Restricted Access!!
| Citekey | HyndmanAthanasopoulos2018Forecasting |
| Source Code | https://github.com/johnpaparrizos/AnomalyDetection/tree/master/code/ptsa |
| Learning type | unsupervised |
| Input dimensionality | univariate |
After receiving the original source code from the authors, place the directory ptsa into this folder.
The ptsa algorithms require sklearn in version 19 to 23. This is checked in the utility.py. Our python image, however, uses a newer sklearn version, which is 24.1 or higher. Hence we removed the check:
if int(sklearn_version.split(".")[1]) < 19: #or int(sklearn_version.split(".")[1]) > 23:
raise ValueError("Sklearn version error")The ARIMA algorithm performs a fitted check, but that check cannot find the called function in sklearn 20 and higher - the function signature has probably changed. Because the function does not add any computation logic and we do call the fitting in algorithm.py, we removed the checking line from arima.py:
#check_is_fitted(self, ['decision_scores_', 'threshold_', 'labels_'])The sklearn MinMaxScaler has probably moved to a new location in sklearn version 24 or higher. Therefore, we need to import that location in arima.py as well in order to find the MinMaxScaler:
from sklearn.preprocessing import MinMaxScalerR. Hyndman, Y. Khandakar. Automatic Time Series Forecasting: The forecast Package for R. Journal of Statistical Software, 27(3), 1 - 22, 2008.
See also: https://otexts.com/fpp2/arima.html https://research.monash.edu/en/publications/forecasting-principles-and-practice-2