Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement

• URL: http://arxiv.org/abs/2503.01875v1
• Date: Wed, 26 Feb 2025 13:47:13 GMT
• Title: Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement
• Authors: Yaxuan Kong, Yiyuan Yang, Yoontae Hwang, Wenjie Du, Stefan Zohren, Zhangyang Wang, Ming Jin, Qingsong Wen,
• Abstract summary: Time Series Multi-Task Question Answering (Time-MQA) is a unified framework that enables natural language queries across multiple time series tasks.<n>Central to Time-MQA is the TSQA dataset, a large-scale dataset containing $sim $200k question-answer pairs.
• Score: 55.2439260314328
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time series data are foundational in finance, healthcare, and energy domains. However, most existing methods and datasets remain focused on a narrow spectrum of tasks, such as forecasting or anomaly detection. To bridge this gap, we introduce Time Series Multi-Task Question Answering (Time-MQA), a unified framework that enables natural language queries across multiple time series tasks - numerical analytical tasks and open-ended question answering with reasoning. Central to Time-MQA is the TSQA dataset, a large-scale dataset containing $\sim$200k question-answer pairs derived from diverse time series spanning environment, traffic, etc. This comprehensive resource covers various time series lengths and promotes robust model development. We further demonstrate how continually pre-training large language models (Mistral 7B, Llama-3 8B, and Qwen-2.5 7B) on the TSQA dataset enhanced time series reasoning capabilities, moving beyond mere numeric tasks and enabling more advanced and intuitive interactions with temporal data. The complete TSQA dataset, models, executable codes, user study questionnaires for evaluation, and results have all been open-sourced.

Related papers

• Chat-TS: Enhancing Multi-Modal Reasoning Over Time-Series and Natural Language Data [22.274663165215237]
  Time-series analysis is critical for a wide range of fields such as healthcare, finance, transportation, and energy. Current time-series models are limited in their ability to perform reasoning that involves both time-series and their textual content. Chat-TS integrates time-series tokens into LLMs' vocabulary, enhancing its reasoning ability over both modalities.
  arXiv  Detail & Related papers  (2025-03-13T21:05:11Z)
• TimesBERT: A BERT-Style Foundation Model for Time Series Understanding [72.64824086839631]
  GPT-style models have been positioned as foundation models for time series forecasting.<n>BERT-style architecture has not been fully unlocked for time series understanding.<n>We design TimesBERT to learn generic representations of time series.<n>Our model is pre-trained on 260 billion time points across diverse domains.
  arXiv  Detail & Related papers  (2025-02-28T17:14:44Z)
• Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrative [65.84249211767921]
  Texts as Time Series (TaTS) considers the time-series-paired texts to be auxiliary variables of the time series.<n>TaTS can be plugged into any existing numerical-only time series models and enable them to handle time series data with paired texts effectively.
  arXiv  Detail & Related papers  (2025-02-13T03:43:27Z)
• ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning [10.854285913078257]
  This paper introduces ChatTS, a novel MLLM designed for time series analysis. ChatTS treats time series as a modality, similar to how vision MLLMs process images. Time Series Evol-Instruct generates diverse time series Q&As, enhancing the model's reasoning capabilities.
  arXiv  Detail & Related papers  (2024-12-04T08:06:15Z)
• Multi-Step Time Series Inference Agent for Reasoning and Automated Task Execution [19.64976935450366]
  We propose a novel task: multi-step time series inference that demands both compositional reasoning and precision of time series analysis.<n>By integrating in-context learning, self-correction, and program-aided execution, our proposed approach ensures accurate and interpretable results.
  arXiv  Detail & Related papers  (2024-10-05T06:04:19Z)
• Deep Time Series Models: A Comprehensive Survey and Benchmark [74.28364194333447]
  Time series data is of great significance in real-world scenarios. Recent years have witnessed remarkable breakthroughs in the time series community. We release Time Series Library (TSLib) as a fair benchmark of deep time series models for diverse analysis tasks.
  arXiv  Detail & Related papers  (2024-07-18T08:31:55Z)
• A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language Model [33.17908422599714]
  Large language foundation models have unveiled their capabilities for cross-task transferability, zero-shot/few-shot learning, and decision-making explainability. There are two main research lines, namely pre-training foundation models from scratch for time series and adapting large language foundation models for time series. This survey offers a 3E analytical framework for comprehensive examination of related research.
  arXiv  Detail & Related papers  (2024-05-03T03:12:55Z)
• UniTS: A Unified Multi-Task Time Series Model [31.675845788410246]
  UniTS is a unified multi-task time series model that integrates predictive and generative tasks into a single framework. UniTS is tested on 38 datasets across human activity sensors, healthcare, engineering, and finance.
  arXiv  Detail & Related papers  (2024-02-29T21:25:58Z)
• Temporal Treasure Hunt: Content-based Time Series Retrieval System for Discovering Insights [34.1973242428317]
  Time series data is ubiquitous across various domains such as finance, healthcare, and manufacturing. The ability to perform Content-based Time Series Retrieval (CTSR) is crucial for identifying unknown time series examples. We introduce a CTSR benchmark dataset that comprises time series data from a variety of domains.
  arXiv  Detail & Related papers  (2023-11-05T04:12:13Z)
• Continuous-time convolutions model of event sequences [46.3471121117337]
  Event sequences are non-uniform and sparse, making traditional models unsuitable. We propose COTIC, a method based on an efficient convolution neural network designed to handle the non-uniform occurrence of events over time. COTIC outperforms existing models in predicting the next event time and type, achieving an average rank of 1.5 compared to 3.714 for the nearest competitor.
  arXiv  Detail & Related papers  (2023-02-13T10:34:51Z)
• Instance-wise Graph-based Framework for Multivariate Time Series Forecasting [69.38716332931986]
  We propose a simple yet efficient instance-wise graph-based framework to utilize the inter-dependencies of different variables at different time stamps. The key idea of our framework is aggregating information from the historical time series of different variables to the current time series that we need to forecast.
  arXiv  Detail & Related papers  (2021-09-14T07:38:35Z)
• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks [91.65637773358347]
  We propose a general graph neural network framework designed specifically for multivariate time series data. Our approach automatically extracts the uni-directed relations among variables through a graph learning module. Our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets.
  arXiv  Detail & Related papers  (2020-05-24T04:02:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.