Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion
Authors: Ling Yang, Shenda Hong
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Specifically, we firstly conducts downstream evaluations on three major tasks for time series including classification, forecasting and anomaly detection. Experimental results shows that our BTSF consistently significantly outperforms the state-of-the-art methods. |
| Researcher Affiliation | Academia | 1National Institute of Health Data Science, Peking University, Beijing, China 2Institute of Medical Technology, Health Science Center of Peking University, Beijing, China. |
| Pseudocode | No | The paper describes the method and its components mathematically and textually but does not include a formally labeled pseudocode block or algorithm. |
| Open Source Code | No | The paper does not provide a link or an explicit statement about the availability of its source code. It only mentions implementing existing works using public codes. |
| Open Datasets | Yes | We evaluate our learned representation on downstream classification tasks for time series on widely-used time series classification datasets (Anguita et al., 2013; Goldberger et al., 2000; Andrzejak et al., 2001; Moody, 1983). |
| Dataset Splits | Yes | In the training stage, we keep the original train/test splits of datasets and use the training set to train all the models. |
| Hardware Specification | Yes | In all experiments, we use Pytorch 1.8.1 (Paszke et al., 2017) and train all the models on a Ge Force RTX 2080 Ti GPU with CUDA 10.2. |
| Software Dependencies | Yes | In all experiments, we use Pytorch 1.8.1 (Paszke et al., 2017) and train all the models on a Ge Force RTX 2080 Ti GPU with CUDA 10.2. |
| Experiment Setup | Yes | We apply an Adam optimizer (Kingma & Ba, 2017) with a learning rate of 3e-4, weight decay of 1e-4 and batch size is set to 256. ... the dropout rate, temperature number τ and the loops number of iterative bilinear fusion. Table 6 illustrates that when the rate is set to 0.1 ... Table 7 demonstrates that when τ is set to 0.05 |