Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Task-oriented Time Series Imputation Evaluation via Generalized Representers
Authors: Zhixian Wang, Linxiao Yang, Liang Sun, Qingsong Wen, Yi Wang
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 32,397 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,682 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |