class hana_ml.algorithms.pal.tsa.outlier_detection.OutlierDetectionTS(window_size=None, outlier_method=None, threshold=None, detect_seasonality=None, alpha=None, extrapolation=None, periods=None, random_state=None, n_estimators=None, max_samples=None, bootstrap=None, contamination=None, minpts=None, eps=None, thread_ratio=None)

Outlier detection for time-series.

window_sizeint, optional

Odd number, the window size for median filter, not less than 3.

Defaults to 3.

outlier_methodstr, optional

The method for calculate the outlier score from residual.

  • 'z1' : Z1 score.

  • 'z2' : Z2 score.

  • 'iqr' : IQR score.

  • 'mad' : MAD score.

  • 'isolationforest' : isolation forest score.

  • 'dbscan' : DBSCAN.

Defaults to 'z1'.

thresholdfloat, optional

The threshold for outlier score. If the absolute value of outlier score is beyond the threshold, we consider the corresponding data point as an outlier.

Only valid when outlier_method = 'iqr', 'isolationforest', 'mad', 'z1', 'z2'. For outlier_method = 'isolationforest', when contamination is provided, threshold is not valid and outliers are decided by contamination.

Defaults to 3 when outlier_method is 'mad', 'z1' and 'z2'. Defaults to 1.5 when outlier_method is 'iqr'. Defaults to 0.7 when outlier_method is 'isolationforest'.

detect_seasonalitybool, optional

When calculating the residual,

  • False: Does not consider the seasonal decomposition.

  • True: Considers the seasonal decomposition.

Defaults to False.

alphafloat, optional

The criterion for the autocorrelation coefficient. The value range is (0, 1).

A larger value indicates a stricter requirement for seasonality.

Only valid when detect_seasonality is True.

Defaults to 0.2.

extrapolationbool, optional

Specifies whether to extrapolate the endpoints. Set to True when there is an end-point issue.

Only valid when detect_seasonality is True.

Defaults to False.

periodsint, optional

When this parameter is not specified, the algorithm will search the seasonal period. When this parameter is specified between 2 and half of the series length, autocorrelation value is calculated for this number of periods and the result is compared to alpha parameter. If correlation value is equal to or higher than alpha, decomposition is executed with the value of periods. Otherwise, the residual is calculated without decomposition. For other value of parameter periods, the residual is also calculated without decomposition.

Only valid when detect_seasonality is True. If the user knows the seasonal period, specifying periods can speed up the calculation, especially when the time series is long.

No Default value.

random_stateint, optional

Specifies the seed for random number generator.

  • 0: Uses the current time (in second) as seed.

  • Others: Uses the specified value as seed.

Only valid when outlier_method is 'isolationforest'.

Default to 0.

n_estimatorsint, optional

Specifies the number of trees to grow.

Only valid when outlier_method is 'isolationforest'.

Default to 100.

max_samplesint, optional

Specifies the number of samples to draw from input to train each tree. If max_samples is larger than the number of samples provided, all samples will be used for all trees.

Only valid when outlier_method is 'isolationforest'.

Default to 256.

bootstrapbool, optional

Specifies sampling method.

  • False: Sampling without replacement.

  • True: Sampling with replacement.

Only valid when outlier_method is 'isolationforest'.

Default to False.

contaminationdouble, optional

The proportion of outliers in the data set. Should be in the range (0, 0.5].

Only valid when outlier_method is 'isolationforest'. When outlier_method is 'isolationforest' and contamination is specified, threshold is not valid.

No Default value.

minptsint, optional

Specifies the minimum number of points required to form a cluster. The point itself is not included in minpts.

Only valid when outlier_method is 'dbscan'.

Defaults to 1.

epsfloat, optional

Specifies the scan radius.

Only valid when outlier_method is 'dbscan'.

Defaults to 0.5.

thread_ratiofloat, optional

The ratio of available threads.

  • 0: single thread.

  • 0~1: percentage.

  • Others: heuristically determined.

Only valid when detect_seasonality is True or outlier_method is 'isolationforest' or 'dbscan'.

Defaults to -1.


Outlier detection methods implemented in this class are commonly consisted of two steps:

Please refer to the above links for detailed description of all methods as well as related parameters.


Time series DataFrame df:

>>> df.collect().head()
0    1       2.0
1    2       2.5
2    3       3.2
3    4       2.8
14  15       5.3
15  16      10.0
16  17       4.6
17  18       4.4
18  19       4.8
19  20       5.1

Initialize the class:

>>> tsod = OutlierDetectionTS(detect_seasonality=False,
>>> res = tsod.fit_predict(data=df,

Outputs and attributes:

>>> res.collect()
0           1       2.0       0.0      -0.297850           0
1           2       2.5       0.0      -0.297850           0
2           3       3.2       0.4      -0.010766           0
13         14       5.1       0.0      -0.297850           0
14         15       5.3       0.0      -0.297850           0
15         16      10.0       4.7       3.075387           1
16         17       4.6       0.0      -0.297850           0
17         18       4.4      -0.2      -0.441392           0
18         19       4.8       0.0      -0.297850           0
19         20       5.1       0.0      -0.297850           0
>>> tsod.stats_.collect()
1          OutlierNum          1
2                Mean      0.415
3  Standard Deviation    1.39332
4          HandleZero          0
Data statistics related to time-series outlier detection, structured as follows:
  • STAT_NAME : Name of statistics.

  • STAT_VALUE : Value of statistics.

Relevent metrics for time-series outlier detection, structured as follows:
  • NAME : Metric name.

  • VALUE : Metric value.


fit_predict(data[, key, endog])

Detection of outliers in time-series data.

fit_predict(data, key=None, endog=None)

Detection of outliers in time-series data.


Input data containing the target time-series.

data should have at least two columns: one is ID column, the other is raw data.

keystr, optional

Specifies the ID column, in this case the column that shows the order of time-series.

It is recommended that you always specifies this column manually.

Defaults to the first column of data if the index column of data is not provided. Otherwise, defaults to the index column of data.

endogstr, optional

Specifies the column that contains the values of time-series to be tested.

Defaults to the first non-key column.

Outlier detection result, structured as follows:
  • TIMESTAMP : ID of data.

  • RAW_DATA : Original value.

  • RESIDUAL : Residual.

  • OUTLIER_SCORE : Outlier score.

  • IS_OUTLIER : 0: normal, 1: outlier.

property fit_hdbprocedure

Returns the generated hdbprocedure for fit.

property predict_hdbprocedure

Returns the generated hdbprocedure for predict.

Inherited Methods from PALBase

Besides those methods mentioned above, the OutlierDetectionTS class also inherits methods from PALBase class, please refer to PAL Base for more details.