Note

Click here to download the full example code

Changepoint Detection

You can detect trend and seasonality changepoints with just a few lines of code.

Provide your timeseries as a pandas dataframe with timestamp and value.

For example, to work with daily sessions data, your dataframe could look like this:

import pandas as pd
df = pd.DataFrame({
    "datepartition": ["2020-01-08-00", "2020-01-09-00", "2020-01-10-00"],
    "macrosessions": [10231.0, 12309.0, 12104.0]
})

The time column can be any format recognized by pd.to_datetime.

In this example, we’ll load a dataset representing log(daily page views) on the Wikipedia page for Peyton Manning. It contains values from 2007-12-10 to 2016-01-20. More dataset info here.

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 import warnings

 warnings.filterwarnings("ignore")

 import pandas as pd
 import plotly

 from greykite.algo.changepoint.adalasso.changepoint_detector import ChangepointDetector
 from greykite.framework.benchmark.data_loader_ts import DataLoaderTS
 from greykite.framework.templates.autogen.forecast_config import ForecastConfig
 from greykite.framework.templates.forecaster import Forecaster
 from greykite.framework.templates.model_templates import ModelTemplateEnum

 # Loads dataset into UnivariateTimeSeries
 dl = DataLoaderTS()
 ts = dl.load_peyton_manning_ts()
 df = ts.df  # cleaned pandas.DataFrame

Detect trend change points

Let’s plot the original timeseries. There are actually trend changes within this data set. The UnivariateTimeSeries class is used to store a timeseries and to provide basic description and plotting functions. The load_peyton_manning function automatically returns a UnivariateTimeSeries instance, however, for any df, you can always initialize a UnivariateTimeSeries instance and do further explorations. (The interactive plot is generated by plotly: click to zoom!)

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 fig = ts.plot()
 plotly.io.show(fig)

ChangepointDetector utilizes pre-filters, regularization with regression based models, and post-filters to find time points where trend changes.

To create a simple trend changepoint detection model, we first initialize the ChangepointDetector class, then run its attribute function find_trend_changepoints.

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 model = ChangepointDetector()
 res = model.find_trend_changepoints(
     df=df,            # data df
     time_col="ts",    # time column name
     value_col="y")    # value column name
 pd.DataFrame({"trend_changepoints": res["trend_changepoints"]})  # prints a dataframe showing the result
trend_changepoints
0 2008-02-06
1 2008-07-06
2 2008-09-20
3 2008-12-18
4 2009-02-13
5 2009-06-08
6 2009-09-03
7 2009-12-07
8 2010-02-04
9 2010-07-02
10 2010-10-30
11 2011-01-24
12 2011-04-21
13 2011-07-16
14 2011-10-11
15 2011-12-09
16 2012-02-06
17 2013-02-15
18 2013-08-08
19 2014-01-28
20 2014-03-27
21 2014-12-12
22 2015-06-03


The code above runs trend changepoint detection with the default parameters. We may visualize the detection results by plotting it with the attribute function plot.

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 fig = model.plot(plot=False)  # plot = False returns a plotly figure object.
 plotly.io.show(fig)