Windowing

Synopsis

This operator converts one or more time series to a windowed ExampleSet with the windowed values and, if enabled, the horizon values as attributes.

Description

This operator converts time series data into a windowed ExampleSet which can be processed for example with standard machine learning methods. For each time series attribute window size attributes are created, holding the values of the corresponding window. If create horizon (label) is selected horizon size additional attributes are also created, holding the horizon values of the corresponding window.

If has indices is selected, also the last index value of the time series in the corresponding window is added. If has indices is not selected an attribute named Window id with the number of the corresponding window (starting from 0) is added. See the description of the parameter for details.

The described behavior is the default example based windowing. It can be changed to time based windowing or custom windowing by changing the unit parameter. For time based windowing, the windowing parameter are specified in time durations/periods. For the "custom" windowing an additional ExampleSet has to be provided to the new "custom windows" input port. It holds the start (and optional the stop values) of the windows. For more details see the unit parameter and the description of the corresponding parameters.

Expert settings (for example no overlapping windows, a value for the horizon offset, the empty window handling, ...) can be enabled by selecting the corresponding expert settings parameter.

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

Input

example set

The ExampleSet which contains the time series data as attributes.

custom windows

The example set which contains the start (and stop) values of the custom windows. Only needs to be connected if the parameter unit is set to custom.

Output

windowed example set

The windowed ExampleSet. For each time series attribute window size attributes are created. Also for each value in the horizon an attribute is created. If an indices attribute is specified, an additional attribute containing the index value of the last value in the window is created. Otherwise an attribute named Window id with the number of the corresponding window (starting from 0) is added.

original

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

Parameters

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.

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.

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.

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.

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.

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).

Value type

This option allows to select a type of attribute.

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.

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.

Block type

This option allows to select a block type of attribute.

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.

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.

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 ||.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Synopsis​

Description​

Input​

example set​

custom windows​

Output​

windowed example set​

original​

Parameters​

Attribute filter type​

Attribute​

Attributes​

Regular expression​

Use except expression​

Except regular expression​

Value type​

Use value type exception​

Except value type​

Block type​

Use block type exception​

Except block type​

Numeric condition​

Invert selection​

Include special attributes​

Has indices​

Indices attribute​

Expert settings​

Unit​

Windows defined​

Custom start point​

Custom end point​

Custom start time​

Custom end time​

Date format​

Window size​

Window size time​

No overlapping windows​

Step size​

Step size time​

Create horizon (labels)​

Horizon attribute​

Horizon size​

Horizon size time​

Horizon offset​

Horizon offset time​

Windows stop definition​

Window start attribute​

Window stop attribute​

Horizon start attribute​

Horizon stop attribute​

Empty window handling​