waico/tsad
Package for Time Series Forecasting and Anomaly Detection Problems.
This tool helps industrial researchers and operations teams monitor equipment and processes. It takes your operational time series data (like sensor readings) and identifies unusual patterns or predicts future issues, giving you insights into potential faults or areas for improvement. Anyone responsible for maintaining industrial machinery, optimizing production lines, or ensuring product quality would benefit from this.
No commits in the last 6 months. Available on PyPI.
Use this if you need to automatically detect anomalies or forecast short-term trends in real-time sensor data from industrial equipment or production lines to prevent failures and optimize operations.
Not ideal if your primary need is general-purpose financial forecasting, marketing trend analysis, or other non-industrial time series applications.
Stars
57
Forks
10
Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
May 20, 2024
Commits (30d)
0
Dependencies
16
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/waico/tsad"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
yzhao062/pyod
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques
unit8co/darts
A python library for user-friendly forecasting and anomaly detection on time series.
elki-project/elki
ELKI Data Mining Toolkit
raphaelvallat/antropy
AntroPy: entropy and complexity of (EEG) time-series in Python
Minqi824/ADBench
Official Implement of "ADBench: Anomaly Detection Benchmark", NeurIPS 2022.