emadeldeen24/AdaTime
[TKDD 2023] AdaTime: A Benchmarking Suite for Domain Adaptation on Time Series Data
This project helps researchers and data scientists evaluate how well different machine learning models perform when applied to new, unseen time series data that comes from a different source or environment than the data they were originally trained on. You input time series datasets and choose various domain adaptation algorithms, and it outputs systematic performance metrics to compare these methods. This is for machine learning researchers, data scientists, and academics working with time series data across varied domains.
224 stars. No commits in the last 6 months.
Use this if you need a standardized way to compare and benchmark different domain adaptation techniques for time series data, especially when dealing with shifts in data distribution.
Not ideal if you are looking for a plug-and-play solution for general time series forecasting or classification without the specific need to evaluate domain adaptation strategies.
Stars
224
Forks
25
Language
Python
License
MIT
Category
Last pushed
Jun 07, 2023
Commits (30d)
0
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