Symbolic representations of time series have proved their usefulness in the field of time series motif discovery, clustering, classification, forecasting, anomaly detection, etc. Symbolic time series representation methods do not only reduce the dimensionality of time series but also speedup the downstream time series task. It has been demonstrated by [S. Elsworth and S. Güttel, Time series forecasting using LSTM networks: a symbolic approach, arXiv, 2020] that symbolic forecasting has greatly reduce the sensitivity of hyperparameter settings for Long Short Term Memory networks. How to appropriately deploy machine learning algorithm on the level of symbols instead of raw time series poses a challenge to the interest of applications. To boost the development of research community on symbolic representation, we develop this Python library to simplify the process of machine learning algorithm practice on symbolic representation. slearn library provide different API for symbols generation associated with complexity measure and machine learining traning. We will illustrate several topics in detail as below.
Install the slearn package simply by
pip install slearn
conda install -c conda-forge slearn
To check which version you install, please use:
conda list slearn
slearn currently supports SAX, ABBA, and fABBA symbolic representation, and the machine learning classifiers as below:
| Support Classifiers | Parameter call |
|---|---|
| Multi-layer Perceptron | 'MLPClassifier' |
| K-Nearest Neighbors | 'KNeighborsClassifier' |
| Gaussian Naive Bayes | 'GaussianNB' |
| Decision Tree | 'DecisionTreeClassifier' |
| Support Vector Classification | 'SVC' |
| Radial-basis Function Kernel | 'RBF' |
| Logistic Regression | 'LogisticRegression' |
| Quadratic Discriminant Analysis | 'QuadraticDiscriminantAnalysis' |
| AdaBoost classifier | 'AdaBoostClassifier' |
| Random Forest | 'RandomForestClassifier' |
Our documentation is available.
This slearn implementation is maintained by Roberto Cahuantzi (University of Manchester), Xinye Chen (Charles University Prague), and Stefan Güttel (University of Manchester). If you use the function of LZWStringLibrary in your research, or if you find slearn useful in your work, please consider citing the paper below. If you have any problems or questions, just drop us an email.
@InProceedings{10.1007/978-3-031-37963-5_53,
author="Cahuantzi, Roberto
and Chen, Xinye
and G{\"u}ttel, Stefan",
title="A Comparison of LSTM and GRU Networks for Learning Symbolic Sequences",
booktitle="Intelligent Computing",
year="2023",
publisher="Springer Nature Switzerland",
pages="771--785"
}This project is licensed under the terms of the MIT license.
Contributing to this repo is welcome! We will work through all the pull requests and try to merge into main branch.