torsk

Torsk

Citekey	HeimAvery2019Adaptive
Source Code	https://github.com/nmheim/torsk
Learning type	unsupervised
Input dimensionality	multivariate

Dependencies

• python 3
• joblib
• numpy
• pandas
• matplotlib
• scipy
• scikit-learn

Notes

• Returns window anomaly scores. We can transform them back to point scores using post-processing:

from timeeval.utils.window import ReverseWindowing
# post-processing for Torsk
def _post_torsk(scores: np.ndarray, args: dict) -> np.ndarray:
    pred_size = args.get("hyper_params", {}).get("prediction_window_size", 20)
    context_window_size = args.get("hyper_params", {}).get("context_window_size", 10)
    size = pred_size * context_window_size + 1
    return ReverseWindowing(window_size=size).fit_transform(scores)

• We don't use the input mapping feature to its full potential. Only a single random weight mapping is used. The parameters of this mapping are exposed.

• The pytorch implementation had various errors, so we disabled this backend and removed the code for it.

• Information about the different window sizes and why the actual window size is one larger than train_size + pred_size:

  dataset_size = 40
  train_size = 20
  transient_size = 10
  pred_size  = 10
  steps = dataset_size - train_size - 1 - pred_size = 9
  
  ------------------------------------------
  |          dataset size = 40             |
  ------------------------------------------
  
  For training:
  ++++++++++++++++++++++++++++++++++
  |         window size = 31       |
  |       train 21      |  pred 10 |
  |     train_x 20     |    -      |
  ||      train_y 20    |    -     |
  ++++++++++++++++++++++++++++++++++
  
  For optimization:
  ++++++++++++++++++++++++++++++++++
  |         window size = 31       |
  |  tran 10 | opt data |     -    |
  ++++++++++++++++++++++++++++++++++
  
  For prediction:
  ++++++++++++++++++++++++++++++++++
  |         window size = 31       |
  |          -          |  pred 10 |
  ++++++++++++++++++++++++++++++++++
  

• Parameter values are very dependent on a dataset. The default parameters are just rough estimates.