SimpleTM: A Simple Baseline for Multivariate Time Series Forecasting
Authors: Hui Chen, Viet Luong, Lopamudra Mukherjee, Vikas Singh
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we cover our experimental findings in detail. We divide our experimental protocol into two phases: evaluating the quality of forecasting both for long-term and short-term and an ablation study to evaluate the efficacy of our proposed model, Simple TM. |
| Researcher Affiliation | Academia | Hui Chen1 Viet Luong1 Lopamudra Mukherjee2 Vikas Singh1 1University of Wisconsin-Madison 2University of Wisconsin-Whitewater EMAIL EMAIL EMAIL EMAIL |
| Pseudocode | No | The paper describes the methodology in narrative text and does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at Git Hub: https://github.com/vsingh-group/SimpleTM. |
| Open Datasets | Yes | We evaluate our model on 8 widely recognized benchmarks: the ETT datasets (ETTh1, ETTh2, ETTm1, and ETTm2)... as well as the Weather, Solar-Energy, Electricity, and Traffic datasets... We adopt the PEMS dataset Chen et al. (2001) with four public traffic subsets (PEMS03, PEMS04, PEMS07, and PEMS08). |
| Dataset Splits | Yes | We mainly follow the experimental configurations in Wu et al. (2023), including the same data processing and splitting protocol... Details of the dataset are provided in Table 4. Table 4: Dataset statistics. The dimension indicates the number of channels/variates, and the dataset size is organized in (training, validation, testing). |
| Hardware Specification | Yes | All experiments were conducted using Py Torch Paszke et al. (2019) on a single NVIDIA A100 40GB GPU. |
| Software Dependencies | No | All experiments were conducted using Py Torch Paszke et al. (2019)... The paper mentions PyTorch but does not specify a version number for the software used in the experiments. |
| Experiment Setup | Yes | Table 5 summarizes the hyperparameters and training settings used in our experiments. Our hyperparameter selection followed a systematic approach, combining grid search with domain-specific considerations. The number of layers was fixed at 1, and the input length L was set to 96 for all datasets and baselines... For training parameters, we performed a grid search over learning rates within a logarithmic scale from 10-3 to 2 * 10-2. Batch sizes and training epochs were systematically evaluated within the ranges {16, 24, 256} and {10, 20}, respectively. |