Unroll timeseries sequences based on step_size

[sarahmish]

• Orion version: 0.1.1
• Python version: 3.6

Description

At the moment, the support for timeseries prediction is unrolled as the "shrunked" version of the timeseries in case where step_size > 1.

A clear example of this is demonstrated here

Assume the original timeseries length is 10100, and window_size=100.

For step_size = 1
The input matrix is (10000, 100, 1) so y_hat is also (10000, 100, 1), then we take median of 100 numbers at every time step -> (10000, 1). The final output of primitive score_anomaly is (10000, 1) indicating the error at every time step.

For step_size = 5
The input matrix will be (2000, 100, 1) -> y_hat (2000, 100, 1) -> (2000, 1) -> the final output of primitive score_anomaly is (2000, 1). Also the index array is of shape (2000, 1). But in fact, we expect the output to be (10000, 1) also the recorded index array of the same shape (10000, 1)

Proposed Solution

Alter the score_anomaly function to take step_size as an argument and unroll the timeseries to the original dimension.