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..
Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting
Authors: Qingxiang Liu, Xu Liu, Chenghao Liu, Qingsong Wen, Yuxuan Liang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our comprehensive experiments indicate that TIME-FFM outperforms state-of-the-arts and promises effective few-shot and zero-shot forecaster. |
| Researcher Affiliation | Collaboration | Qingxiang Liu1,2 Xu Liu3 Chenghao Liu4 Qingsong Wen5 Yuxuan Liang1 1 The Hong Kong University of Science and Technology (Guangzhou) 2 Institute of Computing Technology Chinese Academy of Sciences 3 National University of Singapore 4 Salesforce AI Research 5 Squirrel AI |
| Pseudocode | Yes | The training procedure of TIME-FFM is elaborated in Algorithm 1. |
| Open Source Code | Yes | The code is available at https://github.com/City Mind Lab/Neur IPS24-Time-FFM/tree/main. |
| Open Datasets | Yes | We evaluate on 8 benchmark datasets from various domains: ETTh1, ETTh2, ETTm1, ETTm2, Electricity, Weather, Exchange, and ILI, which have been widely adopted for evaluating time series forecasting performance. Detailed introduction of implementation and datasets can be found in Appendix A. |
| Dataset Splits | Yes | Detailed descriptions of datasets. The dataset size is organized in (training, validation, test). (8545, 2881, 2881) [for ETTh1] and The round with lowest validation value serves as the optimal round, and then the corresponding model is used for test. |
| Hardware Specification | Yes | All models are implemented on PyTorch with all experiments conducted on NVIDIA A100-80G GPUs. |
| Software Dependencies | No | All models are implemented on PyTorch with all experiments conducted on NVIDIA A100-80G GPUs. The Adam optimizer with the initial learning rate of 10 4 is adopted in the training process. |
| Experiment Setup | Yes | The Adam optimizer with the initial learning rate of 10 4 is adopted in the training process. The lookback window Li is set to 36 for the ILI dataset, and 96 for the others. The future prediction window Fi is set to {24, 36, 48, 60} for the ILI dataset, and {96, 192, 336, 720} for other ones. We adopt the pretrained GPT2-backbone of the first 6 layers as the LM encoder. The local epoch E is set to 1 for all domains. The global round number T is set to 100. V , M, P and H are set to 100, 12, 16, and 8 respectively for all domains. Si is set to 4 for the ILI dataset, and 16 for other ones. |