Quick Start

In this example, we build a model to detect violation of seasonal (weekly and daily) traffic pattern. The data used here is the NYC taxi traffic dataset from Numenta Anomaly Benchmark.

1. Load and validate time series for training.

   >>> import pandas as pd
   >>> s_train = pd.read_csv("./training.csv", index_col="Datetime", parse_dates=True, squeeze=True)
   >>> from adtk.data import validate_series
   >>> s_train = validate_series(s_train)
   >>> print(s_train)
   Time
   2014-07-01 00:00:00    10844
   2014-07-01 00:30:00     8127
   2014-07-01 01:00:00     6210
   2014-07-01 01:30:00     4656
   2014-07-01 02:00:00     3820
                          ...
   2015-01-04 09:30:00     9284
   2015-01-04 10:00:00    10955
   2015-01-04 10:30:00    13348
   2015-01-04 11:00:00    13517
   2015-01-04 11:30:00    14443
   Freq: 30T, Name: Traffic, Length: 9000, dtype: int64
   

2. Visualize training time series.

   >>> from adtk.visualization import plot
   >>> plot(s_train)
   

   

3. Detect violation of seasonal pattern.

   >>> from adtk.detector import SeasonalAD
   >>> seasonal_ad = SeasonalAD()
   >>> anomalies = seasonal_ad.fit_detect(s_train)
   >>> plot(s_train, anomaly=anomalies, anomaly_color="red", anomaly_tag="marker")
   

   

4. If known anomalies are available, cross check with detection results.

   >>> known_anomalies = pd.read_csv("./known_anomalies.csv", index_col="Datetime", parse_dates=True, squeeze=True)
   >>> from adtk.data import to_events
   >>> known_anomalies = to_events(known_anomalies)
   >>> print(known_anomalies)
   [(Timestamp('2014-07-03 07:00:00', freq='30T'),
     Timestamp('2014-07-06 14:59:59.999999999', freq='30T')),
    (Timestamp('2014-08-31 18:30:00', freq='30T'),
     Timestamp('2014-09-01 21:59:59.999999999', freq='30T')),
    (Timestamp('2014-10-31 14:30:00', freq='30T'),
     Timestamp('2014-11-02 13:59:59.999999999', freq='30T')),
    (Timestamp('2014-11-26 19:00:00', freq='30T'),
     Timestamp('2014-11-29 14:29:59.999999999', freq='30T')),
    (Timestamp('2014-12-23 19:00:00', freq='30T'),
     Timestamp('2014-12-28 13:59:59.999999999', freq='30T')),
    (Timestamp('2014-12-28 19:30:00', freq='30T'),
     Timestamp('2015-01-02 21:29:59.999999999', freq='30T'))]
   >>> plot(s_train,
            anomaly={"Known": known_anomalies, "Model": anomalies},
            anomaly_tag={"Known": "span", "Model": "marker"},
            anomaly_color={"Known": "orange", "Model": "red"})
   

   

5. Apply the trained model to new data.

   >>> s_test = pd.read_csv("./testing.csv", index_col="Datetime", parse_dates=True, squeeze=True)
   >>> s_test = validate_series(s_test)
   >>> print(s_test)
   Datetime
   2015-01-04 12:00:00    15285
   2015-01-04 12:30:00    16028
   2015-01-04 13:00:00    16329
   2015-01-04 13:30:00    15891
   2015-01-04 14:00:00    15960
                          ...
   2015-01-31 21:30:00    24670
   2015-01-31 22:00:00    25721
   2015-01-31 22:30:00    27309
   2015-01-31 23:00:00    26591
   2015-01-31 23:30:00    26288
   Freq: 30T, Name: Traffic, Length: 1320, dtype: int64
   >>> anomalies_pred = seasonal_ad.detect(s_test)
   >>> plot(s_test, anomaly=anomalies_pred,
            ts_linewidth=1, anomaly_color='red', anomaly_tag="marker")
   

   

For more examples, please check Examples. But before that, we recommend you to read User Guide first.