ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning

• URL: http://arxiv.org/abs/2412.03104v2
• Date: Wed, 01 Jan 2025 07:23:17 GMT
• Title: ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning
• Authors: Zhe Xie, Zeyan Li, Xiao He, Longlong Xu, Xidao Wen, Tieying Zhang, Jianjun Chen, Rui Shi, Dan Pei,
• Abstract summary: This paper introduces ChatTS, a novel MLLM designed for time series analysis.<n>ChatTS treats time series as a modality, similar to how vision MLLMs process images.<n>Time Series Evol-Instruct generates diverse time series Q&As, enhancing the model's reasoning capabilities.
• Score: 10.854285913078257
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding time series is crucial for its application in real-world scenarios. Recently, large language models (LLMs) have been increasingly applied to time series tasks, leveraging their strong language capabilities to enhance various applications. However, research on multimodal LLMs (MLLMs) for time series understanding and reasoning remains limited, primarily due to the scarcity of high-quality datasets that align time series with textual information. 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, enabling it to perform both understanding and reasoning with time series. To address the scarcity of training data, we propose an attribute-based method for generating synthetic time series with detailed attribute descriptions. We further introduce Time Series Evol-Instruct, a novel approach that generates diverse time series Q&As, enhancing the model's reasoning capabilities. To the best of our knowledge, ChatTS is the first TS-MLLM that takes multivariate time series as input for understanding and reasoning, which is fine-tuned exclusively on synthetic datasets. We evaluate its performance using benchmark datasets with real-world data, including six alignment tasks and four reasoning tasks. Our results show that ChatTS significantly outperforms existing vision-based MLLMs (e.g., GPT-4o) and text/agent-based LLMs, achieving a 46.0% improvement in alignment tasks and a 25.8% improvement in reasoning tasks.

Related papers

• LLM-PS: Empowering Large Language Models for Time Series Forecasting with Temporal Patterns and Semantics [56.99021951927683]
  Time Series Forecasting (TSF) is critical in many real-world domains like financial planning and health monitoring. Existing Large Language Models (LLMs) usually perform suboptimally because they neglect the inherent characteristics of time series data. We propose LLM-PS to empower the LLM for TSF by learning the fundamental textitPatterns and meaningful textitSemantics from time series data.
  arXiv  Detail & Related papers  (2025-03-12T11:45:11Z)
• A Time Series Multitask Framework Integrating a Large Language Model, Pre-Trained Time Series Model, and Knowledge Graph [1.3654846342364308]
  Time series analysis is crucial in fields like finance, transportation, and industry. This paper proposes a novel time series multitask framework, called LTM, which integrates temporal features with textual descriptions. Experiments on benchmark datasets show that LTM significantly outperforms existing methods.
  arXiv  Detail & Related papers  (2025-03-10T11:25:01Z)
• Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement [55.2439260314328]
  Time Series Multi-Task Question Answering (Time-MQA) is a unified framework that enables natural language queries across multiple time series tasks. Central to Time-MQA is the TSQA dataset, a large-scale dataset containing $sim $200k question-answer pairs.
  arXiv  Detail & Related papers  (2025-02-26T13:47:13Z)
• 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. 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)
• Position: Empowering Time Series Reasoning with Multimodal LLMs [49.73647759532127]
  We argue that multimodal language models (MLLMs) can enable more powerful and flexible reasoning for time series analysis. We call on researchers and practitioners to leverage this potential by developing strategies that prioritize trust, interpretability, and robust reasoning in MLLMs.
  arXiv  Detail & Related papers  (2025-02-03T16:10:48Z)
• Time Series Language Model for Descriptive Caption Generation [11.796431549951055]
  We introduce TSLM, a novel time series language model designed specifically for time series captioning. TSLM operates as an encoder-decoder model, leveraging both text prompts and time series data representations. We show that TSLM outperforms existing state-of-the-art approaches from multiple data modalities by a significant margin.
  arXiv  Detail & Related papers  (2025-01-03T14:34:30Z)
• TableTime: Reformulating Time Series Classification as Zero-Shot Table Understanding via Large Language Models [54.44272772296578]
  Large language models (LLMs) have demonstrated their effectiveness in multivariate time series classification. LLMs directly encode embeddings for time series within the latent space of LLMs from scratch to align with semantic space of LLMs. We propose TableTime, which reformulates MTSC as a table understanding task.
  arXiv  Detail & Related papers  (2024-11-24T07:02:32Z)
• AutoTimes: Autoregressive Time Series Forecasters via Large Language Models [67.83502953961505]
  AutoTimes projects time series into the embedding space of language tokens and autoregressively generates future predictions with arbitrary lengths. We formulate time series as prompts, extending the context for prediction beyond the lookback window. AutoTimes achieves state-of-the-art with 0.1% trainable parameters and over $5times$ training/inference speedup.
  arXiv  Detail & Related papers  (2024-02-04T06:59:21Z)
• Large Language Models for Time Series: A Survey [34.24258745427964]
  Large Language Models (LLMs) have seen significant use in domains such as natural language processing and computer vision. LLMs present a significant potential for analysis of time series data, benefiting domains such as climate, IoT, healthcare, traffic, audio and finance.
  arXiv  Detail & Related papers  (2024-02-02T07:24:35Z)
• Time-LLM: Time Series Forecasting by Reprogramming Large Language Models [110.20279343734548]
  Time series forecasting holds significant importance in many real-world dynamic systems. We present Time-LLM, a reprogramming framework to repurpose large language models for time series forecasting. Time-LLM is a powerful time series learner that outperforms state-of-the-art, specialized forecasting models.
  arXiv  Detail & Related papers  (2023-10-03T01:31:25Z)
• LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters [12.887118862534331]
  We propose a framework for time-series forecasting with pre-trained Large Language Models (LLMs) LLM4TS consists of a two-stage fine-tuning strategy to align LLMs with the nuances of time-series data, and the textitforecasting fine-tuning stage for downstream time-series forecasting tasks. Our framework features a novel two-level aggregation method that integrates multi-scale temporal data within pre-trained LLMs, enhancing their ability to interpret time-specific information.
  arXiv  Detail & Related papers  (2023-08-16T16:19:50Z)

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.