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 [1].
Non-collective Calibrating Strategy for Time Series Forecasting
Authors: Bin Wang, Yongqi Han, Minbo Ma, Tianrui Li, Junbo Zhang, Feng Hong, Yanwei Yu
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various time series benchmarks and a spatio-temporal meteorological ERA5 dataset demonstrate the effectiveness of So P, achieving up to a 22% improvement even when employing a simple MLP as the Plug (highlighted in Figure 1). |
| Researcher Affiliation | Collaboration | Bin Wang1 , Yongqi Han1 , Minbo Ma2 , Tianrui Li2 , Junbo Zhang3,4,5 , Feng Hong1 , Yanwei Yu1 1Ocean University of China 2Southwest Jiaotong University 3JD Intelligent Cities Research 4JD i City, JD Technology, China 5Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence EMAIL, EMAIL, EMAIL, EMAIL, EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 Training algorithm illustrated through variable-wise So P |
| Open Source Code | No | The paper does not explicitly provide a link to its source code or state that its code is open-sourced for the methodology described. |
| Open Datasets | Yes | Datasets. We initially conducted extensive experiments on benchmark time series datasets, including ETTh1, ETTh2, ECL, Exchange, Weather, and Solar-Energy. ... The experiments were conducted on the ERA5 dataset [Alibaba Group, 2023], which includes five meteorological variables: T2M, U10, V10, MSL and TP. |
| Dataset Splits | No | The paper mentions using benchmark datasets and the TSLib library for reproduction, but it does not explicitly state the training/validation/test splits within the main text. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions utilizing the TSLib library but does not provide specific version numbers for TSLib or any other software dependencies such as Python, PyTorch, or CUDA. |
| Experiment Setup | No | The paper describes the architecture of the Plug (e.g., 'two d-dimension hidden layers and a GELU activation') and mentions using optimizers and early-stopping monitors for each Plug. However, it does not explicitly provide concrete hyperparameter values such as specific learning rates, batch sizes, number of epochs, or detailed optimizer settings for the experiments conducted. |