Model-agnostic Retrieval-Augmented Extended Forecasting for time series
Time series forecasting with pretrained foundation models has demonstrated strong zero-shot capabilities. However, achieving optimal performance on time series with short or negligible historical data in domain-specific applications typically requires adaptation via either fine-tuning or RAG. While fine-tuning is effective, it incurs substantial computational costs. This work explores RAG within univariate time series (Retrieval Augmented Generation) as a more efficient alternative, in particular RAF (Retrieval Augmented Forecasting), and introduces RAEF (Retrieval-Augmented Extended Forecasting), a model-agnostic method built upon RAF. RAEF incorporates key refinements to the retrieval and aggregation mechanisms: (1) direct retrieval in input-space rather than embedding-space, reducing inference overhead, and (2) concatenation-based aggregation that preserves temporal structure instead of averaging. Empirical evaluation across multiple benchmark datasets demonstrates that RAEF outperforms RAF in both accuracy and inference overhead. Furthermore, comprehensive comparisons with zero-shot and fine-tuned foundation models show that RAEF achieves competitive or superior performance to fine-tuning while avoiding its computational burden, establishing it as a practical and scalable approach for domain adaptation in time series forecasting.
Code (0)
등록된 구현이 없습니다.
Tasks
Time Series ForecastingDomain AdaptationSimilar Papers 제목 키워드 기반
Channel-wise Retrieval for Multivariate Time Series Forecasting
Multivariate time series forecasting often struggles to capture long-range dependencies due to fixed lookback windows. Retrieval-augmented forecasting addresses this by retrieving historical segments from memory, but exi…
Multivariate Time Series ForecastingQuiZSF: A Retrieval-Augmented Framework for Zero-Shot Time Series Forecasting
Accurate forecasting of sequential data streams is a cornerstone of modern Web services, supporting applications such as traffic management, user behavior modeling, and online anomaly prevention. However, in many Web env…
Time Series ForecastingTimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting
Time series forecasting plays a crucial role in data mining, driving rapid advancements across numerous industries. With the emergence of large models, time series foundation models (TSFMs) have exhibited remarkable gene…
RAGRetrievalRetrieval-augmented GenerationTime Series+2Retrieval Augmented Time Series Forecasting
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 sc…
RAGRetrievalRetrieval-augmented GenerationTime Series+1Retrieval Augmented Time Series Forecasting
Time series forecasting uses historical data to predict future trends, leveraging the relationships between past observations and available features. In this paper, we propose RAFT, a retrieval-augmented time series fore…
RetrievalTime SeriesTime Series Forecasting