Time-LLM

As explained in Time Series Forecasting Using Foundation Models

Time-LLM is a language-model-based framework that adapts existing large language models for time-series forecasting.

Time-LLM reprograms an existing large language model for time-series forecasting. It combines historical values with a natural-language prompt, aligning numeric time-series patches with the LLM’s text modality. section 9.0 section 9.7

Figure 9.1 General architecture of Time-LLM. (Right) The raw series is fed to the framework and undergoes patching before entering the patch-reprogramming phase. (Left) A prompt is used to provide context about the data. The prompt and reprogrammed patches are joined as a patch embedding, which is sent to the LLM. Then the output is projected to create a forecast for our series.

Follow the data path: patch the raw series, reprogram patches into text-aligned representations, prepend contextual prompt information, run the LLM, and project its output into numeric forecasts. section 9.1

A practical Time-LLM workflow includes these decisions:

• Provide a prompt describing the series, such as store-sales trends or weekday and weekend seasonality. section 9.2.1 section 10.2.8
• Choose an open-source LLM available through Hugging Face, then provide its tokenizer, configuration, and hidden dimension. section 9.1 section 9.2.1
• Train the patch-reprogramming components and output projection; unlike zero-shot forecasters, Time-LLM always requires training steps. section 9.2.2 section 9.6
• Use NeuralForecast to manage fitting, prediction, frequency configuration, and cross-validation, while supplying univariate series data. section 9.2 section 10.2.8

Weekly store-sales forecasting

A weekly store-sales dataset can be forecast over an eight-step horizon after removing exogenous columns because the demonstrated Time-LLM setup supports only univariate forecasting. One evaluation reported MAE of $80,417 and sMAPE of 2.51% across four eight-week cross-validation windows. The result was better than direct LLM prompting in that experiment, but not better than every specialized forecasting model. section 9.2 section 9.4

Time-LLM is easy to access through NeuralForecast, but reprogramming can be computationally expensive, especially with large backbones or many training steps. Forecast quality and anomaly-detection performance depend on training effort and evaluation design; one anomaly experiment produced an F1 score of 0.15. section 9.6 section 9.5.2

How Time Series Forecasting Using Foundation Models covers it

How deep it goes

Explains patch alignment, prompts, projections, initialization, training, forecasting, and evaluation, but stops short of detailed optimization internals.

Examples

Uses weekly store sales, daily blog visits with weekly seasonality, and daily taxi-ride anomaly detection.

What it assumes

Assumes Python forecasting workflow familiarity while teaching prompts, patch reprogramming, backbone configuration, and univariate constraints.

What you can do after

Apply it via NeuralForecast to configure, train, forecast, cross-validate, and evaluate an LLM-based time-series model.

How it connects

Connects multimodal learning, large language models, prompt engineering, time-series forecasting, anomaly detection, and NeuralForecast.

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Related concepts

• cross-validationused by · Cross-validation is a resampling technique that evaluates a model by repeatedly fitting it on some portions of available data and assessing it on held-out portions.
• reprogramminguses · Reprogramming is the technique of adapting a pretrained language model's representations or processing so it can perform a different task such as forecasting.
• forecastinguses · Forecasting is the process of predicting future values or outcomes from information currently available, often including historical observations.
• zero-shot forecastingcontrasts with · Zero-shot forecasting is forecasting with a model on a target time series without task-specific training or fine-tuning on that series.
• language modeluses · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.

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