Task-oriented Time Series Imputation Evaluation via Generalized Representers
Authors: Zhixian Wang, Linxiao Yang, Liang Sun, Qingsong Wen, Yi Wang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate our method, we conduct experiments on six datasets: the GEF load forecasting competition dataset with the corresponding temperature [39], the UCI dataset (electricity load and air quality) [40], the Traffic dataset containing road occupancy rates2, and two transformer datasets, ETTH1 and ETTH2 [41]. |
| Researcher Affiliation | Collaboration | Zhixian Wang1,2, Linxiao Yang2, Liang Sun2, Qingsong Wen2, Yi Wang1 1The University of Hong Kong, 2DAMO Academy, Alibaba Group |
| Pseudocode | Yes | Algorithm 1: Task-oriented Imputation Emsemble. |
| Open Source Code | Yes | The corresponding code can be found in the repository https://github.com/hkuedl/Task-Oriented-Imputation. [...] We have provide the public accessible link to both the code and dataset we used for experiment. |
| Open Datasets | Yes | To validate our method, we conduct experiments on six datasets: the GEF load forecasting competition dataset with the corresponding temperature [39], the UCI dataset (electricity load and air quality) [40], the Traffic dataset containing road occupancy rates2, and two transformer datasets, ETTH1 and ETTH2 [41]. [...] We have provide the public accessible link to both the code and dataset we used for experiment. |
| Dataset Splits | Yes | Table 5: Datasets used in the forecasting task. [Provides Training, Validation, Test date ranges for each dataset] |
| Hardware Specification | Yes | We use 1 NVIDIA GTX 4090 GPU with 24GB of memory for all our experiments. |
| Software Dependencies | No | The paper mentions software like 'torch.SGD optimizer [53]' and 'Py POTS [45]', but does not specify their version numbers or the version of PyTorch. |
| Experiment Setup | Yes | In our main experiment, we set the downstream task as univariate time series forecasting, with both input sequence and prediction lengths set to 24. [...] We mainly consider 40% missing rates as our main experimental setup. [...] We use the torch.SGD optimizer [53] to optimize the parameters of the model, where the learning rate is set to 0.1. The maximum epochs for each training are 300, and the patience is set to 10. [...] The hyperparameters for each method are set as shown in Table 6. |