yyalau/iclr2025_dsof
This is the official repository for the ICLR 2025 Conference Paper - Fast and Slow Streams for Online Time Series Forecasting without Information Leakage.
This project offers an advanced method for predicting future values in time series data, specifically designed for real-time situations where data arrives continuously. It takes historical time series observations (like electricity consumption or traffic flow) and produces accurate forecasts for multiple steps into the future. This tool is for researchers and practitioners working on developing and evaluating state-of-the-art online forecasting models.
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Use this if you need to perform online time series forecasting where predictions are continuously updated as new data arrives, and you want to avoid common issues like data leakage and overfitting to recent noise.
Not ideal if you are looking for a simple, out-of-the-box forecasting solution without needing to engage with advanced model configuration or academic research settings.
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Language
Python
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Last pushed
Apr 30, 2025
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