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license: mit
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# BEDTime: Benchmark for Evaluating Descriptions of Time Series in Multi-Modal Language Models

**Dataset Summary**
BEDTime is a unified benchmark for evaluating large language and vision-language models on the task of describing time series data. While there are few existing datasets specifically created for time series-to-text generation, many datasets with paired time series and natural language descriptions can be reformulated to fit this purpose. BEDTime does exactly that — harmonizing four existing datasets into a cohesive benchmark.

BEDTime contains a total of **10,175 time series** with corresponding natural language descriptions drawn from four sources:

1. **TRUCE-Stock**:

   * Real-world stock data collected via the Google Finance API
   * 5,687 time series, each 12 time steps long
   * 3 human-written crowd-sourced descriptions per series
   * Descriptions (≤ 9 words) focus on **trends, volatility, and relative change**

2. **TRUCE-Synthetic**:

   * 1,677 synthetic time series
   * Each constructed to exhibit **exactly one** of six predefined patterns (e.g., increase-in-beginning)
   * 3 crowd-sourced descriptions per series (2–8 words)
   * Small noise added for realism

3. **SUSHI-Tiny**:

   * 1,400 synthetic time series
   * Generated with combinations of **trends, periodicities**, and **fluctuation types**
   * Descriptions are **synthetically generated** based on known generation properties

4. **TaxoSynth**:

   * 1,400 synthetic time series
   * Lengths range from 24 to 150 time steps
   * Annotated using **attribute-based taxonomy** and **template-based text generation**
   * Descriptions are 5 to 40 words long

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**Supported Tasks and Leaderboards**

* **Time Series Captioning** (open-generation)
* **Time Series Understanding** (true/false(recognition), multiple choice(differentiation))

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**Languages**

* English

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**Dataset Structure**
Each entry includes:

* A time series \( x<sub>i</sub> \)
* One or more paired natural language descriptions \( d<sub>i</sub> \)