Windowing (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. 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.
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• 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.

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• 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.

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• 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.

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• 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.

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• 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).

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• value_type

  This option allows to select a type of attribute.

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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.

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• 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.

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• block_type

  This option allows to select a block type of attribute.

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• 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.

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• 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.

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• 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 ||.

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• 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.

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• 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, an additional attribute is added which contains the value of the last index value in the corresponding window and is named: Last <indices attribute> in window. The role of this attribute is set to the ID role. If this parameter is set to false, an attribute named Window id is added with the number of the corresponding window (starting from 0).

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• 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.

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• expert_settings

  This parameter can be selected to show expert settings for a more detailed configuration of the operator. The expert settings are: windows defined, custom start point, custom end point, date format, no overlapping windows, horizon offset and empty window handling.

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• unit

  The mode on how windows are defined. It defines the unit of the window parameters (window size, step size, horizon size and horizon offset).

  • example based: The window parameters are specified in number of examples. This is the default option.
  • time based: The window parameter are specified in time durations/periods (units ranging from milliseconds to years).
  • custom: An additional example set has to be provided to the new "custom windows" input port. It holds the start (and optional the stop values) of the windows.
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• windows_defined

  This parameter defines the point from which the windows are defined of. It is an expert setting and hence it is only shown if the parameter expert settings is selected.

  • from start: The first window will start at the first example of the input data set. The following windows are set up according to the window parameters.
  • from end: The last window will end at the last example of the input data set. The previous windows are set up according to the window parameters.
  • custom start: The first window will start at the custom start point provided by the parameter custom start point / custom start time. The following windows are set up according to the window parameters.
  • custom end: The last window will end at the custom end point provided by the parameter custom end point / custom end time. The previous windows are set up according to the window parameters.
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• custom_start_point

  If the parameter windows defined is set to custom start and the unit is set to example based, this parameter defines the custom point from which the windows start. It is an expert setting and hence it is only shown if the parameter expert settings is selected.

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• custom_end_point

  If the parameter windows defined is set to custom end and the unit is set to example based, this parameter defines the custom point where the windows end. It is an expert setting and hence it is only shown if the parameter expert settings is selected.

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• custom_start_time

  If the parameter windows defined is set to custom start and the unit is set to time based, this parameter defines the custom date time point from which the windows start.

  The date time format used to interpret the string provided in this parameter is defined by the parameter date format. It is an expert setting and hence it is only shown if the parameter expert settings is selected.

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• custom_end_time

  If the parameter windows defined is set to custom end and the unit is set to time based, this parameter defines the custom date time point where the windows end.

  The date time format used to interpret the string provided in this parameter is defined by the parameter date format. It is an expert setting and hence it is only shown if the parameter expert settings is selected.

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• date_format

  Date format used for the custom start time and custom end time parameters. It is an expert setting and hence it is only shown if the parameter expert settings is selected.

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• window_size

  The number of values in one window. The windowed ExampleSet will contain one attribute per value in the window. The attributes are named <time series attribute name> - i with i running from (window size - 1), ..., 0 The window size has to be smaller or equal to the length of the time series.

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• window_size_time

  The time duration/period of one window.

  The windowed example set will contain one attribute per example in the window. Hence, the window with the most examples will define how many attributes the resulting windowed example set will have. For windows with less examples, the other attributes are filled with missing values.

  The attributes are named <time series attribute name> - i with i running from (max number of examples in windows - 1), ..., 0

  The window size time has to be smaller or equal to the time duration of the time series.

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• no_overlapping_windows

  If this parameter is set to true, the parameter stepsize is determined automatically, so that windows and horizons don't overlap. The stepsize is set to window size + horizon size + horizon offset. It is an expert setting and hence it is only shown if the parameter expert settings is selected.

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• step_size

  The step size between the first values of two consecutive windows. E.g. with a window size of 10 and a step size of 2, the first window has the values from 0, ..., 9, the second window the values from 2, ..., 11 and so on. If no overlapping windows is set to true the step size is automatically determined depending on window size, horizon size and horizon offset.

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• step_size_time

  The step size (in units of time) between the start points of two consecutive windows. E.g. with a window size of 1 week and a step size of 2 days, the first window has the days from 0, ..., 6, the second window the days from 2, ..., 8 and so on. If no overlapping windows is set to true the step size time is automatically determined depending on window size time, horizon size time and horizon offset time.

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• create_horizon_(labels)

  If this parameter is set to true, one or more label attributes are created, containing the horizon of the windows. The parameter horizon attribute, horizon size and horizon offset are used to define the horizon.

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• horizon_attribute

  If the parameter create horizon (labels) is set to true, this parameter defines the attribute holding the horizon. The attribute name can be selected from the drop down box of the parameter if the meta data is known.

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• horizon_size

  The number of values taken as the horizon. For each value in the horizon, an attribute is created, named <time series attribute name> + i (horizon) with i running from 1, ..., horizon size. If the horizon size is one, the role of the created attribute is set to label. If the size is larger (and thus more than one attribute is created), the role of the attributes are set to Horizon + i, with i running from 1, ..., horizon size.

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• horizon_size_time

  The time duration/period taken as the horizon. The windowed example set will contain one attribute per example in the horizon window. Hence, the horizon window with the most examples (maximum number of horizon values) will define how many attributes are added. For windows with less examples, the other attributes are filled with missing values.

  The name of the new attributes are <time series attribute name> + i (horizon) with i running from 1, ..., maximum number of horizon values. If the maximum number of horizon values is one, the role of the created attribute is set to label. If it is larger the role of the attributes are set to Horizon + i, with i running from 1, ..., horizon size.

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• horizon_offset

  The offset between the windows and their corresponding horizon. If the offset is 0 the horizon is taken from the consecutive values behind the window. Otherwise the horizon is horizon offset values behind the end of the window. It is an expert setting and hence it is only shown if the parameter expert settings is selected.

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• horizon_offset_time

  The offset (in time units) between the windows and their corresponding horizons. If the offset is 0 the horizon is set directly behind the window. Otherwise the horizon starts the time duration provided by this parameter behind the end of the window. It is an expert setting and hence it is only shown if the parameter expert settings is selected.

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• windows_stop_definition

  Defines if the end of the custom windows are either defined by the start of the next window (windows are spanning over the whole index range) or from an additional attribute.

  • from next window start: The end of the windows are defined by the start of the next window (windows are spanning over the whole index range) Training windows end at the start of the next horizon window (or the next training window, if there aren't horizon windows). Horizon windows end at the start of the next training window. Be aware that the last value of the start definition values (the last value of the horizon start attribute or the last value of the window start attribute, if there aren't horizon windows) is only used as the end of the final window.
  • from attribute: The end of the windows are defined by additional attribute(s) in the custom window example set. The attribute names have to be provided by the parameters window stop attribute and horizon stop attribute.
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• window_start_attribute

  This parameter defines the attribute in the custom window example set (the example set provided at the custom windows input port) which contains the start values for the custom training windows.

  The window start attribute, window stop attribute, horizon start attribute and horizon stop attribute have to be of the same data type. If the data type is integer, the windowing is example based (see parameter unit) otherwise the attributes needs to be the same data type as the indices attribute.

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• window_stop_attribute

  This parameter defines the attribute in the custom window example set (the example set provided at the custom windows input port) which contains the end values for the custom training windows.

  The window start attribute, window stop attribute, horizon start attribute and horizon stop attribute have to be of the same data type. If the data type is integer, the windowing is example based (see parameter unit) otherwise the attributes needs to be the same data type as the indices attribute.

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• horizon_start_attribute

  This parameter defines the attribute in the custom window example set (the example set provided at the custom windows input port) which contains the start values for the custom horizon windows.

  The window start attribute, window stop attribute, horizon start attribute and horizon stop attribute have to be of the same data type. If the data type is integer, the windowing is example based (see parameter unit) otherwise the attributes needs to be the same data type as the indices attribute.

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• horizon_stop_attribute

  This parameter defines the attribute in the custom window example set (the example set provided at the custom windows input port) which contains the stop values for the custom horizon windows.

  The window start attribute, window stop attribute, horizon start attribute and horizon stop attribute have to be of the same data type. If the data type is integer, the windowing is example based (see parameter unit) otherwise the attributes needs to be the same data type as the indices attribute.

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• empty_window_handling

  This parameter defines how empty windows (windows which do not contain an Example) will be handled. It is an expert setting and hence it is only shown if the parameter expert settings is selected.

  • add empty exampleset: Empty windows will be added as an empty ExampleSet, or a row with missing values.
  • skip: Empty windows will be skipped completely in the processing. If horizon windows are created as well and either the training or the horizon window is empty, the processing for both windows is skipped
  • fail: A user error is thrown, if an empty window occurs.
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