Related papers: TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation

TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation

URL: http://arxiv.org/abs/2412.16643v1
Date: Sat, 21 Dec 2024 14:27:38 GMT
Title: TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation
Authors: Silin Yang, Dong Wang, Haoqi Zheng, Ruochun Jin,
Abstract summary: TimeRAG is a framework that incorporates Retrieval-Augmented Generation (RAG) into time series forecasting LLMs. Experiments on datasets from various domains show that the integration of RAG improved the prediction accuracy of the original model by 2.97% on average.
Score: 5.607649016637917
License:
Abstract: Although the rise of large language models (LLMs) has introduced new opportunities for time series forecasting, existing LLM-based solutions require excessive training and exhibit limited transferability. In view of these challenges, we propose TimeRAG, a framework that incorporates Retrieval-Augmented Generation (RAG) into time series forecasting LLMs, which constructs a time series knowledge base from historical sequences, retrieves reference sequences from the knowledge base that exhibit similar patterns to the query sequence measured by Dynamic Time Warping (DTW), and combines these reference sequences and the prediction query as a textual prompt to the time series forecasting LLM. Experiments on datasets from various domains show that the integration of RAG improved the prediction accuracy of the original model by 2.97% on average.

Related papers

An Evaluation of Standard Statistical Models and LLMs on Time Series Forecasting [16.583730806230644]
This study highlights the key challenges that large language models encounter in the context of time series prediction. The empirical results indicate that while large language models can perform well in zero-shot forecasting for certain datasets, their predictive accuracy diminishes notably when confronted with diverse time series data and traditional signals.
arXiv Detail & Related papers (2024-08-09T05:13:03Z)
A Comprehensive Evaluation of Large Language Models on Temporal Event Forecasting [45.0261082985087]
We conduct a comprehensive evaluation of Large Language Models (LLMs) for temporal event forecasting. We find that directly integrating raw texts into the input of LLMs does not enhance zero-shot extrapolation performance. In contrast, incorporating raw texts in specific complex events and fine-tuning LLMs significantly improves performance.
arXiv Detail & Related papers (2024-07-16T11:58:54Z)
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)
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)
TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling [67.02157180089573]
Time series pre-training has recently garnered wide attention for its potential to reduce labeling expenses and benefit various downstream tasks. This paper proposes TimeSiam as a simple but effective self-supervised pre-training framework for Time series based on Siamese networks.
arXiv Detail & Related papers (2024-02-04T13:10:51Z)
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)
Cluster-and-Conquer: A Framework For Time-Series Forecasting [94.63501563413725]
We propose a three-stage framework for forecasting high-dimensional time-series data. Our framework is highly general, allowing for any time-series forecasting and clustering method to be used in each step. When instantiated with simple linear autoregressive models, we are able to achieve state-of-the-art results on several benchmark datasets.
arXiv Detail & Related papers (2021-10-26T20:41:19Z)

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