Retrieval Augmented Time Series Forecasting
📰 ArXiv cs.AI
Retrieval augmented time series forecasting combines RAG and time-series foundation models for improved forecasting performance
Action Steps
- Combine retrieval-augmented generation (RAG) with time-series foundation models (TSFM) to leverage the strengths of both approaches
- Utilize TSFM to generate initial forecasts and then refine them using RAG
- Implement zero-shot forecasting to adapt to various time-series domains without requiring extensive retraining
- Evaluate the performance of the retrieval-augmented time series forecasting model using metrics such as mean absolute error (MAE) or mean squared error (MSE)
Who Needs to Know This
Data scientists and AI engineers on a team can benefit from this approach as it enables more accurate and efficient time-series forecasting, particularly in scenarios where up-to-date information is crucial
Key Insight
💡 Retrieval-augmented time series forecasting can improve forecasting performance by leveraging the strengths of both RAG and TSFM
Share This
📈 Boost time-series forecasting with retrieval-augmented generation (RAG) and time-series foundation models (TSFM)
Key Takeaways
Retrieval augmented time series forecasting combines RAG and time-series foundation models for improved forecasting performance
Full Article
Title: Retrieval Augmented Time Series Forecasting
Abstract:
arXiv:2411.08249v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) is a central component of modern LLM systems, particularly in scenarios where up-to-date information is crucial for accurately responding to user queries or when queries exceed the scope of the training data. The advent of time-series foundation models (TSFM), such as Chronos, and the need for effective zero-shot forecasting performance across various time-series domains motivates the question: Do bene
Abstract:
arXiv:2411.08249v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) is a central component of modern LLM systems, particularly in scenarios where up-to-date information is crucial for accurately responding to user queries or when queries exceed the scope of the training data. The advent of time-series foundation models (TSFM), such as Chronos, and the need for effective zero-shot forecasting performance across various time-series domains motivates the question: Do bene
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