In-context Time Series Predictor
Authors: Jiecheng Lu, Yan Sun, Shihao Yang
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct comprehensive experiments under full-data, few-shot, and zero-shot settings using widely-used TSF datasets (details in A.2), including ETTs (Zhou et al., 2021), Traffic, Electricity (ECL), and Weather. We use K = 3 TF layers with d = 128 and 8 heads. We set LI = 1440, Lb = 512, and LP {96, 192, 336, 720}, performing 4 experiments for each dataset. |
| Researcher Affiliation | Academia | Jiecheng Lu, Yan Sun, Shihao Yang Georgia Institute of Technology EMAIL, EMAIL |
| Pseudocode | No | The paper describes the model architecture and processes using mathematical formulations (e.g., Equation 1 for Transformer layers, Equations 6-10 for Token Retrieval) but does not contain a dedicated section or figure presenting pseudocode or an algorithm block. |
| Open Source Code | Yes | Code implementation is available at: https://anonymous.4open.science/r/ICTSP-C995 |
| Open Datasets | Yes | Our main TSF experiments are conducted based on commonly used time series forecasting datasets, detailed as follows: ETT Datasets2 (Zhou et al., 2021): This dataset includes...2https://github.com/zhouhaoyi/ETDataset. Electricity Dataset3: This dataset covers...3https://archive.ics.uci.edu/ml/datasets/Electricity Load Diagrams20112014. Traffic Dataset4: Sourced from...4http://pems.dot.ca.gov/. Weather Dataset5: This dataset captures...5https://www.bgc-jena.mpg.de/wetter/. |
| Dataset Splits | Yes | In the full-data experiment setting, we split each dataset with 70% training set, 10% validation, set and 20% test set. |
| Hardware Specification | Yes | Our models are trained on single Nvidia RTX 4090 GPU with a batch size equals to 32 for most of the datasets. |
| Software Dependencies | No | The ICTSP model is trained using the Adam optimizer and MSE loss in Pytorch, with a learning rate of 0.0005 each dataset. The paper mentions using Pytorch but does not provide specific version numbers for Pytorch or any other software dependencies. |
| Experiment Setup | Yes | We use K = 3 TF layers with d = 128 and 8 heads. We set LI = 1440, Lb = 512, and LP {96, 192, 336, 720}, performing 4 experiments for each dataset. We use sampling step m = 8 and the token retrieval method with q = 10%, r = 30 in main experiments. The ICTSP model is trained using the Adam optimizer and MSE loss in Pytorch, with a learning rate of 0.0005 each dataset. We test the model every 200 training steps with a early-stopping patience being 30 * 200 steps. The first 1000 steps are for learning rate warm-up, followed by a linear decay of learning rate. We set the random seed as 2024. |