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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
General Incomplete Time Series Analysis via Patch Dropping Without Imputation
Authors: Yangyang Wu, Yi Yuan, Mengying Zhu, Xiaoye Miao, Meng Xi
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on 11 public real-world time series datasets demonstrate that INTER improves accuracy by over 20% compared to state-of-the-art methods, while maintaining competitive computational efficiency. 5 Experiment In this section, we evaluate the performance of our proposed model INTER on five tasks long-term forecasting, short-term forecasting, imputation, classification, and anomaly detection using 11 (in)complete multivariate time series datasets. |
| Researcher Affiliation | Academia | Yangyang Wu1,4 , Yi Yuan1 , Mengyin Zhu1 , Xiaoye Miao2,3 and Meng Xi1,4 1School of Software Technology, Zhejiang University 2Center for Data Science, Zhejiang University 3The State Key Lab of Brain-Machine Intelligence, Zhejiang University 4Binjiang Institute of Zhejiang University EMAIL |
| Pseudocode | Yes | The pseudo-code of INTER can be found in Appendix B. |
| Open Source Code | No | The paper does not explicitly state that the source code for the methodology is openly available, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | Extensive experiments on 11 public real-world time series datasets demonstrate that INTER improves accuracy by over 20% compared to state-of-the-art methods, while maintaining competitive computational efficiency. For the long-term setting, we use four widely-used public multivariate time series datasets: Electricity [Gasparin et al., 2022], Weather [Zhou et al., 2021], Exchange [Zhang and Berardi, 2001], and Illness [Zhou et al., 2021], covering four real-world scenarios. For the short-term setting, we adopt the M4 [Makridakis, 2018] dataset and its representative subsets, including yearly and monthly collected univariate marketing data. We use three public real-world time series datasets: two representative multivariate datasets from the UEA Time Series Classification Archive [Bagnall et al., 2018] (i.e., Heartbeat and Japanese Vowels), and one public medical dataset (i.e., Physionet) [Goldberger et al., 2000]. |
| Dataset Splits | No | The paper mentions that for forecasting models, "we randomly remove 50% of the observed values prior to model training." It also refers to using established datasets like M4 and UEA, and preprocessing "following the descriptions in [Zerveas et al., 2021]". However, it does not explicitly provide specific percentages, sample counts, or direct methodologies for the standard training, validation, and test splits within the main text for reproduction. |
| Hardware Specification | Yes | The experiments were conducted on a server with an Intel Core 2.80GHz processor, 3 NVIDIA A40 GPUs, and 192GB RAM, running Ubuntu 18.04. |
| Software Dependencies | No | The paper states: "All approaches were implemented in Python." but does not specify a Python version or any other software libraries with version numbers. |
| Experiment Setup | Yes | To evaluate the effectiveness of forecasting models on incomplete multivariate time series datasets, we randomly remove 50% of the observed values prior to model training. Each metric value is obtained by averaging the results of five experimental runs on each dataset. |