VerbalTS: Generating Time Series from Texts
Authors: Shuqi Gu, Chuyue Li, Baoyu Jing, Kan Ren
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on two synthetic and four real-world datasets demonstrate that VERBALTS outperforms existing methods in both generation quality and semantic alignment with textual conditions. The project page is at https://seqml.github.io/Verbal TS/. |
| Researcher Affiliation | Academia | 1School of Information Science and Technology, Shanghai Tech University, Shanghai, China 2University of Illinois at Urbana Champaign, Illinois, United States. |
| Pseudocode | Yes | Algorithm 1 Pseudocode for the CTTP Model Input:A batch of time series and text pairs (X RB K L, C NB M) Output:Total cross-entropy loss Lcross |
| Open Source Code | Yes | We have released all the reproducible code and benchmarking datasets at https://seqml.github.io/Verbal TS/. |
| Open Datasets | Yes | Experiments on two synthetic and four real-world datasets demonstrate that VERBALTS outperforms existing methods in both generation quality and semantic alignment with textual conditions. The project page is at https://seqml.github.io/Verbal TS/. |
| Dataset Splits | Yes | We randomly split the samples into training set, validation set, and test set in a ratio of 6: 1: 1. Finally, we get 24000 training samples, 2400 validation samples, and 2400 test samples. |
| Hardware Specification | No | The authors also gratefully acknowledge further assistance provided by Shanghai Frontiers Science Center of Human-centered Artificial Intelligence, Mo E Key Lab of Intelligent Perception and Human-Machine Collaboration, and HPC Platform of Shanghai Tech University. |
| Software Dependencies | No | we used the tsfresh (Nils Braun, 2024) library to extract 6 time series features, serving as attributes for baseline input. Then, text annotations are generated from extracted features through prompt templates. Details are given in Sec. A.2.1. |
| Experiment Setup | Yes | For all experiments, we set the number of diffusion steps as T = 50, embedding size for attributes and time series as 64. For training, we use Adam optimizer to train the model, the initial learning rate is set to be 1e-4 with Multi Step LR scheduler for all datasets, the batch size is set to be 512 for Synth-M, Synth-U, Weather, ETTm1 and Traffic, 16 for Blind Ways. For the hyperparameters of the multi-focal modeling, (R, S) = (3, 3) for all datasets. All our experiments were conducted three times running with different random seeds. |