Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Lightweight Sparse Interaction Network for Time Series Forecasting
Authors: Xu Zhang, Qitong Wang, Peng Wang, Wei Wang
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on public datasets show that LSINet achieves both higher accuracy and better efficiency than advanced linear models and transformer models in TSF tasks. |
| Researcher Affiliation | Academia | 1Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China 2Universite Paris Cite, Paris, France EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the architecture and mechanisms in detail through text and diagrams (Figure 3), but it does not include any explicitly labeled pseudocode or algorithm blocks with structured steps. |
| Open Source Code | No | The paper does not provide any concrete statement about releasing source code for the methodology described, nor does it include any links to a code repository. |
| Open Datasets | Yes | We evaluate the performance of the proposed LSINet on 6 popular datasets, including Weather, Electricity, and 4 ETT datasets, covering a range of time steps (17420 to 69680) and variables (7 to 321) and have been widely employed in the literature for multivariate forecasting tasks (Nie et al. 2023; Wu et al. 2021; Zhou et al. 2022b). |
| Dataset Splits | Yes | All methods follow the same data loading parameters (e.g., train/val/test split ratio) as in (Nie et al. 2023). |
| Hardware Specification | Yes | Experiments are conducted on NVIDIA Ge Force RTX 3090 GPU on Py Torch. |
| Software Dependencies | No | The paper mentions 'Py Torch' as the framework used, but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | For LSINet, the hidden size for patch embedding, position embedding (Eq. 2), and all used MLPs are fixed at 128. The multi-head h is fixed at 4. η {1, 3} is used for controlling the interval of using sparse regularization loss. The number of patch e N is fixed at 64 for sparse interaction learning. δ for controlling sparsity is fixed at 0.15, i.e., the sparse rate of C is 0.85. The number of stacked STI modules is fixed at 1 on all datasets. ... The learning rate is fixed at 1e-4. The batch size for 4 ETT datasets is fixed at 128 while for Weather and Electricity datasets are fixed at 64 and 32 respectively. All methods follow the same data loading parameters (e.g., train/val/test split ratio) as in (Nie et al. 2023). For each experiment, we independently ran 5 times with 5 different seeds for 30 epochs and reported the average metrics and standard deviations. |