ADLStream.data.MovingWindowStreamGenerator

Moving window stream generator.

This class performs a moving-window preprocessing method for time series forecasting problems.

TODO: Implement variable selection for x and y.

Parameters:

Name	Type	Description	Default
stream	inherits ADLStream.data.stream.BaseStream	Stream source to be feed to the ADLStream framework.	required
past_history	int	The width (number of time steps) of the input window (x).	required
forecasting_horizon	int	The width (number of time steps) of the label window (y).	required
shift	int >=1	The time offset between input and label windows. Defaults to 1	1

Source code in ADLStream/data/moving_window_generator.py

class MovingWindowStreamGenerator(BaseStreamGenerator):
    """Moving window stream generator.

    This class performs a moving-window preprocessing method for time series forecasting
    problems.

    TODO: Implement variable selection for x and y.

    Arguments:
        stream (inherits ADLStream.data.stream.BaseStream): 
            Stream source to be feed to the ADLStream framework.
        past_history (int): The width (number of time steps) of the input window (`x`).
        forecasting_horizon (int): 
            The width (number of time steps) of the label window (`y`).
        shift (int >=1, optional): The time offset between input and label windows.
            Defaults to 1     
    """

    def __init__(self, stream, past_history, forecasting_horizon, shift=1, **kwargs):
        super().__init__(stream, **kwargs)
        self.past_history = past_history
        self.forecasting_horizon = forecasting_horizon
        self.shift = shift

        self.x_window = []
        self.y_window = []

    def preprocess(self, message):
        x, y = None, None
        self.x_window.append(message)
        if self.num_messages >= self.past_history + self.shift:
            self.y_window.append(message)

        if len(self.x_window) > self.past_history:
            self.x_window.pop(0)
            x = self.x_window

        if len(self.y_window) > self.forecasting_horizon:
            self.y_window.pop(0)
            y = self.y_window

        return x, y

preprocess(self, message)

The function that contains the logic to transform a stream message into model imput and target data (x ,y).

Both output, x or y, can be None what means it should not be added to the context.

The target data y can be delayed. Although we are sending x and y at the same time, it does not mean that y is the corresponding target value of x. However, input data and target data should be in order: y_i is the target value of x_i. So the first target data sent (y_0) corresponds with the first input sent (x_0).

Parameters:

Name	Type	Description	Default
message	list	message received from the stream	required

Exceptions:

Type	Description
NotImplementedError	This is an abstract method which should be implemented.

Returns:

Type	Description
x (list)	instance of model's input data. y (list): instance of model's target data.

Source code in ADLStream/data/moving_window_generator.py

def preprocess(self, message):
    x, y = None, None
    self.x_window.append(message)
    if self.num_messages >= self.past_history + self.shift:
        self.y_window.append(message)

    if len(self.x_window) > self.past_history:
        self.x_window.pop(0)
        x = self.x_window

    if len(self.y_window) > self.forecasting_horizon:
        self.y_window.pop(0)
        y = self.y_window

    return x, y