One Fits All: Universal Time Series Analysis by Pretrained LM and
Specially Designed Adaptors
- URL: http://arxiv.org/abs/2311.14782v1
- Date: Fri, 24 Nov 2023 16:32:47 GMT
- Title: One Fits All: Universal Time Series Analysis by Pretrained LM and
Specially Designed Adaptors
- Authors: Tian Zhou, Peisong Niu, Xue Wang, Liang Sun, Rong Jin
- Abstract summary: We introduce four unique adapters, designed specifically for downstream tasks based on the pre-trained model.
These adapters are further enhanced with efficient parameter tuning, resulting in superior performance compared to all state-of-the-art methods.
- Score: 23.292260325891032
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Despite the impressive achievements of pre-trained models in the fields of
natural language processing (NLP) and computer vision (CV), progress in the
domain of time series analysis has been limited. In contrast to NLP and CV,
where a single model can handle various tasks, time series analysis still
relies heavily on task-specific methods for activities such as classification,
anomaly detection, forecasting, and few-shot learning. The primary obstacle to
developing a pre-trained model for time series analysis is the scarcity of
sufficient training data. In our research, we overcome this obstacle by
utilizing pre-trained models from language or CV, which have been trained on
billions of data points, and apply them to time series analysis. We assess the
effectiveness of the pre-trained transformer model in two ways. Initially, we
maintain the original structure of the self-attention and feedforward layers in
the residual blocks of the pre-trained language or image model, using the
Frozen Pre-trained Transformer (FPT) for time series analysis with the addition
of projection matrices for input and output. Additionally, we introduce four
unique adapters, designed specifically for downstream tasks based on the
pre-trained model, including forecasting and anomaly detection. These adapters
are further enhanced with efficient parameter tuning, resulting in superior
performance compared to all state-of-the-art methods.Our comprehensive
experimental studies reveal that (a) the simple FPT achieves top-tier
performance across various time series analysis tasks; and (b) fine-tuning the
FPT with the custom-designed adapters can further elevate its performance,
outshining specialized task-specific models.
Related papers
- TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents [52.13094810313054]
TimeCAP is a time-series processing framework that creatively employs Large Language Models (LLMs) as contextualizers of time series data.
TimeCAP incorporates two independent LLM agents: one generates a textual summary capturing the context of the time series, while the other uses this enriched summary to make more informed predictions.
Experimental results on real-world datasets demonstrate that TimeCAP outperforms state-of-the-art methods for time series event prediction.
arXiv Detail & Related papers (2025-02-17T04:17:27Z) - Context information can be more important than reasoning for time series forecasting with a large language model [0.0]
We explore the characteristics of large language models (LLMs) for time series forecasting.
Findings indicate that no single prompting method is universally applicable.
LLMs often fail to follow the procedures described by the prompt.
arXiv Detail & Related papers (2025-02-08T21:39:07Z) - Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting [26.4608782425897]
Time-VLM is a novel framework that bridges temporal, visual, and textual modalities for enhanced forecasting.
Our framework comprises three key components: (1) a Retrieval-Augmented Learner, which extracts enriched temporal features through memory bank interactions; (2) a Vision-Augmented Learner, which encodes time series as informative images; and (3) a Text-Augmented Learner, which generates contextual textual descriptions.
arXiv Detail & Related papers (2025-02-06T05:59:45Z) - 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) - Rethinking Time Series Forecasting with LLMs via Nearest Neighbor Contrastive Learning [1.7892194562398749]
We propose NNCL-TLLM: Nearest Neighbor Contrastive Learning for Time series forecasting via Large Language Models.
First, we generate time series compatible text prototypes such that each text prototype represents both word token embeddings in its neighborhood and time series characteristics.
We then fine-tune the layer normalization and positional embeddings of the LLM, keeping the other layers intact, reducing the trainable parameters and decreasing the computational cost.
arXiv Detail & Related papers (2024-12-06T06:32:47Z) - Context is Key: A Benchmark for Forecasting with Essential Textual Information [87.3175915185287]
"Context is Key" (CiK) is a forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context.
We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters.
We propose a simple yet effective LLM prompting method that outperforms all other tested methods on our benchmark.
arXiv Detail & Related papers (2024-10-24T17:56:08Z) - Is Your LLM Outdated? Evaluating LLMs at Temporal Generalization [37.58752947129519]
The rapid advancement of Large Language Models (LLMs) highlights the urgent need for evolving evaluation methodologies.
Traditional benchmarks, which are often static, fail to capture the continually changing information landscape.
Our study examines temporal generalization, which includes the ability to understand, predict, and generate text relevant to past, present, and future contexts.
arXiv Detail & Related papers (2024-05-14T09:31:31Z) - Time Series Forecasting with LLMs: Understanding and Enhancing Model Capabilities [46.02234423159257]
Large language models (LLMs) have been applied in many fields and have developed rapidly in recent years.
Recent works treat large language models as emphzero-shot time series reasoners without further fine-tuning.
Our study shows that LLMs perform well in predicting time series with clear patterns and trends, but face challenges with datasets lacking periodicity.
arXiv Detail & Related papers (2024-02-16T17:15:28Z) - 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) - 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)
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.