Zhining Liu, Jingrui He, Hanghang Tong, Yada Zhu, Tianxin Wei, Ruizhong Qiu, Ze Yang, Xiao Lin, Hendrik Hamann
We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| zhiningliu1998/timefuse | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| ModelFusor | Ran | zhiningliu1998/timefuse/timefuse.py code served (permissive licence) · get_code("281a2c7c65eb8032") |
| get_datasets_best_single_perf | Ran | zhiningliu1998/timefuse/timefuse.py code served (permissive licence) · get_code("dfd48d95a2ec6fa9") |
| get_length_aligned_loaders | Ran | zhiningliu1998/timefuse/timefuse.py code served (permissive licence) · get_code("d515367e52699f83") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Time-series forecasting plays a critical role in many real-world applications. Although increasingly powerful models have been developed and achieved superior results on benchmark datasets, through a fine-grained sample-level inspection, we find that (i) no single model consistently outperforms others across different test samples, but instead (ii) each model excels in specific cases. These findings prompt us to explore how to adaptively leverage the distinct strengths of various forecasting models for different samples. We introduce TIMEFUSE, a framework for collective time-series forecasting with sample-level adaptive fusion of heterogeneous models. TIMEFUSE utilizes meta-features to characterize input time series and trains a learnable fusor to predict optimal model fusion weights for any given input. The fusor can leverage samples from diverse datasets for joint training, allowing it to adapt to a wide variety of temporal patterns and thus generalize to new inputs, even from unseen datasets. Extensive experiments demonstrate the effectiveness of TIMEFUSE in various long-/short-term forecasting tasks, achieving near-universal improvement over the state-of-the-art individual models. Code is available at https://github.com/ ZhiningLiu1998/TimeFuse.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2505.18442")
get_code_for_paper("2505.18442")
have("2505.18442")
Connect an agent — have() is free.