Extract Mode (Time Series)

Synopsis

This operator calculates the mode (most frequent values) of one or more time series.

Description

This operator calculates one or more modes (most frequent values) of one or more time series. The calculated features are provided as an ExampleSet at the features output port of the operator. The maximal order of modes (most frequent, second most frequent, ...) can be defined by the parameter max mode order. The parameter multi modal mode defines how several values with the same frequency (this is called multimodal) are handled.

Depending on the parameter add time series name the ExampleSet will have one example with attributes for all combination of time series and features, or n examples, one example per time series. In combination with the Process Windows operator, this operator can be used to calculate features of windows of time series as a preparation for a general machine learning problem.

By default invalid values (missing for all time series, positive infinity and negative infinity for numeric time series and empty strings for nominal time series) are included in the determination of the modes. If one of this invalid values is the most frequent in a time series, the computed mode feature is this value. Select the parameter ignore invalid values to change this and ignore invalid values.

This operator works on all time series (numerical, nominal and time series with date time values).

Input

  • example set (IOObject)

    The ExampleSet which contains the time series data as attributes.

Output

  • features (IOObject)

    The ExampleSet which contains the calculated modes as attributes. Depending on the parameter add time series name the ExampleSet will have one example with attributes for all combination of time series and features, or n examples, one example per time series.

  • original (IOObject)

    The ExampleSet that was given as input is passed through without changes.

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. 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.

    Range:
  • 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.

    Range:
  • 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.

    Range:
  • max_mode_order

    This parameter defines the maximum order of modes which is extracted.

    Range:
  • multi_modal_mode

    This parameter defines how values with the same frequency are handled:

    • first k occurence: Only the first k values (first in respect of their occurence in the series) are returned. k is defined by the parameter max k.
    • random k: k values randomly drawn are returned. k is defined by the parameter max k.
    • all: All multimodal values are returned.
    Range:
  • max_k

    The maximum number of values per mode order, which are calculated in case the multi modal mode is set to first k occurence or random k.

    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.

    Range:
  • ignore_invalid_values

    If this parameter is set to true invalid values (missing for all time series, positive infinity and negative infinity for numeric time series and empty strings for nominal time series) are ignored in the calculation of the modes.

    Range:
  • use_local_random_seed

    This parameter indicates if a local random seed should be used in case the multi modal mode is set to random k

    Range:
  • local_random_seed

    If the use local random seed parameter is checked this parameter determines the local random seed.

    Range:

Tutorial Processes

Extract Modes of the Daily Mean Temperatur Data set

In this tutorial process, the 4 most frequent temperature values of the Daily Mean Temperatur Data Set are extracted. See the comments in the process for a detailed description.