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Position: What Can Large Language Models Tell Us about Time Series Analysis
Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang, Shirui Pan, Qingsong Wen
TL;DR
Time-series models often depend on domain knowledge and extensive tuning, and AGI with time-series capabilities remains nascent. This paper reviews LLM-centered approaches and argues that LLMs can advance general-purpose time-series analysis. It calls for efficient, accountable, and adaptable enhancers while emphasizing trust and the need to address hallucination.
Problem
Existing time-series models rely heavily on domain knowledge and extensive tuning, while AGI with time-series capabilities remains nascent.
Source: §1 · p. 1Method
Results
Takeaways & Limitations
Future enhancer work should pursue efficient, accountable, universally adaptable plug-and-play solutions for practical challenges such as sparsity and noise.
Source: §3.3 · p. 4Addressing hallucinations through reliable prompts or instruction-data fine-tuning requires substantial human effort, creating scalability and efficiency challenges.
Source: §5.3 · p. 8
Abstract
from arXiv · showhide
Time series analysis is essential for comprehending the complexities inherent in various realworld systems and applications. Although large language models (LLMs) have recently made significant strides, the development of artificial general intelligence (AGI) equipped with time series analysis capabilities remains in its nascent phase. Most existing time series models heavily rely on domain knowledge and extensive model tuning, predominantly focusing on prediction tasks. In this paper, we argue that current LLMs have the potential to revolutionize time series analysis, thereby promoting efficient decision-making and advancing towards a more universal form of time series analytical intelligence. Such advancement could unlock a wide range of possibilities, including time series modality switching and question answering. We encourage researchers and practitioners to recognize the potential of LLMs in advancing time series analysis and emphasize the need for trust in these related efforts. Furthermore, we detail the seamless integration of time series analysis with existing LLM technologies and outline promising avenues for future research.
1. Introduction
Current time-series models are task-specific and depend on domain knowledge and extensive tuning, while AGI with time-series capabilities remains an open gap. The paper positions LLMs as a hub for more general analysis and reviews its potential roles and future directions.
1. Introduction
- A gap remains between task-specific time-series research and AGI with time-series capabilities, while existing models rely heavily on domain knowledge and tuning.The paper notes that effective updates and validations are not assured for conventional models.Source: §1 · pp. 1–2
- The proposed integration pairs time-series and textual context with LLM knowledge and reasoning, alongside pretrained time-series models’ pattern recognition.The authors present this combination as a potential general-purpose, unified system.Source: §1 · p. 2
- The paper reviews preliminary work, outlines integration forms, and identifies research opportunities and challenges.Its stated contributions include a systematic categorization and a roadmap of areas current research has not yet addressed.Source: §1 · p. 2
- LLMs are framed as data and model enhancers, predictors, and next-generation agents for time-series analysis.The paper distinguishes augmentation, prediction, and active engagement in analysis as three roles.Source: Figure 2 · p. 2, §1 · p. 2
2. Background
The background defines time-series data and its analytical tasks, describes LLM modeling and emergent abilities, and frames LLM-centric models as a fourth generation extending analysis beyond prediction.
2.1. Time Series Analysis
- Time series may be regularly or irregularly sampled and univariate or multivariate; common tasks include forecasting, classification, anomaly detection, and imputation.Modality switching and question answering are also emerging tasks.Source: §2.1 · pp. 2–3
2.3. Research Roadmap
- The roadmap moves from statistical models to deep networks and pretrained models, then to LLM-centric models that process language instructions and time series.The proposed fourth generation extends analysis toward question answering, interpretable predictions, and complex reasoning beyond conventional predictive analytics.Source: Figure 3 · p. 3, §2.3 · pp. 3–4
2.2. Large Language Models
- Autoregressive language models predict the next token from preceding context, modeling the probability of a word sequence.The paper describes this as an autoregressive language-generation objective.Source: §2.2 · p. 3
- LLMs’ emergent abilities include in-context learning, instruction following, and step-by-step reasoning for complex tasks.In-context learning uses instructions and examples without additional training.Source: §2.2 · p. 3
3. LLM-assisted Enhancer for Time Series
LLM-assisted enhancers use language-model knowledge and reasoning to improve time-series data understanding and existing models. The paper reviews data- and model-based approaches while noting scale and universality challenges.
3. LLM-assisted Enhancer for Time Series
- LLM-assisted enhancers aim to use LLM knowledge and reasoning to improve time-series understanding and existing model performance.The paper distinguishes data-based from model-based enhancement.Source: §3 · p. 4
3.1. Data-based Enhancer
- Data-based enhancers provide textual descriptions and summaries of patterns and anomalies, or enrich time-series context with diverse data sources.The cited applications include mobility, biological signals, trend mining, sensor analysis, and financial decision-making.Source: §3.1 · p. 4
3.2. Model-based Enhancer
- Model-based enhancers transfer LLM knowledge to domain models, using approaches such as dual-tower models, contrastive alignment, and prompting.Examples include ECG analysis, aligning text and video with sensor data, and traffic decision analysis.Source: §3.2 · p. 4
3.3. Discussion
- Enhancers may address sparsity and noise and provide plug-and-play assistance, but large-scale use has significant time and cost overheads.Application diversity also complicates building universally effective enhancers; the paper calls for efficient, accountable, universally adaptable solutions.Source: §3.3 · p. 4
4. LLM-centered Predictor for Time Series
LLM-centered predictors use LLM knowledge for time-series tasks, with methods divided by whether model parameters are accessible. The section compares tuning-based and non-tuning-based predictors, notes from-scratch foundation models, and outlines trade-offs that remain.
4.1. Tuning-based Predictor
- Tuning-based predictors patch and tokenize time series, optionally tokenize related text, then use accessible LLM parameters and a task layer for analysis.Some approaches partially unfreeze parameters or add lightweight adaptation layers.Source: §4.1 · p. 5
4.2. Non-tuning-based Predictor
- Non-tuning-based predictors preprocess time series to fit LLM input spaces, obtain responses from the model, and parse prediction labels.This approach is suitable for closed-source models.Source: §4.2 · p. 5
4.3. Others
- Another approach builds generic or domain-specific time-series foundation models from scratch, aiming to emulate LLM scaling laws.The paper describes this branch as peripheral to its primary positions.Source: §4.3 · pp. 5–6
4.4. Discussion
- Tuning-based predictors can improve performance and domain adaptability but incur training costs and risk catastrophic forgetting; non-tuning methods rely on manual prompts and may be unstable.The paper argues that further refinement is needed and suggests future advances could reduce tuning costs and improve prediction stability and reliability.Source: §4.4 · p. 6
5. LLM-empowered Agent for Time Series
The paper demonstrates both the promise and limits of LLMs as time-series agents, then proposes ways to integrate time-series knowledge and improve agent reliability. It also identifies open challenges involving hallucination, human preferences, and changing data.
5. LLM-empowered Agent for Time Series
- Prompted LLMs show potential as time-series agents, but struggle with intricate patterns and can produce hallucinatory outputs.The paper proposes developing more robust and reliable general-purpose single-agent and multi-agent systems.Source: §5 · p. 6
5.1. Empirical Insights: LLMs as Time Series Analysts
- In zero-shot HAR classification, all Stand instances were correctly classified, while Lay instances were repeatedly misclassified as Sit or Stand.The evaluation used ten instances per class across Stand, Sit, Lay, and Walk.Source: §5.1 · pp. 6–7
- The agent presents its classification reasoning in natural language, but may refuse complex queries when it lacks detailed information about the classification algorithm.The paper also reports plausible-sounding but fabricated explanations for Lay misclassifications.Source: §5.1 · pp. 6–7
5.2. Key Lessons for Advancing Time Series Agents
- The authors propose integrating time-series knowledge through feature-to-language alignment, fusion of text embeddings with time-series features, or LLM orchestration of external tools.The proposed tool-use route keeps time-series knowledge in an external model hub, with the LLM selecting and coordinating tools in response to user queries.Source: §5.2 · p. 7
- Future research should harness LLMs’ zero-shot capabilities for general pattern manipulation and enable user-facing problem solving through in-context learning.The authors distinguish this direction from repurposing or fine-tuning models for specific tasks.Source: §5.2 · p. 7
5.3. Exploring Alternative Research Avenues
- Reliable time-series agents need better instruction guidance, since prompt and instruction-dataset approaches require substantial human effort and effective formats remain unclear.The authors also identify hallucination as a deployment concern demonstrated by their experiments.Source: §5.3 · p. 8
- The paper identifies multi-agent collaboration, alignment with human preferences, and adaptation to concept drift as directions for more robust agents.For concept drift, it points to continual knowledge acquisition or lifelong learning without costly retraining.Source: §5.3 · p. 8
6. Further Discussion
The discussion weighs trust and accountability concerns alongside privacy, security, and the environmental and computational costs of LLM-centric time series analysis.
6. Further Discussion
- LLM capabilities, mechanisms, and efficiency remain difficult to assess, motivating calls for transparent development, evaluation, and model reporting.The paper also calls for clear explanations of internal processes and outputs.Source: §6 · p. 8
- Sensitive industrial time series data and possible memorization of training segments raise privacy and security concerns, including data leakage and misuse.The discussion calls for safeguards, ethical guidelines, and regulatory frameworks.Source: §6 · p. 8
- Environmental and computational costs remain concerns, with optimization and more efficient alignment and inference identified as opportunities.The discussion particularly notes handling tokenized high-precision numerical data.Source: §6 · p. 8
7. Conclusion
The paper positions LLMs as a central hub for advancing time series analysis and uses a literature review to explore directions for bridging existing gaps. It aims to raise awareness and spark discussion about this interdisciplinary area.
7. Conclusion
- LLMs could serve as the central hub for understanding and advancing time series analysis toward general-purpose analytical intelligence.The paper describes them as potential enhancers, predictors, or agents.Source: §7 · p. 8
- The paper reviews relevant literature and debates possible directions for LLM-centric time series analysis to bridge existing gaps.Source: §7 · p. 8
- The paper aims to raise awareness of the area and spark discussion on this interdisciplinary topic.It presents shifting community discourse as one way the paper could achieve its purpose.Source: §7 · p. 8
Impact Statement
The paper emphasizes responsible, transparent LLM use and trust, while anticipating potential effects on decision-making across industries. Figure 7 provides an overview of LLM-centric time series analysis and related research.
Impact Statement
- The paper highlights responsible and transparent LLM use, trust, and potential societal impacts on decision-making across industries.It notes no immediate societal consequences requiring specific emphasis, while recognizing possible future impacts.Source: Impact Statement · p. 9
A. Literature Review
- Figure 7 presents an overview of LLM-centric time series analysis and related research.Source: Figure 7 · p. 15
B.1. Overview of Related Works
The overview groups general-purpose time series agent approaches into external knowledge integration and alignment to target modality content. It notes distinct limitations for both strategies.
B.1. Overview of Related Works
- External knowledge integration uses in-context prompts to add domain information without additional training, but input-length constraints and linguistic encoding can hinder complex time series patterns.Examples include prompts with traffic, weather, and road information for domain-informed inferences.Source: §B.1 · p. 15
- Alignment to target modality content uses approaches such as instruction data and fine-tuning, but comprehensive instruction design is difficult and may favor tasks overrepresented in training data.Source: §B.1 · p. 15
- General-purpose time series agents remain a nascent area, with the overview organizing related approaches into two categories.Source: §B.1 · p. 15
B.2. Demonstrations
The demonstrations show human interactions with ChatGPT for time-series classification, data augmentation, and anomaly detection, alongside justifications for Sit and Stand classifications.
B.2. Demonstrations
- Table 1 presents justifications for classifying Sit and Stand activities.Source: Table 1 · p. 15
- Figure 8 depicts human interaction with ChatGPT for a time-series classification task.Source: Figure 8 · p. 16
- Figure 9 depicts human interaction with ChatGPT for time-series data augmentation and anomaly detection tasks.Source: Figure 9 · p. 17