Regressors

Use model_components.regressors to specify external regressors. Regressors can be numeric or categorical.

You need to provide historical values of the regressor for training, and future values for prediction. See Examine Input Data for the input data format.

For example, to use weather as a regressor to forecast the amount of vehicle traffic, you can train a model using historical traffic and weather conditions. Then predict future traffic based on forecasted weather conditions.

Silverkite

Examples:

# For input data with 3 regressors.
# Input data columns: ["time", "value", "gdp", "weather", "population"]
regressors=dict(
    regressor_cols=["gdp", "weather", "population"]
)

# No regressors (default)
regressors=None

# Grid search is possible
regressors=dict(
    regressor_cols=[
        ["gdp", "weather", "population"],
        ["gdp", "weather"],
        None
    ]
)

Prophet

Options:

add_regressor_dict: `dict` or None or list of such values for grid search
    Dictionary of extra regressors to be modeled. Predictions will be influenced by these regressors.
    None by default.

Follow the same guidance as Silverkite for input data format.

Examples:

# For input data with 3 regressors.
# Input data columns: ["time", "value", "gdp", "weather", "population"]
regressors=dict(
    add_regressor_dict={  # add as many regressors as you'd like, in the following format
        "gdp": {
            "prior_scale": 10.0,  # default is 10.0, decreasing the prior scale will add additional regularization
            "mode": 'additive'  # this regressor's effect on predictions
        },
        "weather": {
            "prior_scale": 20.0,
            "mode": 'multiplicative'
        },
        "population": {
            "prior_scale": 15.0,
            "mode": 'multiplicative'
        }
    }
)

# No regressors (default)
regressors=None

# Grid search is possible
regressors=dict(
    add_regressor_dict=[{  # it is possible to enable different modes for given regressors
        "gdp": {
            "prior_scale": 10.0,
            "mode": 'additive'
        },
        "weather": {
            "prior_scale": 20.0,
            "mode": 'multiplicative'
        },
        "population": {
            "prior_scale": 15.0,
            "mode": 'multiplicative'
        }
    },
    {
        "gdp": {
            "prior_scale": 15.0,
            "mode": 'additive'
        },
        "weather": {
            "prior_scale": 10.0,
            "mode": 'additive'
        },
        "population": {
            "prior_scale": 25.0,
            "mode": 'additive'
        }
    }
)

Note

prior_scale and mode work in similar way as for custom seasonality (Seasonality). Fit customization can be done for each regressor.