The tiny collection of algorithms to work with time series.
-
Preprocessing and Feature Engineering tools -- tsfresh https://tsfresh.readthedocs.io/en/latest/ -- imbalance learn https://imbalanced-learn.readthedocs.io/en/stable/
-
Gradient Boosting tree model https://xgboost.readthedocs.io/en/latest/
-
LSTM Autoencoder https://blog.keras.io/building-autoencoders-in-keras.html
-
SAX-PAA and discords Pavel Senin et al. "Time series anomaly discovery with grammar-based compression." In: EDBT. 2015, pp. 481–492 (https://openproceedings.org/2015/conf/edbt/paper-155.pdf)
-
SSH and minhash [NIPS Time Series Workshop 2016] SSH (Sketch, Shingle, & Hash) for Indexing Massive-Scale Time Series. by Chen Luo, Anshumali Shrivastava (https://arxiv.org/abs/1610.07328)
-
Wighted minhash "Improved consistent sampling, weighted minhash and l1 sketching.", by Ioffe, Sergey. Data Mining (ICDM), 2010 IEEE 10th International Conference on. IEEE, 2010.
- git clone ...
- pip install -r requirements.txt
- run
jupyter notebook
- Anomaly Classification (notebooks/Classification of Anomalous TimeSeries.ipynb)
- TimeSeries Similarity (notebooks/TimeSeries similarity.ipynb)
- SAX Anomaly (notebooks/Anomaly-Sequitur.ipynb)