RTS-LLM: Reshaping Time Series Prediction with Pre-trained Large Language Models

RTS-LLM is an open-source tool that achieves accurate time series prediction using pre-trained large language models by restoring the inherent temporal structure of time series data, supporting local offline operation. It solves the problem that traditional time series prediction methods require complex feature engineering and domain knowledge, making prediction simpler and more efficient.

Time series data has unique temporal dependencies, but existing machine learning methods often ignore or destroy these structures, leading to poor prediction performance. The core insight of RTS-LLM is: pre-trained large language models have learned complex sequence patterns and long-range dependencies, which can be transferred to time series prediction tasks—only need to correctly "translate" time series data into a form understandable by the model and restore the original structure.

RTS-LLM adopts a modular design, with core components including:

• Data Layer: Handles data loading and preprocessing, supports CSV format, automatically processes missing values and outliers, and converts input formats;
• Prompt Library: Designs specialized prompt strategies for different time series data types to help the model understand context;
• Model Layer: Encapsulates interaction logic with pre-trained LLMs, supports multiple open-source models, and optimizes temporal dependency capture;
• Custom Layer: Converts model outputs into numerical predictions through neural network layers, maintaining temporal consistency (including trend decomposition, seasonal adjustment, etc.).

Advantages of RTS-LLM compared to traditional methods:

• Transfer Learning: Uses pre-trained LLM knowledge, no need for从头 training, saves resources and quickly adapts to different data;
• Temporal Structure Restoration: Understands and preserves the temporal characteristics of data (trends, periodicity, etc.) through specially designed layers;
• Privacy First: All computations are completed locally, no data is uploaded to external servers;
• Zero Cost: Open-source and free, allowing free download, use, and modification of code.

Best Applicable Scenarios: Energy management (power consumption, solar power generation prediction), financial analysis (stock trend, trading volume prediction), weather forecasting (temperature, precipitation prediction), supply chain optimization (inventory demand, logistics flow prediction). Current Limitations: Focuses on time series prediction and is not suitable for static datasets; friendly for Windows desktop applications, but enterprise integration requires additional development.

Initial use recommendations:

• Computation Lag: Complex tasks require large resources, please wait patiently;
• Data Format Error: Ensure the CSV contains correct timestamps and value columns;
• Model Selection: Choose specialized models based on data types to improve performance.

RTS-LLM uses an open-source license and welcomes community contributions. The project has a clear structure, including development documents and example scripts; the utils directory provides tools for data visualization, performance evaluation, etc. Developers can conduct secondary development or submit PRs to contribute new features.

RTS-LLM transfers the capabilities of large language models to time series analysis, overcomes the limitations of traditional methods by restoring the inherent temporal structure, and provides a low-threshold, high-efficiency prediction tool for data analysts, researchers, and enterprise users. With the development of LLM technology in the future, its accuracy, efficiency, and applicability are expected to further improve, bringing more innovations to the field of time series prediction.