Skip to main content

A package linking symbolic representation with sklearn for time series prediction

Project description

Build Status PyPI version PyPI pyversions PyPI pyversions Documentation Status

A package linking symbolic representation with scikit-learn machine learning for time series prediction.

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.

Install

Install the slearn package simply by

pip install slearn

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

slearn-0.2.3.tar.gz (15.5 kB view hashes)

Uploaded Source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page