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Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y. Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, Qingsong Wen

arXiv:2310.01728v2cs.LGcs.AI

TL;DR

Time-series forecasting lacks the broadly reusable, data-efficient foundation models available in NLP and CV, partly because continuous time-series data must be aligned with LLMs’ discrete-token representations. Time-LLM reprograms time series into text prototypes, uses Prompt-as-Prefix with a frozen LLM, and projects transformed patches into forecasts; evaluations show it outperforms specialized forecasting models, including in few-shot and zero-shot settings.

  • Problem

    Time-series forecasting commonly requires specialized designs and abundant in-domain data, while aligning continuous time series with discrete-token LLMs remains an open challenge.

  • Method

    Time-LLM reprograms time-series inputs into text prototypes and adds Prompt-as-Prefix guidance before processing them with an intact, frozen LLM.

  • Results

    TIME-LLM consistently outperforms state-of-the-art forecasting methods across benchmarks and settings, especially few-shot and zero-shot scenarios.

  • Takeaways & Limitations

    The results indicate that forecasting can be cast as a language task handled by an off-the-shelf LLM through reprogramming.

Abstract

from arXiv · show

Time series forecasting holds significant importance in many real-world dynamic systems and has been extensively studied. Unlike natural language process (NLP) and computer vision (CV), where a single large model can tackle multiple tasks, models for time series forecasting are often specialized, necessitating distinct designs for different tasks and applications. While pre-trained foundation models have made impressive strides in NLP and CV, their development in time series domains has been constrained by data sparsity. Recent studies have revealed that large language models (LLMs) possess robust pattern recognition and reasoning abilities over complex sequences of tokens. However, the challenge remains in effectively aligning the modalities of time series data and natural language to leverage these capabilities. In this work, we present Time-LLM, a reprogramming framework to repurpose LLMs for general time series forecasting with the backbone language models kept intact. We begin by reprogramming the input time series with text prototypes before feeding it into the frozen LLM to align the two modalities. To augment the LLM's ability to reason with time series data, we propose Prompt-as-Prefix (PaP), which enriches the input context and directs the transformation of reprogrammed input patches. The transformed time series patches from the LLM are finally projected to obtain the forecasts. Our comprehensive evaluations demonstrate that Time-LLM is a powerful time series learner that outperforms state-of-the-art, specialized forecasting models. Moreover, Time-LLM excels in both few-shot and zero-shot learning scenarios.

1 INTRODUCTION

Time-LLM addresses the mismatch between specialized, data-hungry time-series forecasting and general-purpose language models by reprogramming frozen LLMs for forecasting. It combines text-prototype representations with natural-language prompts and reports strong performance, especially in few-shot and zero-shot settings.

  • Motivation: Time-series forecasting remains specialized by task and domain, unlike language models that support diverse tasks in few-shot or zero-shot settings.Current forecasting methods typically require extensive domain expertise and task-specific designs, whereas foundation language models generalize across NLP tasks.
  • Motivation: Aligning continuous time-series data with LLMs’ discrete-token processing is an open challenge for accurate, data-efficient, and task-agnostic forecasting.The relevant time-series knowledge and reasoning capabilities are not naturally present in LLM pre-training.
  • Approach: TIME-LLM reprograms time series into text-prototype representations while keeping the backbone language model intact.The framework treats forecasting as a task that can be handled by an off-the-shelf LLM without altering its pre-trained backbone.
  • Approach: TIME-LLM augments reprogrammed inputs with declarative prompts, including domain knowledge and task instructions, to guide LLM reasoning.These prompts enrich the input context and direct desired transformations of the reprogrammed time-series patches.
  • Results: TIME-LLM consistently exceeds state-of-the-art forecasting performance, especially in few-shot and zero-shot scenarios, while maintaining efficient reprogramming.The reported result positions LLM reprogramming as a route toward using language-model capabilities for sequential data.

2 RELATED WORK

Prior forecasting approaches are commonly specialized within the time-series modality, while related work has begun transferring capabilities across modalities. TIME-LLM belongs to the emerging cross-modality adaptation line and reprograms LLMs for time-series learning.

  • Task-specific Learning: Task-specific forecasting models target narrow domains or temporal structures, limiting versatility and generalizability across diverse time-series data.Examples include ARIMA for univariate forecasting, LSTM for sequence modeling, and temporal convolutional networks or transformers for longer dependencies.
  • In-modality Adaptation: In-modality adaptation develops time-series pre-trained models using supervised or self-supervised pre-training strategies.These approaches follow the broader success of pre-trained models fine-tuned for downstream tasks in computer vision and natural language processing.
  • Cross-modality Adaptation: Cross-modality adaptation transfers knowledge from pre-trained NLP and CV foundations to time-series modeling through multimodal fine-tuning or model reprogramming.The paper places TIME-LLM in this category, where existing time-series research remains limited.

3 METHODOLOGY

TIME-LLM reprograms time-series inputs into representations usable by a frozen language model, then projects its transformed patches into forecasts. Its pipeline combines patch-based input transformation, text-prototype alignment, Prompt-as-Prefix context, frozen-LLM processing, and output projection.

  • Input Transformation: TIME-LLM partitions multivariate series into independently processed univariate channels, normalizes and patches each channel, and embeds the resulting patches.Patching preserves local semantic information while forming a compact token sequence that reduces computational burdens.
  • Forecasting Pipeline: Only the lightweight input transformation and output projection are updated, while the backbone language model remains frozen.After the prompt and patch embeddings pass through the LLM, the prefix representations are discarded and the remaining outputs are flattened and linearly projected into forecasts.
  • Patch Reprogramming: Learned text prototypes reprogram time-series patch embeddings into the language model’s representation space to align time-series and natural-language modalities.The prototypes are a small collection derived from the backbone vocabulary and encode language cues such as “short up” and “steady down.”
  • Patch Reprogramming: A multi-head cross-attention layer uses time-series patches and text prototypes to produce representations whose hidden dimensions are aligned with the backbone model.The reprogrammed outputs are formed across attention heads and linearly projected to the backbone hidden dimension.
  • Prompt-as-Prefix: Prompt-as-Prefix adds dataset context, task instructions, and input statistics to guide the frozen LLM’s transformation of reprogrammed patches.It is designed to avoid the precision and post-processing constraints associated with asking language models to predict numerical values directly in natural language.

4 MAIN RESULTS

TIME-LLM outperforms strong forecasting baselines across long-term, short-term, few-shot, and zero-shot settings. Ablations indicate that cross-modality alignment, prompting, input statistics, and model capacity materially affect performance, while the reprogramming network remains lightweight.

  • Long-term forecasting: 12% and 20% average performance gains over GPT4TS and TimesNet, respectively, were observed in long-term forecasting.TIME-LLM also achieved a 1.4% average MSE reduction relative to PatchTST and improvements exceeding 12% over DLinear.
  • Few-shot forecasting: 5% MSE reduction over GPT4TS was achieved with 10% training data without fine-tuning the language model.Average MSE improvements over PatchTST, DLinear, and TimesNet exceeded 8%, 12%, and 33%, respectively; analogous gains also appeared with 5% data.
  • Zero-shot forecasting: TIME-LLM outperformed competitive baselines by over 14.2% in MSE reduction and showed increasing error reductions over GPT4TS in 10% and 5% few-shot and zero-shot settings.The cited passage reports reductions of 7.7%, 8.4%, and a further value for the three settings, respectively.
  • Model analysis: Ablating representation alignment, prompting, or input statistics degraded forecasting, with average degradation reaching 9.2%, over 8%, and 10.2% MSE increase, respectively.Few-shot degradation exceeded 17% without representation alignment and 19% without prompting; removing task context and input statistics also reduced performance.
  • Model analysis: The reprogramming network uses fewer than 6.6 million trainable parameters, approximately 0.2% of Llama-7B’s total parameters.Overall efficiency is capped primarily by the selected backbone language model.

5 CONCLUSION AND FUTURE WORK

TIME-LLM adapts frozen LLMs for forecasting by reprogramming time series into text prototypes and adding natural-language guidance. The results support forecasting as a language task while motivating broader multimodal and analytical extensions.

  • 5 CONCLUSION AND FUTURE WORK: TIME-LLM reprograms time series into text prototypes and uses Prompt-as-Prefix to augment frozen LLM reasoning.The framework keeps the backbone model intact and adds natural-language context and task instructions.
  • 5 CONCLUSION AND FUTURE WORK: Evaluations show that adapted frozen LLMs can outperform specialized expert forecasting models.The paper reports state-of-the-art performance and describes the adapted models as effective time series machines.
  • 5 CONCLUSION AND FUTURE WORK: The results suggest that time series forecasting can be cast as a language task for an off-the-shelf LLM.This conclusion follows from reprogramming the time series into representations more natural for language models.
  • 5 CONCLUSION AND FUTURE WORK: Future work includes optimizing reprogramming representations, adding explicit time series knowledge through continued pre-training, and developing multimodal joint reasoning.The paper also proposes extending the framework to broader time series analytical abilities and other capabilities.

B.2 DATASET DETAILS

The experiments cover long- and short-term forecasting across established datasets with varied domains, frequencies, horizons, and evaluation metrics. TIME-LLM uses standardized configurations and examines sensitivity to key architectural and input hyperparameters.

  • B.2 DATASET DETAILS: The evaluation spans eight long-term benchmarks plus the M4 and M3-Quarterly short-term forecasting datasets.The long-term benchmarks include four ETT datasets, Weather, Electricity, Traffic, and ILI.
  • B.2 DATASET DETAILS: The datasets vary substantially in sampling frequency, domain, dimensionality, and series count.Examples include ETT power-load data, German meteorological stations, California freeway sensors, business and economic series, and quarterly M3 series.
  • B.2 DATASET DETAILS: Long-term forecasting is evaluated with MSE and MAE, while M4 short-term forecasting uses SMAPE, MASE, and OWA.OWA is adopted as the overall weighted average metric used in the M4 competition.
  • B.2 DATASET DETAILS: The default setup uses Llama-7B, 100 text prototypes for short-term tasks, and 1000 for long-term tasks.The backbone has 32 layers across tasks, with Adam used by default.
  • B.2 DATASET DETAILS: Sensitivity analysis examines backbone layers, text prototypes V′, input length T, and patch reprogramming cross-attention heads K.The correlated results are reported in Figure 6 for ETTh1.

D.1 LONG-TERM FORECASTING

TIME-LLM performs strongly across long-term forecasting benchmarks and remains competitive in short-term and few-shot settings. It achieves broad state-of-the-art coverage while using an intact backbone LLM.

  • D.1 LONG-TERM FORECASTING: 36 out of 40 instances achieve state-of-the-art performance across eight time series benchmarks.This result is obtained by reprogramming the smallest Llama model while keeping it intact.
  • D.1 LONG-TERM FORECASTING: Over 22% MSE and 16% MAE margins separate TIME-LLM from the second-best results in the reported long-term comparison.The paper compares against statistical baselines and recent models including N-HiTS and N-BEATS.
  • D.2 SHORT-TERM FORECASTING: TIME-LLM surpasses GPT4TS by 8.7% overall and TimesNet by 10% overall in short-term forecasting.The reported gains include larger improvements on selected M4 sampling intervals and yearly data.
  • D.2 SHORT-TERM FORECASTING: TIME-LLM achieves state-of-the-art results in 32 of 35 10% few-shot cases and 21 of 32 5% few-shot cases.These cases span seven different time series benchmarks.

E.2 ZERO-SHOT FORECASTING

TIME-LLM performs strongly in zero-shot forecasting, including cross-domain transfers, while outperforming competitive models on average. The paper attributes this performance to resource-efficient activation of knowledge transfer and reasoning capabilities.

  • E.2 ZERO-SHOT FORECASTING: TIME-LLM reduces average zero-shot MSE by over 23.5% and MAE by 12.4% across all baselines.The comparison covers six competitive time series models.
  • E.2 ZERO-SHOT FORECASTING: Cross-domain transfers show average reductions exceeding 20.8% in MSE and 11.3% in MAE.Examples include ETTh2 → ETTh1 and ETTm2 → ETTm1.
  • E.2 ZERO-SHOT FORECASTING: TIME-LLM exhibits superior performance gains compared with the similarly sized 7B-backbone LLMTime model.The paper identifies LLMTime as a recent zero-shot time series forecasting effort using a similarly sized backbone.
  • E.2 ZERO-SHOT FORECASTING: The authors attribute the zero-shot results to reprogramming that activates LLM knowledge transfer and reasoning capabilities resource-efficiently.This explanation is stated specifically for time series tasks in zero-shot adaptation.

F ABLATION STUDY

The ablation study reports that TIME-LLM’s reprogramming approach substantially outperforms its QLoRA fine-tuning variant.

  • 19% average performance gain over the QLoRA variant demonstrates the advantage of TIME-LLM’s reprogramming approach.The comparison is reported as part of the full ablation results in Tab. 17.

G EFFICIENCY COMPARISON WITH MODEL FINE-TUNING

The efficiency comparison evaluates model reprogramming against QLoRA-based parameter-efficient fine-tuning on long-term ETTh1 forecasting. Reprogramming reduces trainable parameters, memory use, and training time.

  • Model reprogramming is compared with QLoRA using Llama backbones at one-quarter and full capacity.The protocol evaluates ETTh1 forecasts 96 and 336 steps ahead.
  • 71.2% average reduction in trainable parameters is achieved across four scenarios.The comparison covers long-range forecasting efficiency.
  • 23.1% smaller memory consumption accompanies model reprogramming across the four evaluated scenarios.
  • 25.3% faster training speed is reported for model reprogramming compared with QLoRA.

H ERROR BARS

The error-bar analysis reports standard deviations for TIME-LLM and the strongest competing methods across long- and short-term forecasting datasets.

  • Experiments are conducted three times, with standard deviations reported for TIME-LLM and the runner-up model.Long-term comparisons use PatchTST across four ETT datasets, while short-term comparisons use N-HiTS on M4 datasets.

I VISUALIZATION

The visualization section compares TIME-LLM with representative forecasting methods across long-term, short-term, few-shot, and zero-shot settings. The figures plot model predictions against ground truths.

  • TIME-LLM is visualized against GPT4TS, PatchTST, and Autoformer across multiple forecasting scenarios.
  • TIME-LLM shows notably superior forecasting accuracy in long-term and short-term comparisons.Figure 7 uses input-96-predict-96 long-term forecasting, while Figure 8 covers short-term forecasting.
  • Few-shot and zero-shot visualizations use long-term input-96-predict-96 forecasting setups.Figures 9 and 10 present these limited-data scenarios.
  • TIME-LLM exhibits remarkable superiority when forecasting with limited data, particularly relative to GPT4TS.
  • Figures 7, 9, and 10 encode ground truths in blue and predictions in orange.Figure 7 covers ETTh1 long-term forecasting, Figure 9 covers ETTm1 few-shot forecasting, and Figure 10 covers ETTh1→ETTh2 zero-shot forecasting.
  • Figure 8 presents short-term forecasting cases from the M4 dataset under input-36-predict-18 settings.
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