yfzhang114/OneNet
This is an official PyTorch implementation of the NeurIPS 2023 paper 《OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling》
This project helps operations managers, financial analysts, or anyone forecasting critical time series data that changes unpredictably over time. It takes streaming time series data as input and produces more accurate, stable predictions by automatically adapting to these shifts. The output is a robust forecast even when underlying patterns change.
126 stars. No commits in the last 6 months.
Use this if you need highly accurate, continuously updated forecasts for time series data that frequently experiences 'concept drift' – where the underlying patterns change over time.
Not ideal if your time series data patterns are stable and don't typically change, as simpler forecasting methods might suffice.
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126
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16
Language
Python
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Last pushed
Nov 27, 2024
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