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TS-RAG: Retrieval Augmented Generation for Time Ser... | AI Research

Key Takeaways

  • TS-RAG: Retrieval Augmented Generation for Time Series Forecasting introduces a framework that adapts Retrieval-Augmented Generation (RAG) to improve the acc...
  • However, most time series models are constrained by limited training data, smaller parameter scales, and a lack of the extensive generative capabilities found in large language models.
  • Simply concatenating reference sequences into the prompt, as done in language models, may not yield the expected results.
  • To address these challenges, we propose a novel approach, TS-RAG, which leverages RAG to enhance forecasting performance.
  • Experimental results demonstrate that TS-RAG achieves consistent state-of-the-art performance across several real-world forecasting benchmarks.
Paper AbstractExpand

While deep learning models, particularly transformer-based architectures, have shown impressive performance in time series forecasting, the application of retrieval-augmented generation (RAG) in this domain remains limited. Since RAG has proven effective in enhancing the capabilities of large language models by incorporating relevant external information, retrieving similar time series sequences as references might also improve accuracy in time series forecasting tasks. However, most time series models are constrained by limited training data, smaller parameter scales, and a lack of the extensive generative capabilities found in large language models. Simply concatenating reference sequences into the prompt, as done in language models, may not yield the expected results. To address these challenges, we propose a novel approach, TS-RAG, which leverages RAG to enhance forecasting performance. The framework introduces specially designed reference tokens to effectively fuse information from the input sequence with that from retrieved similar sequences, enabling a more robust capture of complex temporal dynamics. Experimental results demonstrate that TS-RAG achieves consistent state-of-the-art performance across several real-world forecasting benchmarks.

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting introduces a framework that adapts Retrieval-Augmented Generation (RAG) to improve the accuracy of time series predictions. By retrieving and integrating historical sequences that share similar patterns with current input data, the model gains a broader context for forecasting, addressing the limitations of models that rely solely on their internal training weights.

Addressing Forecasting Limitations

While transformer-based models have improved time series forecasting, they often struggle with non-stationary patterns or rare events because they rely on implicit knowledge learned during training. The authors, Yixiong Xiao, Congxi Xiao, Shuangli Li, and Jingbo Zhou from Baidu, Inc., note that simply appending retrieved data to an input—a common practice in large language models—is ineffective for time series models due to their smaller scale and lack of extensive generative capabilities. TS-RAG is designed to overcome this by creating a structured way to fuse external historical information with the current input sequence.

How the Framework Works

TS-RAG uses a specialized retrieval and integration process:

  • Similarity Retrieval: The model uses vector search techniques to identify relevant historical sequences from a database, which is more computationally efficient than traditional methods like Dynamic Time Warping (DTW).

  • Reference Tokens: The framework introduces learnable "reference tokens." These tokens act as compressed representations of the retrieved historical patterns.

  • Information Fusion: These tokens are prepended to the input sequence. The model then uses self-attention to process the input and cross-attention to align and extract useful information from the retrieved sequences. This allows the model to explicitly incorporate external historical context into its final forecast.

Experimental Performance

The researchers evaluated TS-RAG on six benchmark datasets: ECL, ETTh1, ETTh2, ETTm1, ETTm2, and Weather. According to the paper, the version of the model that accounts for channel dependencies (TS-RAG-CM) achieved the lowest average Mean Squared Error (0.310) and Mean Absolute Error (0.348) across these datasets. The results indicate that the framework consistently outperforms several state-of-the-art models, including PatchTST, iTransformer, and TimeXer, in multivariate forecasting tasks.

Considerations for Implementation

The authors identify computational efficiency as a primary challenge in retrieval-based forecasting. While traditional retrieval methods can be too slow for real-time applications, the authors state that their use of vector search techniques allows for faster inference times without sacrificing predictive accuracy. The framework is designed to be modular, allowing for the integration of external knowledge without requiring constant fine-tuning of the base model.

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