thuml/Koopa
Code release for "Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors" (NeurIPS 2023), https://arxiv.org/abs/2305.18803
This project offers a fast and efficient way to predict future values in real-world data streams that constantly change, like stock prices or weather patterns. It takes historical numerical data, processes it quickly, and outputs accurate forecasts. This is designed for data scientists, financial analysts, or operations engineers who need to predict future trends with less computational overhead.
240 stars. No commits in the last 6 months.
Use this if you need to make accurate predictions on time series data that is constantly evolving and you want to save significant time and computing resources compared to traditional deep learning models.
Not ideal if your time series data is perfectly stable and predictable, or if you prefer models with extensive interpretability features over pure predictive performance and efficiency.
Stars
240
Forks
28
Language
Python
License
MIT
Category
Last pushed
Jul 01, 2024
Commits (30d)
0
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