Extract Peaks (Time Series)

Parameters

• attribute_filter_type

  This parameter allows you to select the filter for the time series attributes selection filter; the method you want to select the attributes which holds the time series values. Only numeric attributes can be selected as time series attributes. The different filter types are:

  • all: This option selects all attributes of the ExampleSet to be time series attributes. This is the default option.
  • single: This option allows the selection of a single time series attribute. The required attribute is selected by the attribute parameter.
  • subset: This option allows the selection of multiple time series attributes through a list (see parameter attributes). If the meta data of the ExampleSet is known all attributes are present in the list and the required ones can easily be selected.
  • regular_expression: This option allows you to specify a regular expression for the time series attribute selection. The regular expression filter is configured by the parameters regular expression, use except expression and except expression.
  • value_type: This option allows selection of all the attributes of a particular type to be time series attributes. It should be noted that types are hierarchical. For example real and integer types both belong to the numeric type. The value type filter is configured by the parameters value type, use value type exception, except value type.
  • block_type: This option allows the selection of all the attributes of a particular block type to be time series attributes. It should be noted that block types may be hierarchical. For example value_series_start and value_series_end block types both belong to the value_series block type. The block type filter is configured by the parameters block type, use block type exception, except block type.
  • no_missing_values: This option selects all attributes of the ExampleSet as time series attributes which do not contain a missing value in any example. Attributes that have even a single missing value are not selected.
  • numeric_value_filter: All numeric attributes whose examples all match a given numeric condition are selected as time series attributes. The condition is specified by the numeric condition parameter.
  Range:
• attribute

  The required attribute can be selected from this option. The attribute name can be selected from the drop down box of the parameter if the meta data is known.

  Range:
• attributes

  The required attributes can be selected from this option. This opens a new window with two lists. All attributes are present in the left list. They can be shifted to the right list, which is the list of selected time series attributes.

  Range:
• regular_expression

  Attributes whose names match this expression will be selected. The expression can be specified through the edit and preview regular expression menu. This menu gives a good idea of regular expressions and it also allows you to try different expressions and preview the results simultaneously.

  Range:
• use_except_expression

  If enabled, an exception to the first regular expression can be specified. This exception is specified by the except regular expression parameter.

  Range:
• except_regular_expression

  This option allows you to specify a regular expression. Attributes matching this expression will be filtered out even if they match the first expression (expression that was specified in regular expression parameter).

  Range:
• value_type

  This option allows to select a type of attribute. One of the following types can be chosen: numeric, integer, real.

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• use_value_type_exception

  If enabled, an exception to the selected type can be specified. This exception is specified by the except value type parameter.

  Range:
• except_value_type

  The attributes matching this type will be removed from the final output even if they matched the before selected type, specified by the value type parameter. One of the following types can be selected here: numeric, integer, real.

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• block_type

  This option allows to select a block type of attribute. One of the following types can be chosen: value_series, value_series_start, value_series_end.

  Range:
• use_block_type_exception

  If enabled, an exception to the selected block type can be specified. This exception is specified by the except block type parameter.

  Range:
• except_block_type

  The attributes matching this block type will be removed from the final output even if they matched the before selected type by the block type parameter. One of the following block types can be selected here: value_series, value_series_start, value_series_end.

  Range:
• numeric_condition

  The numeric condition used by the numeric condition filter type. A numeric attribute is selected if all examples match the specified condition for this attribute. For example the numeric condition '> 6' will keep all numeric attributes having a value of greater than 6 in every example. A combination of conditions is possible: '> 6 && < 11' or '<= 5 || < 0'. But && and || cannot be used together in one numeric condition. Conditions like '(> 0 && < 2) || (>10 && < 12)' are not allowed because they use both && and ||.

  Range:
• invert_selection

  If this parameter is set to true the selection is reversed. In that case all attributes not matching the specified condition are selected as time series attributes. Special attributes are not selected independent of the invert selection parameter as along as the include special attributes parameter is not set to true. If so the condition is also applied to the special attributes and the selection is reversed if this parameter is checked.

  Range:
• include_special_attributes

  Special attributes are attributes with special roles. These are: id, label, prediction, cluster, weight and batch. Also custom roles can be assigned to attributes. By default special attributes are not selected as time series attributes irrespective of the filter conditions. If this parameter is set to true, special attributes are also tested against conditions specified and those attributes are selected that match the conditions.

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• has_indices

  This parameter indicates if there is an index attribute associated with the time series. If this parameter is set to true, the index attribute has to be selected.

  Range:
• indices_attribute

  If the parameter has indices is set to true, this parameter defines the associated index attribute. It can be either a date, date_time or numeric value type attribute. The attribute name can be selected from the drop down box of the parameter if the meta data is known.

  Range:
• peak_detection_method

  This parameter defines which peak detection algorithm is used to detect the peaks from which the features are extracted.

  • z-score: The Z-Score Peak detection algorithm is used. Please have a look into the help text of the Z-Score Peak Transformation operator for details of the algorithm.
  • highest-peak: The Highest Peaks detection algorithm is used. Please have a look into the help text of the Highest Peak Transformation operator for details of the algorithm.
  Range:
• number_of_peaks

  Maximum number of peaks to be detected. If the peak detection algorithm detects more peaks, only the largest (in terms of high-low amplitude of the peaks) are kept. Be aware that this maximum number is either for both peak types separately or combined (see parameter peak types).

  Range:
• peak_types

  This parameter defines the types (maximum/minimum) to be detected by the peak detection algorithm. n is the value of the number of peaks parameter.

  • only maxima: Only maximum peaks are detected. (maximal number of peaks is n)
  • only minima: Only minimum peaks are detected. (maximal number of peaks is n)
  • maxima and minima separately: Both maximum and minimum peaks are detected. The number of peaks is counted for each type separately (so that the maximal number of peaks is 2n)
  • maxima and minima combined: Both maximum and minimum peaks are detected. The number of peaks is counted for both types combined (so that the maximal number of peaks is n)
  Range:
• use_heuristics

  If selected the specific configuration parameters for the peak detection algorithm are determined by a heuristic. For details about the concrete heuristic used, see the help text of the use heuristics parameter of the corresponding Peak Transformation algorithm.

  For the z-score peak detection algorithm these are the parameters lag, threshold, influence and robust measures.

  For the highest peaks peak detection algorithm these are the parameters sloppy values and minimum change.

  Be aware that this is only a rough heuristic, for optimized results the parameters have to be adapted to your data.

  Range:
• lag

  Parameter for the z-score peak detection algorithm: The size of the window of previous data points that are considered for the peak detection. As a result the points in the first window can't be scored. The less the data changes over time, the larger the lag can be. For more volatile time series, a smaller lag is better suited.

  Range:
• threshold

  Parameter for the z-score peak detection algorithm: Value of the Z-Score above which a point is flagged as a peak. The threshold represents the number of standard deviations above a point is flagged as a peak.

  Range:
• influence

  Parameter for the z-score peak detection algorithm: The (relative) influence previous peaks have on the calculation of the Z-Score. If set to zero, they are completely ignored. An influence of 0 is therefore the most robust option (but assumes stationarity). If it's expected, that after a peak the data return to a normal value, an influence close to zero is appropriate.

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• robust_measures

  Parameter for the z-score peak detection algorithm: If selected, the more robust median and interquartile range (IQR) are used to calculate the Z-Score of a point. Otherwise the mean and standard deviation are used.

  Range:
• ignore_invalid_values

  If this parameter is set to true invalid values (missing, positive and negative infinity) are ignored in the calculation of the features.

  Range:
• sloppy_values

  Parameter for the highest peaks peak detection algorithm: Allowed number of sloppy values (values for which the relative change is smaller than the minimum change) until end of current peak is reached. Number of sloppy values should be increased for noisy data.

  Range:
• minimum_change

  Parameter for the highest peaks peak detection algorithm: Threshold on the relative change between last and next value to count the next value as a peak value.

  The relative change is calculated as the decrease / increase (maximum / minimum) between next value and last value divided by the distance of last value to the average:

  relative change = (lastValue - nextValue) / (lastValue - average)

  If the relative change is larger than the minimum change the next value is counted as a peak value. As the minimum change is the threshold per slice, the parameter defines how sharp a peak has to be, to be detected. If you expect wide peaks in your data, decrease the minimum change.

  Range:
• add_index_features

  If selected, additional features containing information about the index of the left, center and right side of the peaks are added.

  Range:
• add_time_series_name

  If this parameter is set to true the name of the time series attribute is added as a prefix to the name of the feature attributes. The resulting ExampleSet will have one example and n attributes, with n = <number of time series> x <number of features>. If this parameter is set to false, an additional attribute named time series is added with the name of the time series. The resulting ExampleSet will have n examples and m+1 attributes, with n = <number of time series> and m = <number of features>. The role of the time series attribute is set to id.

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