# Modelling Events¶

Often in forecasting problems, we need to consider recurring special events. These are supported by neural_prophet. These events can be added both in additive format and multiplicative format.

To provide the information of events into the model, the user has to create a dataframe which has the column ds corresponding to the event dates and the column event which contains the names of the events on specified dates. In the following example we have created the dataframe named history_events_df which contains these events information.

playoffs_history = pd.DataFrame({
'event': 'playoff',
'ds': pd.to_datetime(['2008-01-13', '2009-01-03', '2010-01-16',
'2010-01-24', '2010-02-07', '2011-01-08',
'2013-01-12', '2014-01-12', '2014-01-19',
'2014-02-02', '2015-01-11', '2016-01-17']),
})

superbowls_history = pd.DataFrame({
'event': 'superbowl',
'ds': pd.to_datetime(['2010-02-07', '2014-02-02']),
})
history_events_df = pd.concat((playoffs_history, superbowls_history))


The first few rows of the history_events_df dataframe looks like below.

event

ds

0

playoff

2008-01-13 00:00:00

1

playoff

2009-01-03 00:00:00

2

playoff

2010-01-16 00:00:00

3

playoff

2010-01-24 00:00:00

4

playoff

2010-02-07 00:00:00

5

playoff

2011-01-08 00:00:00

For forecasting, we also need to provide the future dates of these events used to train the model. You can either include these in the same events dataframe that was created before for fitting the model, or in a new dataframe as follows.
playoffs_future = pd.DataFrame({
'event': 'playoff',
'ds': pd.to_datetime(['2016-01-21', '2016-02-07'])
})

superbowl_future = pd.DataFrame({
'event': 'superbowl',
'ds': pd.to_datetime(['2016-01-23', '2016-02-07'])
})

future_events_df = pd.concat((playoffs_future, superbowl_future))


Once the events dataframes have been created, the NeuralProphet object should be created and the events configs should be added. This is done using the add_events function of the NeuralProphet class.

m = NeuralProphet(
n_forecasts=10,
yearly_seasonality=False,
weekly_seasonality=False,
daily_seasonality=False,
)


After that, we need to convert the events data in the previously created dataframes into the binary input data expected by the model. This can be done by calling the create_df_with_events function by passing original time series dataframe along with the created history_events_df.

history_df = m.create_df_with_events(df, history_events_df)


This returns a dataframe in the following format.

ds

y

superbowl

playoff

0

2007-12-10 00:00:00

9.59076

0

0

1

2007-12-11 00:00:00

8.51959

0

0

2

2007-12-12 00:00:00

8.18368

0

0

3

2007-12-13 00:00:00

8.07247

0

0

4

2007-12-14 00:00:00

7.89357

0

0

After that, we can simply fit the model as below by providing to the fit function, the created history_df.

metrics = m.fit(history_df, freq="D")
forecast = m.predict(df=history_df)


<The produced forecasts look like below. The 10 step-ahead forecasts are available in the yhat1 column. The components from the individual events are available in the event_playoff and event_superbowl columns and their agrgegated effect is shown on the events_additive column>

Once the forecasting is done, the different components can be plotted like below. All events are plotted as one component, the Additive Events

{: style=”height:400px”}

The model coefficients would look like below.

{: style=”height:550px”}

## Multiplicative Events¶

The default mode for events in neural_prophet is additive. However, events can also be modelled in a multiplicative format. For this, when adding the events configs to the NeuralProphet object, we need to set the mode to multiplicative as below.

m = m.add_events(["superbowl", "playoff"], mode="multiplicative")


All the other steps are the same as for the additive mode. Now, when you plot the components, the event components will appear as percentages.

{: style=”height:400px”}

## Event Windows¶

You can also provide windows for events. This way, you can consider the days around a particular event also as special events by providing the arguments lower_window and upper_window as appropriate to the add_events function of the NeuralProphet object. By default, the values for these windows are 0, which means windows are not considered.

m = m.add_events(["superbowl", "playoff"], lower_window=-1, upper_window=1)


According to this specification, for both superbowl and playoff events, three special events will be modelled, the event date, the previous day and the next day. These will be visible in the component plots as below.

{: style=”height:550px”}

In the parameters plot too, there will now be superbowl_+1 and superbowl_-1 which correspond to the coefficients of the day following and previous to the superbowl event. The playoff event also has the same new coefficients.

{: style=”height:550px”}

If you want to define different windows for the individual events, this can also be done as follows.

m = m.add_events("superbowl", lower_window=-1, upper_window=1)


In the above example, for the playoff event, the specified event date, as well as the two following dates are considered as three different special events.

## Country Specific Holidays¶

Apart from the user specified events, neural_prophet also supports standard country specific holidays. If you want to add the holidays for a particular country, you simply have to call the add_country_holidays function on the NeuralProphet object and specify the country. Similar to the user specified events, country specific holidays can either be additive or multiplicative and include windows. However, unlike for user specified events, the windows will be the same for all the country specific events.

m = m.add_country_holidays("US", mode="additive", lower_window=-1, upper_window=1)


This example will add all the US holidays into the model in additive format. The coefficients of the individual events will now look like below.

{: style=”height:600px”}

## Regularization for Events¶

Events can also support regularization of the coefficients. You can specify the regularization when adding the event configs into the NeuralProphet object like below.

m = m.add_events(["superbowl", "playoff"], regularization=0.05)


The regularization for the individual events can also be different from each other like below.

m = m.add_events("superbowl", regularization=0.05)

m = m.add_country_holidays("US", mode="additive", regularization=0.05)