Tuning Prophetverse with sktime

This guide explains how to optimize Prophetverse model hyperparameters using sktime's tuning classes (e.g., GridSearchCV).

Overview

Prophetverse is compatible with sktime’s tuning framework. You can define a parameter grid for components (such as trend and seasonality) and then use cross-validation tools (e.g., GridSearchCV) to search for the best parameters.

Example: Using GridSearchCV with Prophetverse

1. Import necessary modules and load your dataset.
2. Define the hyperparameter grid for components (e.g., changepoint_interval and changepoint_prior_scale in the trend).
3. Create a Prophetverse instance with initial settings.
4. Wrap the model with sktime’s GridSearchCV and run the tuning process.

import pandas as pd
from sktime.forecasting.model_selection import ForecastingGridSearchCV
from prophetverse.sktime import Prophetverse
from prophetverse.effects.trend import PiecewiseLinearTrend
from prophetverse.effects.fourier import LinearFourierSeasonality
from prophetverse.engine import MAPInferenceEngine
from prophetverse.utils import no_input_columns

# Load example dataset (replace with your own data as needed)
from prophetverse.datasets.loaders import load_peyton_manning

y = load_peyton_manning()

# Define the hyperparameter grid for the trend component.
param_grid = {
    "trend__changepoint_interval": [300, 700],
    "trend__changepoint_prior_scale": [0.0001, 0.00001],
    "seasonality__prior_scale": [0.1],
}

# Create the initial Prophetverse model.
model = Prophetverse(
    trend=PiecewiseLinearTrend(
        changepoint_interval=500,
        changepoint_prior_scale=0.00001,
        changepoint_range=-250,
    ),
    exogenous_effects=[
        (
            "seasonality",
            LinearFourierSeasonality(
                freq="D",
                sp_list=[7, 365.25],
                fourier_terms_list=[3, 10],
                prior_scale=0.1,
                effect_mode="multiplicative",
            ),
            no_input_columns,
        ),
    ],
    inference_engine=MAPInferenceEngine(),
)

# Set up cv strategy
from sktime.split import ExpandingWindowSplitter

cv = ExpandingWindowSplitter(fh=[1, 2, 3], step_length=1000, initial_window=1000)

# Set up GridSearchCV with 3-fold cross-validation.
grid_search = ForecastingGridSearchCV(model, param_grid=param_grid, cv=cv)

# Run the grid search.
grid_search.fit(y=y, X=None)

# Display the best parameters found.
print("Best parameters:", grid_search.best_params_)

Output: [1]

best_model = grid_search.best_forecaster_
best_model

Output: [2]

Prophetverse(exogenous_effects=[('seasonality',
                                 LinearFourierSeasonality(effect_mode='multiplicative',
                                                          fourier_terms_list=[3,
                                                                              10],
                                                          freq='D',
                                                          prior_scale=0.1,
                                                          sp_list=[7, 365.25]),
                                 '^$')],
             inference_engine=MAPInferenceEngine(),
             trend=PiecewiseLinearTrend(changepoint_interval=700,
                                        changepoint_prior_scale=1e-05,
                                        changepoint_range=-250))

Please rerun this cell to show the HTML repr or trust the notebook.