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..
FITS: Modeling Time Series with $10k$ Parameters
Authors: Zhijian Xu, Ailing Zeng, Qiang Xu
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present the results of our experiments on long-term forecasting in Tab. 1 and Tab. 2. The results for short-term forecasting on the M4 dataset are provided in the Appendix. We conduct a comprehensive case study on the performance of FITS using the ETTh2 dataset, which further highlights the impact of the lookback window and cutoff frequency on model performance. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science and Engineering, CUHK 2International Digital Economy Academy (IDEA) |
| Pseudocode | No | The paper includes pipeline diagrams (Figure 2 and Figure 4) but no explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at: https://github.com/VEWOXIC/FITS. |
| Open Datasets | Yes | All datasets used in our experiments are widely-used and publicly available real-world datasets, including, Traffic, Electricity, Weather, ETT (Zhou et al., 2021). We summarize the characteristics of these datasets in appendix. We use five commonly used benchmark datasets: SMD (Server Machine Dataset (Su et al., 2019)), PSM (Polled Server Metrics (Abdulaal et al., 2021)), SWa T (Secure Water Treatment (Mathur & Tippenhauer, 2016)), MSL (Mars Science Laboratory rover), and SMAP (Soil Moisture Active Passive satellite) (Hundman et al., 2018). |
| Dataset Splits | Yes | To avoid information leakage, We choose the hyper-parameter based on the performance of the validation set. The threshold is selected based on the highest F1 score achieved on the validation set. |
| Hardware Specification | No | The paper mentions 'edge devices' and discusses computational efficiency (MACs), but does not specify the exact hardware (e.g., GPU models, CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific version numbers for software dependencies such as programming languages, libraries, or frameworks (e.g., Python version, PyTorch/TensorFlow version). |
| Experiment Setup | Yes | We conduct grid search on the look-back window of 90, 180, 360, 720 and cutoff frequency, the only hyper-parameter. Further experiments also show that a longer look-back window can result in better performance in most cases. To avoid information leakage, We choose the hyper-parameter based on the performance of the validation set. We use a window size of 200 and downsample the time series segment by a factor of 4 as the input to train FITS to reconstruct the original segment. |