hamrel-cxu/EnbPI
Official code for: Conformal prediction interval for dynamic time-series (conference, ICML 21 Long Presentation) AND Conformal prediction for time-series (journal, IEEE TPAMI)
This project helps scientists, analysts, and engineers make more reliable forecasts from time-series data. It takes your existing time-series measurements (like sensor readings, stock prices, or website traffic) and, instead of just giving a single prediction, it provides a range (a prediction interval) where the future value is highly likely to fall. This helps you understand the uncertainty in your forecasts, which is critical for making informed decisions.
130 stars. No commits in the last 6 months.
Use this if you need to quantify the uncertainty in your time-series forecasts to make more robust decisions, rather than relying solely on single-point predictions.
Not ideal if your primary goal is simple point forecasting without needing to understand the confidence or potential range of future values.
Stars
130
Forks
31
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Nov 25, 2023
Commits (30d)
0
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