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..
SpecAR-Net: Spectrogram Analysis and Representation Network for Time Series
Authors: Yi Dong, Liwen Zhang, Youcheng Zhang, Shi Peng, Wen Chen, Zhe Ma
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the Spec AR-Net achieves excellent performance on 5 major downstream tasks i.e., classification, anomaly detection, imputation, longand shortterm forecasting. To verify the effectiveness and superiority of Spec AR-Net, a comprehensive set of experiments is conducted over 5 mainstream tasks |
| Researcher Affiliation | Industry | Yi Dong1 , Liwen Zhang1 , Youcheng Zhang1 , Shi Peng1 , Wen Chen1 and Zhe Ma1 1Intelligent Science & Technology Academy of CASIC, Beijing, China EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and appendix are available at https://github.com/Dongyi2go/Spec AR Net. |
| Open Datasets | Yes | The data used in this experiment is sourced from the UAE dataset (10 subsets) [Bagnall et al., 2018]... 5 widely-used datasets are employed, i.e., SMD [Su et al., 2019], MSL and SMAP [Hundman et al., 2018], SWa T [Mathur and Tippenhauer, 2016], and PSM [Abdulaal et al., 2021]... In the long-term forecasting (LF) task, a set of benchmark datasets were utilized, including ETT [Zhou et al., 2021b], Electricity, Traffic, Weather, Exchange Rate [Lai et al., 2018a] and ILI (see download links in Appendix)... M4 dataset [Makridakis et al., 2018]. |
| Dataset Splits | No | The paper describes input and prediction lengths, and masking rates, but does not specify explicit train/validation/test dataset splits (e.g., percentages or sample counts for each split). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details with version numbers (e.g., Python version, library versions like PyTorch or TensorFlow). |
| Experiment Setup | Yes | In the experiments, the input past length was set to 96, with ILL for 36. The prediction lengths is [96, 192, 336, 720], with ILI for [24, 36, 48, 60]. Random masking with masking rates of [12.5%, 25%, 37.5%, 50%] was used to simulate missing values. MSE is used as loss function... Spec AR-Net was conducted by introducing a order-preserving into the loss function... window lengths are set as [8, 16, 24] in this paper. |