--- license: mit dataset_info: features: - name: question dtype: string - name: options sequence: string - name: answer dtype: string - name: question_type dtype: string - name: tid dtype: int64 - name: difficulty dtype: string - name: format_hint dtype: string - name: relevant_concepts sequence: string - name: question_hint dtype: string - name: category dtype: string - name: subcategory dtype: string - name: id dtype: int64 - name: ts1 sequence: float64 - name: ts2 sequence: float64 splits: - name: test num_bytes: 1623762 num_examples: 763 download_size: 1278082 dataset_size: 1623762 configs: - config_name: default data_files: - split: test path: data/test-* task_categories: - question-answering language: - en tags: - Time-series - LLMs - GPT - Gemini - Phi pretty_name: timeseriesexam1 size_categories: - n<1K --- # Dataset Card for TimeSeriesExam-1 This dataset provides Question-Answer (QA) pairs for the paper [TimeSeriesExam: A Time Series Understanding Exam](https://arxiv.org/pdf/2410.14752). Example inference code can be found [here](https://github.com/moment-timeseries-foundation-model/TimeSeriesExam). ## đź“–Introduction Large Language Models (LLMs) have recently demonstrated a remarkable ability to model time series data. These capabilities can be partly explained if LLMs understand basic time series concepts. However, our knowledge of what these models understand about time series data remains relatively limited. To address this gap, we introduce TimeSeriesExam, a configurable and scalable multiple-choice question exam designed to assess LLMs across five core time series understanding categories: pattern recognition, noise understanding, similarity analysis, anomaly detection, and causality analysis.
Spider plot of performance of latest LLMs on the TimeSeriesExam Figure. 1: Accuracy of latest LLMs on the `TimeSeriesExam.` Closed-source LLMs outperform open-source ones in simple understanding tasks, but most models struggle with complex reasoning tasks.
Time series in the dataset are created from a combination of diverse baseline Time series objects. The baseline objects cover linear/non-linear signals and cyclic patterns.
time series curation pipeline Figure. 2: The pipeline enables diversity by combining different components to create numerous synthetic time series with varying properties.
## Citation If you find this work helpful, please consider citing our paper: ``` @inproceedings{caitimeseriesexam, title={TimeSeriesExam: A Time Series Understanding Exam}, author={Cai, Yifu and Choudhry, Arjun and Goswami, Mononito and Dubrawski, Artur}, booktitle={NeurIPS Workshop on Time Series in the Age of Large Models} } ``` ## Liscense MIT License Copyright (c) 2024 Auton Lab, Carnegie Mellon University Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. See [MIT LICENSE](LICENSE) for details.