “ccf”: False

fig_kwargs: dict, default = {} (empty dict)

The setting to be used for the plot. Overrides any global setting passed during setup. Pass these as key-value pairs. For available keys, refer to the setup documentation.

Time-series plots support more display_formats, as a result the fig-kwargs can also contain the resampler_kwargs key and its corresponding dict. These are additional keyword arguments that are fed to the display function. This is mainly used for configuring plotly-resampler visualizations (i.e., display_format “plotly-dash” or “plotly-widget”) which down sampler will be used; how many data points are shown in the front-end.

When the plotly-resampler figure is rendered via Dash (by setting the display_format to “plotly-dash”), one can also use the “show_dash” key within this dictionary to configure the show_dash args.

example:

fig_kwargs = {
    "width": None,
    "resampler_kwargs":  {
        "default_n_shown_samples": 1000,
        "show_dash": {"mode": "inline", "port": 9012}
    }
}

save: string or bool, default = False

When set to True, Plot is saved as a ‘png’ file in current working directory. When a path destination is given, Plot is saved as a ‘png’ file the given path to the directory of choice.

Returns:: Path to saved file and list containing figure and data, if any.