dmbee/seglearn
Python module for machine learning time series:
This helps scientists, engineers, and analysts make sense of time-based data like sensor readings or financial ticks. It takes raw time series data and any related contextual information, then processes it to classify events, predict future values, or identify trends. This tool is ideal for anyone who needs to apply machine learning techniques to sequential data.
581 stars. No commits in the last 6 months. Available on PyPI.
Use this if you are working with time series or sequence data and need a flexible, integrated way to prepare your data and apply machine learning models for tasks like classification, regression, or forecasting.
Not ideal if your data is not sequential or time-dependent, or if you prefer to build your machine learning pipelines from scratch without using a structured framework.
Stars
581
Forks
62
Language
Python
License
BSD-3-Clause
Category
Last pushed
Aug 27, 2022
Commits (30d)
0
Dependencies
3
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/dmbee/seglearn"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
aeon-toolkit/aeon
A toolkit for time series machine learning and deep learning
sktime/sktime
A unified framework for machine learning with time series
Nixtla/neuralforecast
Scalable and user friendly neural :brain: forecasting algorithms.
tslearn-team/tslearn
The machine learning toolkit for time series analysis in Python
Nixtla/statsforecast
Lightning ⚡️ fast forecasting with statistical and econometric models.