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 | Conference PDF | Archive PDF | Plain Text | 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 {dongyi0552, lwzhang9161}@126.com, {youcheng17, mazhe thu}@163.com |
| 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. |