AutoTimes: Autoregressive Time Series Forecasters via Large Language Models
- URL: http://arxiv.org/abs/2402.02370v2
- Date: Thu, 23 May 2024 01:58:28 GMT
- Title: AutoTimes: Autoregressive Time Series Forecasters via Large Language Models
- Authors: Yong Liu, Guo Qin, Xiangdong Huang, Jianmin Wang, Mingsheng Long,
- Abstract summary: We propose AutoTimes as autoregressive time series forecasters, which independently projects time series segments into the embedding space and autoregressively generates future predictions with arbitrary lengths.
AutoTimes achieves state-of-the-art with 0.1% trainable parameters and over 5 times training/inference speedup compared to advanced LLM-based forecasters.
- Score: 67.83502953961505
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Foundation models of time series have not been fully developed due to the limited availability of time series corpora and the underexploration of scalable pre-training. Based on the similar sequential formulation of time series and natural language, increasing research demonstrates the feasibility of leveraging large language models (LLM) for time series. Nevertheless, the inherent autoregressive property and decoder-only architecture of LLMs have not been fully considered, resulting in insufficient utilization of LLM abilities. To further exploit the general-purpose token transition and multi-step generation ability of large language models, we propose AutoTimes to repurpose LLMs as autoregressive time series forecasters, which independently projects time series segments into the embedding space and autoregressively generates future predictions with arbitrary lengths. Compatible with any decoder-only LLMs, the consequent forecaster exhibits the flexibility of the lookback length and scalability of the LLM size. Further, we formulate time series as prompts, extending the context for prediction beyond the lookback window, termed in-context forecasting. By adopting textual timestamps as position embeddings, AutoTimes integrates multimodality for multivariate scenarios. Empirically, AutoTimes achieves state-of-the-art with 0.1% trainable parameters and over 5 times training/inference speedup compared to advanced LLM-based forecasters.
Related papers
- Are Language Models Actually Useful for Time Series Forecasting? [21.378728572776897]
Large language models (LLMs) are being applied to time series tasks, particularly time series forecasting.
We find that removing the LLM component or replacing it with a basic attention layer does not degrade the forecasting results.
We also find that pretrained LLMs do no better than models trained from scratch, do not represent the sequential dependencies in time series, and do not assist in few-shot settings.
arXiv Detail & Related papers (2024-06-22T03:33:38Z) - TimeCMA: Towards LLM-Empowered Time Series Forecasting via Cross-Modality Alignment [21.690191536424567]
TimeCMA is a framework for time series forecasting with cross-modality alignment.
Extensive experiments on real data offer insight into the accuracy and efficiency of the proposed framework.
arXiv Detail & Related papers (2024-06-03T00:27:29Z) - CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning [59.88924847995279]
We propose a novel Cross-Modal LLM Fine-Tuning (CALF) framework for MTSF.
To reduce the distribution discrepancy, we develop the cross-modal match module.
CALF establishes state-of-the-art performance for both long-term and short-term forecasting tasks.
arXiv Detail & Related papers (2024-03-12T04:04:38Z) - Time Series Forecasting with LLMs: Understanding and Enhancing Model
Capabilities [39.874834611685124]
Large language models (LLMs) have been applied in many fields with rapid development in recent years.
This paper shows that LLMs excel in predicting time series with clear patterns and trends but face challenges with datasets lacking periodicity.
In addition, the input strategy is investigated, and it is found that incorporating external knowledge and adopting natural language paraphrases positively affects the predictive performance of LLMs for time series.
arXiv Detail & Related papers (2024-02-16T17:15:28Z) - Empowering Time Series Analysis with Large Language Models: A Survey [24.202539098675953]
We provide a systematic overview of methods that leverage large language models for time series analysis.
Specifically, we first state the challenges and motivations of applying language models in the context of time series.
Next, we categorize existing methods into different groups (i.e., direct query, tokenization, prompt design, fine-tune, and model integration) and highlight the key ideas within each group.
arXiv Detail & Related papers (2024-02-05T16:46:35Z) - Unified Training of Universal Time Series Forecasting Transformers [104.56318980466742]
We present a Masked-based Universal Time Series Forecasting Transformer (Moirai)
Moirai is trained on our newly introduced Large-scale Open Time Series Archive (LOTSA) featuring over 27B observations across nine domains.
Moirai achieves competitive or superior performance as a zero-shot forecaster when compared to full-shot models.
arXiv Detail & Related papers (2024-02-04T20:00:45Z) - Timer: Generative Pre-trained Transformers Are Large Time Series Models [83.03091523806668]
This paper aims at the early development of large time series models (LTSM)
During pre-training, we curate large-scale datasets with up to 1 billion time points.
To meet diverse application needs, we convert forecasting, imputation, and anomaly detection of time series into a unified generative task.
arXiv Detail & Related papers (2024-02-04T06:55:55Z) - Large Language Models Are Zero-Shot Time Series Forecasters [48.73953666153385]
By encoding time series as a string of numerical digits, we can frame time series forecasting as next-token prediction in text.
We find that large language models (LLMs) such as GPT-3 and LLaMA-2 can surprisingly zero-shot extrapolate time series at a level comparable to or exceeding the performance of purpose-built time series models trained on the downstream tasks.
arXiv Detail & Related papers (2023-10-11T19:01:28Z) - 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.