LingFengGold/TimeDistill
[KDD 2026] Official implementation of "TimeDistill: Efficient Long-Term Time Series Forecasting with MLPs via Cross-Architecture Distillation"
This tool helps data scientists and machine learning engineers make accurate long-term predictions from time series data using less computational power. It takes historical time series datasets and a 'teacher' model (like a Transformer or CNN), then outputs a more efficient, lightweight prediction model (an MLP) that performs as well as, or better than, the complex teacher. This is ideal for those managing predictive analytics in resource-constrained environments.
Use this if you need to forecast long-term trends in time series data with high accuracy but are limited by computational resources or model size.
Not ideal if you are working with short-term forecasts or if you have ample computational resources and prefer to directly deploy large, complex models.
Stars
15
Forks
3
Language
Python
License
Apache-2.0
Category
Last pushed
Nov 27, 2025
Commits (30d)
0
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