Auto Configuration Tools

The Silverkite model has many hyperparameters to tune. Besides domain knowledge, we also have tools that can help find good choices for certain hyperparameters. In this tutorial, we will present

• seasonality inferrer

• holiday inferrer

Note

If you use the model templates, you can specify the “auto” option for certain model components (growth, seasonality and holiday), and the auto configuration tool will be activated automatically. See auto seasonality, auto growth and auto holidays for the way to activate them. This doc explains how the “auto” options work behind the code. You can replay the “auto” options with the Seasonality Inferrer and Holiday Inferrer below. Please remember that if you are doing train-test split, running the inferrers on training data only is closer to the reality.

Seasonality Inferrer

The Silverkite model uses Fourier series to model seasonalities. It’s sometimes difficult to decide what orders we should use for each Fourier series. Larger orders tend to fit more closely to the curves, while having the risk of overfitting. Small orders tend to underfit the curve and may not learn the exact seasonality patterns.

SeasonalityInferrer is a tool that can help you decide what order to use for a seasonality’s Fourier series. Note that there are many ways to decide the orders, and you don’t have to strictly stick to the results from Seasonality Inferrer.

How it works

The seasonality inferrer utilizes criteria including AIC and BIC to find the most appropriate Fourier series orders. For a specific seasonality, e.g. yearly seasonality, the steps are as follows:

• Trend removal: seasonality inferrer provides 4 options for trend removal. They are listed in TrendAdjustMethodEnum. Specifically:

  • "seasonal_average": given an indicator of seasonal period, the method subtracts the average within each seasonal period from the original time series. For example, given the column year, the average is calculated on each different year.

  • "overall_average": subtracts the overall average from the original time series.

  • "spline_fit": fits a polynomial up to a given degree and subtract from the original time series.

  • "none": does not adjust the trend.

  Typically “seasonal_average” is a good choice with appropriate columns. For example, we can use year_quarter for quarterly seasonality, year_month for monthly seasonality, year_woy_iso for weekly seasonality and year_woy_dow_iso for daily seasonality.

• Optional aggregation: sometimes we want to get rid of shorter fluctuations before fitting a longer seasonality period. We can do an optional aggregation beforehand. For example, when we model yearly seasonality, we can do a "7D" aggregation to eliminate weekly effects to make the result more stable.

• With a pre-specified maximum order n, we fit the de-trended (and aggregated) time series with Fourier series from 1 to n, and calculate the AIC/BIC for those fits. The most appropriate order is then decided by choosing the order with best AIC or BIC. The method also allows to slightly sacrifice the criterion and reduce the order for less risk of overfitting using the tolerance parameter.

• Finally, an optional offset can be applied to any inferred orders to allow manual adjustments. For example, if one would like to use less yearly seasonality order, they may specify offset for yearly seasonality to be -2, and the final order will subtract 2 from the inferred result. This is useful when users tend to use more or less orders to model seasonality, and want a knob on top of the inferring results.

Example

Now we look at an example with the Peyton-Manning Wiki page view data.

 import pandas as pd
 import plotly
 from greykite.common.data_loader import DataLoader
 from greykite.algo.common.seasonality_inferrer import SeasonalityInferConfig
 from greykite.algo.common.seasonality_inferrer import SeasonalityInferrer
 from greykite.algo.common.seasonality_inferrer import TrendAdjustMethodEnum
 from greykite.common import constants as cst

The SeasonalityInferrer class uses SeasonalityInferConfig to specify configuration for a single seasonality component, and it takes a list of such configurations to infer multiple seasonality components together. Now we specify seasonality inferring configs for yearly to weekly seasonalities. In each of these configs, specify the parameters that are distinct for each component. If there are parameters that are the same across all configs, you can specify them in the function directly.

 yearly_config = SeasonalityInferConfig(
     seas_name="yearly",                     # name for seasonality
     col_name="toy",                         # column to generate Fourier series, fixed for yearly
     period=1.0,                             # seasonal period, fixed for yearly
     max_order=30,                           # max number of orders to model
     adjust_trend_param=dict(
         trend_average_col="year"
     ),                                      # column to adjust trend for method "seasonal_average"
     aggregation_period="W",                 # aggregation period,
     offset=0                                # add this to the inferred result, default 0
 )
 quarterly_config = SeasonalityInferConfig(
     seas_name="quarterly",                  # name for seasonality
     col_name="toq",                         # column to generate Fourier series, fixed for quarterly
     period=1.0,                             # seasonal period, fixed for quarterly
     max_order=20,                           # max number of orders to model
     adjust_trend_param=dict(
         trend_average_col="year_quarter"
     ),                                      # column to adjust trend for method "seasonal_average"
     aggregation_period="2D",                # aggregation period
 )
 monthly_config = SeasonalityInferConfig(
     seas_name="monthly",                    # name for seasonality
     col_name="tom",                         # column to generate Fourier series, fixed for monthly
     period=1.0,                             # seasonal period, fixed for monthly
     max_order=20,                           # max number of orders to model
     adjust_trend_param=dict(
         trend_average_col="year_month"
     ),                                      # column to adjust trend for method "seasonal_average"
     aggregation_period="D"                  # aggregation period
 )
 weekly_config = SeasonalityInferConfig(
     seas_name="weekly",                     # name for seasonality
     col_name="tow",                         # column to generate Fourier series, fixed for weekly
     period=7.0,                             # seasonal period, fixed for weekly
     max_order=10,                           # max number of orders to model
     adjust_trend_param=dict(
         trend_average_col="year_woy_iso"
     ),                                      # column to adjust trend for method "seasonal_average"
     aggregation_period="D",
     tolerance=0.005,                        # allows 0.5% higher criterion for lower orders
 )

Next, we put everything together to infer seasonality effects.

 df = DataLoader().load_peyton_manning()
 df[cst.TIME_COL] = pd.to_datetime((df[cst.TIME_COL]))

 model = SeasonalityInferrer()
 result = model.infer_fourier_series_order(
     df=df,
     time_col=cst.TIME_COL,
     value_col=cst.VALUE_COL,
     configs=[
         yearly_config,
         quarterly_config,
         monthly_config,
         weekly_config
     ],
     adjust_trend_method=TrendAdjustMethodEnum.seasonal_average.name,
     fit_algorithm="linear",
     plotting=True,
     criterion="bic",
 )

The method runs quickly and we can simply extract the inferred results from the output.

 result["best_orders"]

Out:

{'yearly': 6, 'quarterly': 2, 'monthly': 1, 'weekly': 2}

We can also plot the results to see how different orders vary the criterion. Similar to other trade-off plots, the plot first goes down and then goes up, reaching the best at some appropriate value in the middle.

 # The [0] extracts the first seasonality component from the results.
 plotly.io.show(result["result"][0]["fig"])

Holiday Inferrer

The Silverkite model supports modeling holidays and their neighboring days as indicators. Significant days are modeled separately, while similar days can be grouped together as one indicator, assuming their effects are the same.

It’s sometimes difficult to decide which holidays to include, to model separately or to model together. HolidayInferrer is a tool that can help you decide which holidays to model and how to model them. It can also automatically generate the holiday configuration parameters. Note that there are many ways to decide the holiday configurations, and you don’t have to strictly stick to the results from Holiday Inferrer.

How it works

The holiday inferrer estimates individual holiday or their neighboring days’ effects by comparing the observations on these days with some baseline prior to or after the holiday period. Then it ranks the effects by their magnitude. Depending on some thresholds, it decides whether to model a day independently, together with others or do not model it.

In detail, the first step is to unify the data frequency. For data whose frequency is greater than daily, holiday effect is automatically turned off. For data whose frequency is less than daily, it is aggregated into daily data, since holidays are daily events. From now on, we have daily data for the next step.

Given a list of countries, the tool automatically pulls candidate holidays from the database. With a pre_search_days and a post_search_days parameters, those holidays’ neighboring days are included in the candidate pool as well.

For every candidate holiday or neighboring day, the baseline is the average of a configurable offsets. For example, for data that exhibits strong weekly seasonality, the offsets can be (-7, 7), where the baseline will be the average of the last same day of week’s observation and the next same day of week’s observation. For example, if the holiday is New Year on 1/1 while 12/25 (7 days ago) is Christmas, it will look at the value on 12/18 instead of 12/25 as baseline.

The day’s effect is the average of the signed difference between the true observation and the baseline across all occurrences in the time series. The effects are ranked from the highest to the lowest by their absolute effects.

To decide how each holiday is modeled, we rely on two parameters: independent_holiday_thres and together_holiday_thres. These parameters are between 0 and 1. Starting from the largest effect, we calculate the cumulative sum of effect of all candidates. Once the cumulative effect reaches independend_holiday_thres of the total effects, these days will be modeled independently (i.e, each day has an individual coefficient). We keep accumulating effects until the sum reaches together_holiday_thres, the days in the between are grouped into “positive_group” and “negative_group”, with each group modeled together.

Example

Now we look at an example with the Peyton-Manning Wiki page view data.

 import pandas as pd
 import plotly
 from greykite.algo.common.holiday_inferrer import HolidayInferrer
 from greykite.common.data_loader import DataLoader
 from greykite.common import constants as cst

 df = DataLoader().load_peyton_manning()
 df[cst.TIME_COL] = pd.to_datetime(df[cst.TIME_COL])

Let’s say we want to infer the holidays in the United States, with consideration on +/- 2 days of each holiday as potential candidates too.

 hi = HolidayInferrer()
 result = hi.infer_holidays(
     df=df,
     countries=["US"],                   # Search holidays in United States
     plot=True,                          # Output a plot
     pre_search_days=2,                  # Considers 2 days before each holiday
     post_search_days=2,                 # Considers 2 days after each holiday
     independent_holiday_thres=0.9,      # The first 90% of effects are modeled separately
     together_holiday_thres=0.99,        # The 90% to 99% of effects are modeled together
     baseline_offsets=[-7, 7]            # The baseline is the average of -7/+7 observations
 )

We can plot the inferred holiday results.

 plotly.io.show(result["fig"])

The class also has a method to generate the holiday configuration based on the inferred results, that is consumable directly by the Silverkite model.

 hi.generate_daily_event_dict()

Out:

{'US_Labor Day':          date    event_name
0  2016-09-05  US_Labor Day
1  2017-09-04  US_Labor Day
2  2007-09-03  US_Labor Day
3  2008-09-01  US_Labor Day
4  2009-09-07  US_Labor Day
5  2010-09-06  US_Labor Day
6  2011-09-05  US_Labor Day
7  2012-09-03  US_Labor Day
8  2013-09-02  US_Labor Day
9  2014-09-01  US_Labor Day
10 2015-09-07  US_Labor Day, 'US_Labor Day_minus_1':          date            event_name
0  2016-09-04  US_Labor Day_minus_1
1  2017-09-03  US_Labor Day_minus_1
2  2007-09-02  US_Labor Day_minus_1
3  2008-08-31  US_Labor Day_minus_1
4  2009-09-06  US_Labor Day_minus_1
5  2010-09-05  US_Labor Day_minus_1
6  2011-09-04  US_Labor Day_minus_1
7  2012-09-02  US_Labor Day_minus_1
8  2013-09-01  US_Labor Day_minus_1
9  2014-08-31  US_Labor Day_minus_1
10 2015-09-06  US_Labor Day_minus_1, 'US_Christmas Day':          date        event_name
0  2016-12-26  US_Christmas Day
1  2017-12-25  US_Christmas Day
2  2007-12-25  US_Christmas Day
3  2008-12-25  US_Christmas Day
4  2009-12-25  US_Christmas Day
5  2010-12-24  US_Christmas Day
6  2011-12-26  US_Christmas Day
7  2012-12-25  US_Christmas Day
8  2013-12-25  US_Christmas Day
9  2014-12-25  US_Christmas Day
10 2015-12-25  US_Christmas Day, 'US_Martin Luther King Jr. Day':          date                     event_name
0  2016-01-18  US_Martin Luther King Jr. Day
1  2017-01-16  US_Martin Luther King Jr. Day
2  2007-01-15  US_Martin Luther King Jr. Day
3  2008-01-21  US_Martin Luther King Jr. Day
4  2009-01-19  US_Martin Luther King Jr. Day
5  2010-01-18  US_Martin Luther King Jr. Day
6  2011-01-17  US_Martin Luther King Jr. Day
7  2012-01-16  US_Martin Luther King Jr. Day
8  2013-01-21  US_Martin Luther King Jr. Day
9  2014-01-20  US_Martin Luther King Jr. Day
10 2015-01-19  US_Martin Luther King Jr. Day, 'US_Washingtons Birthday_minus_1':          date                       event_name
0  2016-02-14  US_Washingtons Birthday_minus_1
1  2017-02-19  US_Washingtons Birthday_minus_1
2  2007-02-18  US_Washingtons Birthday_minus_1
3  2008-02-17  US_Washingtons Birthday_minus_1
4  2009-02-15  US_Washingtons Birthday_minus_1
5  2010-02-14  US_Washingtons Birthday_minus_1
6  2011-02-20  US_Washingtons Birthday_minus_1
7  2012-02-19  US_Washingtons Birthday_minus_1
8  2013-02-17  US_Washingtons Birthday_minus_1
9  2014-02-16  US_Washingtons Birthday_minus_1
10 2015-02-15  US_Washingtons Birthday_minus_1, 'US_Thanksgiving_minus_2':          date               event_name
0  2016-11-22  US_Thanksgiving_minus_2
1  2017-11-21  US_Thanksgiving_minus_2
2  2007-11-20  US_Thanksgiving_minus_2
3  2008-11-25  US_Thanksgiving_minus_2
4  2009-11-24  US_Thanksgiving_minus_2
5  2010-11-23  US_Thanksgiving_minus_2
6  2011-11-22  US_Thanksgiving_minus_2
7  2012-11-20  US_Thanksgiving_minus_2
8  2013-11-26  US_Thanksgiving_minus_2
9  2014-11-25  US_Thanksgiving_minus_2
10 2015-11-24  US_Thanksgiving_minus_2, 'US_Washingtons Birthday_plus_1':          date                      event_name
0  2016-02-16  US_Washingtons Birthday_plus_1
1  2017-02-21  US_Washingtons Birthday_plus_1
2  2007-02-20  US_Washingtons Birthday_plus_1
3  2008-02-19  US_Washingtons Birthday_plus_1
4  2009-02-17  US_Washingtons Birthday_plus_1
5  2010-02-16  US_Washingtons Birthday_plus_1
6  2011-02-22  US_Washingtons Birthday_plus_1
7  2012-02-21  US_Washingtons Birthday_plus_1
8  2013-02-19  US_Washingtons Birthday_plus_1
9  2014-02-18  US_Washingtons Birthday_plus_1
10 2015-02-17  US_Washingtons Birthday_plus_1, 'US_New Years Day_plus_1':          date               event_name
0  2016-01-02  US_New Years Day_plus_1
1  2017-01-03  US_New Years Day_plus_1
2  2007-01-02  US_New Years Day_plus_1
3  2008-01-02  US_New Years Day_plus_1
4  2009-01-02  US_New Years Day_plus_1
5  2010-01-02  US_New Years Day_plus_1
6  2011-01-01  US_New Years Day_plus_1
7  2012-01-03  US_New Years Day_plus_1
8  2013-01-02  US_New Years Day_plus_1
9  2014-01-02  US_New Years Day_plus_1
10 2015-01-02  US_New Years Day_plus_1, 'US_Veterans Day_minus_2':          date               event_name
0  2016-11-09  US_Veterans Day_minus_2
1  2017-11-08  US_Veterans Day_minus_2
2  2007-11-10  US_Veterans Day_minus_2
3  2008-11-09  US_Veterans Day_minus_2
4  2009-11-09  US_Veterans Day_minus_2
5  2010-11-09  US_Veterans Day_minus_2
6  2011-11-09  US_Veterans Day_minus_2
7  2012-11-10  US_Veterans Day_minus_2
8  2013-11-09  US_Veterans Day_minus_2
9  2014-11-09  US_Veterans Day_minus_2
10 2015-11-09  US_Veterans Day_minus_2, 'US_Washingtons Birthday_plus_2':          date                      event_name
0  2016-02-17  US_Washingtons Birthday_plus_2
1  2017-02-22  US_Washingtons Birthday_plus_2
2  2007-02-21  US_Washingtons Birthday_plus_2
3  2008-02-20  US_Washingtons Birthday_plus_2
4  2009-02-18  US_Washingtons Birthday_plus_2
5  2010-02-17  US_Washingtons Birthday_plus_2
6  2011-02-23  US_Washingtons Birthday_plus_2
7  2012-02-22  US_Washingtons Birthday_plus_2
8  2013-02-20  US_Washingtons Birthday_plus_2
9  2014-02-19  US_Washingtons Birthday_plus_2
10 2015-02-18  US_Washingtons Birthday_plus_2, 'US_Christmas Day_plus_1':          date               event_name
0  2016-12-27  US_Christmas Day_plus_1
1  2017-12-26  US_Christmas Day_plus_1
2  2007-12-26  US_Christmas Day_plus_1
3  2008-12-26  US_Christmas Day_plus_1
4  2009-12-26  US_Christmas Day_plus_1
5  2010-12-25  US_Christmas Day_plus_1
6  2011-12-27  US_Christmas Day_plus_1
7  2012-12-26  US_Christmas Day_plus_1
8  2013-12-26  US_Christmas Day_plus_1
9  2014-12-26  US_Christmas Day_plus_1
10 2015-12-26  US_Christmas Day_plus_1, 'US_Memorial Day':          date       event_name
0  2016-05-30  US_Memorial Day
1  2017-05-29  US_Memorial Day
2  2007-05-28  US_Memorial Day
3  2008-05-26  US_Memorial Day
4  2009-05-25  US_Memorial Day
5  2010-05-31  US_Memorial Day
6  2011-05-30  US_Memorial Day
7  2012-05-28  US_Memorial Day
8  2013-05-27  US_Memorial Day
9  2014-05-26  US_Memorial Day
10 2015-05-25  US_Memorial Day, 'US_Veterans Day':          date       event_name
0  2016-11-11  US_Veterans Day
1  2017-11-10  US_Veterans Day
2  2007-11-12  US_Veterans Day
3  2008-11-11  US_Veterans Day
4  2009-11-11  US_Veterans Day
5  2010-11-11  US_Veterans Day
6  2011-11-11  US_Veterans Day
7  2012-11-12  US_Veterans Day
8  2013-11-11  US_Veterans Day
9  2014-11-11  US_Veterans Day
10 2015-11-11  US_Veterans Day, 'US_Washingtons Birthday_minus_2':          date                       event_name
0  2016-02-13  US_Washingtons Birthday_minus_2
1  2017-02-18  US_Washingtons Birthday_minus_2
2  2007-02-17  US_Washingtons Birthday_minus_2
3  2008-02-16  US_Washingtons Birthday_minus_2
4  2009-02-14  US_Washingtons Birthday_minus_2
5  2010-02-13  US_Washingtons Birthday_minus_2
6  2011-02-19  US_Washingtons Birthday_minus_2
7  2012-02-18  US_Washingtons Birthday_minus_2
8  2013-02-16  US_Washingtons Birthday_minus_2
9  2014-02-15  US_Washingtons Birthday_minus_2
10 2015-02-14  US_Washingtons Birthday_minus_2, 'US_Thanksgiving_minus_1':          date               event_name
0  2016-11-23  US_Thanksgiving_minus_1
1  2017-11-22  US_Thanksgiving_minus_1
2  2007-11-21  US_Thanksgiving_minus_1
3  2008-11-26  US_Thanksgiving_minus_1
4  2009-11-25  US_Thanksgiving_minus_1
5  2010-11-24  US_Thanksgiving_minus_1
6  2011-11-23  US_Thanksgiving_minus_1
7  2012-11-21  US_Thanksgiving_minus_1
8  2013-11-27  US_Thanksgiving_minus_1
9  2014-11-26  US_Thanksgiving_minus_1
10 2015-11-25  US_Thanksgiving_minus_1, 'US_Labor Day_minus_2':          date            event_name
0  2016-09-03  US_Labor Day_minus_2
1  2017-09-02  US_Labor Day_minus_2
2  2007-09-01  US_Labor Day_minus_2
3  2008-08-30  US_Labor Day_minus_2
4  2009-09-05  US_Labor Day_minus_2
5  2010-09-04  US_Labor Day_minus_2
6  2011-09-03  US_Labor Day_minus_2
7  2012-09-01  US_Labor Day_minus_2
8  2013-08-31  US_Labor Day_minus_2
9  2014-08-30  US_Labor Day_minus_2
10 2015-09-05  US_Labor Day_minus_2, 'US_Columbus Day':          date       event_name
0  2016-10-10  US_Columbus Day
1  2017-10-09  US_Columbus Day
2  2007-10-08  US_Columbus Day
3  2008-10-13  US_Columbus Day
4  2009-10-12  US_Columbus Day
5  2010-10-11  US_Columbus Day
6  2011-10-10  US_Columbus Day
7  2012-10-08  US_Columbus Day
8  2013-10-14  US_Columbus Day
9  2014-10-13  US_Columbus Day
10 2015-10-12  US_Columbus Day, 'US_Memorial Day_plus_1':          date              event_name
0  2016-05-31  US_Memorial Day_plus_1
1  2017-05-30  US_Memorial Day_plus_1
2  2007-05-29  US_Memorial Day_plus_1
3  2008-05-27  US_Memorial Day_plus_1
4  2009-05-26  US_Memorial Day_plus_1
5  2010-06-01  US_Memorial Day_plus_1
6  2011-05-31  US_Memorial Day_plus_1
7  2012-05-29  US_Memorial Day_plus_1
8  2013-05-28  US_Memorial Day_plus_1
9  2014-05-27  US_Memorial Day_plus_1
10 2015-05-26  US_Memorial Day_plus_1, 'US_Labor Day_plus_1':          date           event_name
0  2016-09-06  US_Labor Day_plus_1
1  2017-09-05  US_Labor Day_plus_1
2  2007-09-04  US_Labor Day_plus_1
3  2008-09-02  US_Labor Day_plus_1
4  2009-09-08  US_Labor Day_plus_1
5  2010-09-07  US_Labor Day_plus_1
6  2011-09-06  US_Labor Day_plus_1
7  2012-09-04  US_Labor Day_plus_1
8  2013-09-03  US_Labor Day_plus_1
9  2014-09-02  US_Labor Day_plus_1
10 2015-09-08  US_Labor Day_plus_1, 'US_Martin Luther King Jr. Day_minus_1':          date                             event_name
0  2016-01-17  US_Martin Luther King Jr. Day_minus_1
1  2017-01-15  US_Martin Luther King Jr. Day_minus_1
2  2007-01-14  US_Martin Luther King Jr. Day_minus_1
3  2008-01-20  US_Martin Luther King Jr. Day_minus_1
4  2009-01-18  US_Martin Luther King Jr. Day_minus_1
5  2010-01-17  US_Martin Luther King Jr. Day_minus_1
6  2011-01-16  US_Martin Luther King Jr. Day_minus_1
7  2012-01-15  US_Martin Luther King Jr. Day_minus_1
8  2013-01-20  US_Martin Luther King Jr. Day_minus_1
9  2014-01-19  US_Martin Luther King Jr. Day_minus_1
10 2015-01-18  US_Martin Luther King Jr. Day_minus_1, 'US_Independence Day_minus_2':          date                   event_name
0  2016-07-02  US_Independence Day_minus_2
1  2017-07-02  US_Independence Day_minus_2
2  2007-07-02  US_Independence Day_minus_2
3  2008-07-02  US_Independence Day_minus_2
4  2009-07-01  US_Independence Day_minus_2
5  2010-07-03  US_Independence Day_minus_2
6  2011-07-02  US_Independence Day_minus_2
7  2012-07-02  US_Independence Day_minus_2
8  2013-07-02  US_Independence Day_minus_2
9  2014-07-02  US_Independence Day_minus_2
10 2015-07-01  US_Independence Day_minus_2, 'US_Christmas Day_minus_1':          date                event_name
0  2016-12-25  US_Christmas Day_minus_1
1  2017-12-24  US_Christmas Day_minus_1
2  2007-12-24  US_Christmas Day_minus_1
3  2008-12-24  US_Christmas Day_minus_1
4  2009-12-24  US_Christmas Day_minus_1
5  2010-12-23  US_Christmas Day_minus_1
6  2011-12-25  US_Christmas Day_minus_1
7  2012-12-24  US_Christmas Day_minus_1
8  2013-12-24  US_Christmas Day_minus_1
9  2014-12-24  US_Christmas Day_minus_1
10 2015-12-24  US_Christmas Day_minus_1, 'US_Independence Day_minus_1':          date                   event_name
0  2016-07-03  US_Independence Day_minus_1
1  2017-07-03  US_Independence Day_minus_1
2  2007-07-03  US_Independence Day_minus_1
3  2008-07-03  US_Independence Day_minus_1
4  2009-07-02  US_Independence Day_minus_1
5  2010-07-04  US_Independence Day_minus_1
6  2011-07-03  US_Independence Day_minus_1
7  2012-07-03  US_Independence Day_minus_1
8  2013-07-03  US_Independence Day_minus_1
9  2014-07-03  US_Independence Day_minus_1
10 2015-07-02  US_Independence Day_minus_1, 'US_Veterans Day_minus_1':          date               event_name
0  2016-11-10  US_Veterans Day_minus_1
1  2017-11-09  US_Veterans Day_minus_1
2  2007-11-11  US_Veterans Day_minus_1
3  2008-11-10  US_Veterans Day_minus_1
4  2009-11-10  US_Veterans Day_minus_1
5  2010-11-10  US_Veterans Day_minus_1
6  2011-11-10  US_Veterans Day_minus_1
7  2012-11-11  US_Veterans Day_minus_1
8  2013-11-10  US_Veterans Day_minus_1
9  2014-11-10  US_Veterans Day_minus_1
10 2015-11-10  US_Veterans Day_minus_1, 'US_New Years Day':          date        event_name
0  2016-01-01  US_New Years Day
1  2017-01-02  US_New Years Day
2  2007-01-01  US_New Years Day
3  2008-01-01  US_New Years Day
4  2009-01-01  US_New Years Day
5  2010-01-01  US_New Years Day
6  2010-12-31  US_New Years Day
7  2012-01-02  US_New Years Day
8  2013-01-01  US_New Years Day
9  2014-01-01  US_New Years Day
10 2015-01-01  US_New Years Day, 'US_Martin Luther King Jr. Day_plus_1':          date                            event_name
0  2016-01-19  US_Martin Luther King Jr. Day_plus_1
1  2017-01-17  US_Martin Luther King Jr. Day_plus_1
2  2007-01-16  US_Martin Luther King Jr. Day_plus_1
3  2008-01-22  US_Martin Luther King Jr. Day_plus_1
4  2009-01-20  US_Martin Luther King Jr. Day_plus_1
5  2010-01-19  US_Martin Luther King Jr. Day_plus_1
6  2011-01-18  US_Martin Luther King Jr. Day_plus_1
7  2012-01-17  US_Martin Luther King Jr. Day_plus_1
8  2013-01-22  US_Martin Luther King Jr. Day_plus_1
9  2014-01-21  US_Martin Luther King Jr. Day_plus_1
10 2015-01-20  US_Martin Luther King Jr. Day_plus_1, 'US_Independence Day_plus_1':          date                  event_name
0  2016-07-05  US_Independence Day_plus_1
1  2017-07-05  US_Independence Day_plus_1
2  2007-07-05  US_Independence Day_plus_1
3  2008-07-05  US_Independence Day_plus_1
4  2009-07-04  US_Independence Day_plus_1
5  2010-07-06  US_Independence Day_plus_1
6  2011-07-05  US_Independence Day_plus_1
7  2012-07-05  US_Independence Day_plus_1
8  2013-07-05  US_Independence Day_plus_1
9  2014-07-05  US_Independence Day_plus_1
10 2015-07-04  US_Independence Day_plus_1, 'US_Martin Luther King Jr. Day_plus_2':          date                            event_name
0  2016-01-20  US_Martin Luther King Jr. Day_plus_2
1  2017-01-18  US_Martin Luther King Jr. Day_plus_2
2  2007-01-17  US_Martin Luther King Jr. Day_plus_2
3  2008-01-23  US_Martin Luther King Jr. Day_plus_2
4  2009-01-21  US_Martin Luther King Jr. Day_plus_2
5  2010-01-20  US_Martin Luther King Jr. Day_plus_2
6  2011-01-19  US_Martin Luther King Jr. Day_plus_2
7  2012-01-18  US_Martin Luther King Jr. Day_plus_2
8  2013-01-23  US_Martin Luther King Jr. Day_plus_2
9  2014-01-22  US_Martin Luther King Jr. Day_plus_2
10 2015-01-21  US_Martin Luther King Jr. Day_plus_2, 'US_Independence Day':          date           event_name
0  2016-07-04  US_Independence Day
1  2017-07-04  US_Independence Day
2  2007-07-04  US_Independence Day
3  2008-07-04  US_Independence Day
4  2009-07-03  US_Independence Day
5  2010-07-05  US_Independence Day
6  2011-07-04  US_Independence Day
7  2012-07-04  US_Independence Day
8  2013-07-04  US_Independence Day
9  2014-07-04  US_Independence Day
10 2015-07-03  US_Independence Day, 'US_Labor Day_plus_2':          date           event_name
0  2016-09-07  US_Labor Day_plus_2
1  2017-09-06  US_Labor Day_plus_2
2  2007-09-05  US_Labor Day_plus_2
3  2008-09-03  US_Labor Day_plus_2
4  2009-09-09  US_Labor Day_plus_2
5  2010-09-08  US_Labor Day_plus_2
6  2011-09-07  US_Labor Day_plus_2
7  2012-09-05  US_Labor Day_plus_2
8  2013-09-04  US_Labor Day_plus_2
9  2014-09-03  US_Labor Day_plus_2
10 2015-09-09  US_Labor Day_plus_2, 'US_Columbus Day_plus_1':          date              event_name
0  2016-10-11  US_Columbus Day_plus_1
1  2017-10-10  US_Columbus Day_plus_1
2  2007-10-09  US_Columbus Day_plus_1
3  2008-10-14  US_Columbus Day_plus_1
4  2009-10-13  US_Columbus Day_plus_1
5  2010-10-12  US_Columbus Day_plus_1
6  2011-10-11  US_Columbus Day_plus_1
7  2012-10-09  US_Columbus Day_plus_1
8  2013-10-15  US_Columbus Day_plus_1
9  2014-10-14  US_Columbus Day_plus_1
10 2015-10-13  US_Columbus Day_plus_1, 'US_Martin Luther King Jr. Day_minus_2':          date                             event_name
0  2016-01-16  US_Martin Luther King Jr. Day_minus_2
1  2017-01-14  US_Martin Luther King Jr. Day_minus_2
2  2007-01-13  US_Martin Luther King Jr. Day_minus_2
3  2008-01-19  US_Martin Luther King Jr. Day_minus_2
4  2009-01-17  US_Martin Luther King Jr. Day_minus_2
5  2010-01-16  US_Martin Luther King Jr. Day_minus_2
6  2011-01-15  US_Martin Luther King Jr. Day_minus_2
7  2012-01-14  US_Martin Luther King Jr. Day_minus_2
8  2013-01-19  US_Martin Luther King Jr. Day_minus_2
9  2014-01-18  US_Martin Luther King Jr. Day_minus_2
10 2015-01-17  US_Martin Luther King Jr. Day_minus_2, 'Holiday_positive_group':          date event_name
0  2016-11-25      event
1  2017-11-24      event
2  2007-11-23      event
3  2008-11-28      event
4  2009-11-27      event
5  2010-11-26      event
6  2011-11-25      event
7  2012-11-23      event
8  2013-11-29      event
9  2014-11-28      event
10 2015-11-27      event
11 2016-01-03      event
12 2017-01-04      event
13 2007-01-03      event
14 2008-01-03      event
15 2009-01-03      event
16 2010-01-03      event
17 2011-01-02      event
18 2012-01-04      event
19 2013-01-03      event
20 2014-01-03      event
21 2015-01-03      event
22 2016-06-01      event
23 2017-05-31      event
24 2007-05-30      event
25 2008-05-28      event
26 2009-05-27      event
27 2010-06-02      event
28 2011-06-01      event
29 2012-05-30      event
30 2013-05-29      event
31 2014-05-28      event
32 2015-05-27      event
33 2015-12-30      event
34 2016-12-31      event
35 2006-12-30      event
36 2007-12-30      event
37 2008-12-30      event
38 2009-12-30      event
39 2010-12-29      event
40 2011-12-31      event
41 2012-12-30      event
42 2013-12-30      event
43 2014-12-30      event
44 2015-12-31      event
45 2017-01-01      event
46 2006-12-31      event
47 2007-12-31      event
48 2008-12-31      event
49 2009-12-31      event
50 2010-12-30      event
51 2012-01-01      event
52 2012-12-31      event
53 2013-12-31      event
54 2014-12-31      event, 'Holiday_negative_group':          date event_name
0  2016-11-13      event
1  2017-11-12      event
2  2007-11-14      event
3  2008-11-13      event
4  2009-11-13      event
..        ...        ...
83 2011-07-06      event
84 2012-07-06      event
85 2013-07-06      event
86 2014-07-06      event
87 2015-07-05      event

[88 rows x 2 columns]}