NeuralProphet之四：事件(Events)-CFANZ编程社区

NeuralProphet之四：事件(Events)

在预测问题中，经常需要考虑反复出现的特殊事件。

1 生成数据

首先生成事件训练数据

# data_location = "https://raw.githubusercontent.com/ourownstory/neuralprophet-data/main/datasets/"
data_location = 'datasets/'
df = pd.read_csv(data_location + 'wp_log_peyton_manning.csv')

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))
print(history_events_df)

history_df = m.create_df_with_events(df, history_events_df)
print(history_df)

生成事件预测数据

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))
print(future_events_df)

2 注册

通过add_events函数为NeuralProphet对象添加事件配置

m = NeuralProphet(
        n_forecasts=10,
        yearly_seasonality=False,
        weekly_seasonality=False,
        daily_seasonality=False,
    )
m = m.add_events(["superbowl", "playoff"])

事件参数配置

模式：additive和multiplicative

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

事件窗口：将一个特定事件周围的日子也视为特殊事件

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

也可以单独针对不同事件设置窗口

m = m.add_events("superbowl", lower_window=-1, upper_window=1)
m = m.add_events("playoff", upper_window=2)

国家法定假期：neural_prophet支持标准的特定国家的假期

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

正则化：

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

也可以单独针对不同事件正则化

m = m.add_events("superbowl", regularization=0.05)
m = m.add_events("playoff", regularization=0.03)

3 训练与预测

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

forecast = m.predict(df=history_df)
fig = m.plot(forecast)

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