One Fits All:Power General Time Series Analysis by Pretrained LM (2302.11939v6)

Abstract: Although we have witnessed great success of pre-trained models in NLP and computer vision (CV), limited progress has been made for general time series analysis. Unlike NLP and CV where a unified model can be used to perform different tasks, specially designed approach still dominates in each time series analysis task such as classification, anomaly detection, forecasting, and few-shot learning. The main challenge that blocks the development of pre-trained model for time series analysis is the lack of a large amount of data for training. In this work, we address this challenge by leveraging language or CV models, pre-trained from billions of tokens, for time series analysis. Specifically, we refrain from altering the self-attention and feedforward layers of the residual blocks in the pre-trained language or image model. This model, known as the Frozen Pretrained Transformer (FPT), is evaluated through fine-tuning on all major types of tasks involving time series. Our results demonstrate that pre-trained models on natural language or images can lead to a comparable or state-of-the-art performance in all main time series analysis tasks, as illustrated in Figure 1. We also found both theoretically and empirically that the self-attention module behaviors similarly to principle component analysis (PCA), an observation that helps explains how transformer bridges the domain gap and a crucial step towards understanding the universality of a pre-trained transformer.The code is publicly available at https://github.com/DAMO-DI-ML/One_Fits_All.

Overview of the Paper "One Fits All: Power General Time Series Analysis by Pretrained LM"

The paper "One Fits All: Power General Time Series Analysis by Pretrained LM" addresses the challenges and potential solutions for applying pre-trained models, usually successful in NLP and CV, to time series analysis. The core of this research is the development of a unified framework, leveraging pre-trained language or image models, to contend with various tasks in time series analysis.

Methodology

The authors propose utilizing the Frozen Pretrained Transformer (FPT), specifically leveraging pre-trained NLP models, such as GPT-2, to solve diverse time series tasks without altering the model's core architecture. This method retains the self-attention layers and the feedforward components, capitalizing on the embedded knowledge within these layers. The frozen configurations help to prevent catastrophic forgetting, ensuring that the pre-trained models maintain their learned representations while adapting to time series data.

The paper highlights the architecture’s adaptation strategy:

• Embedding Layer: Re-trained to map time series data appropriately.
• Normalization and Positional Encoding: Fine-tuned to align with the new data structure.
• Tokenization via Patch Formation: Sequences are segmented into patches akin to CV inputs, thus enhancing historical data representation.

Empirical Evaluation

The methodology was applied and evaluated across six main tasks: time series classification, anomaly detection, imputation, short/long-term forecasting, and few-shot/zero-shot learning. Key findings include:

• Performance: The FPT framework achieves either state-of-the-art or comparable performance to advanced time series models across all tasks.
• Imputation and Classification: The robustness was evident, with models showing superior accuracy in filling data gaps and categorizing sequence-level data.
• Few-shot Learning: Demonstrated notable efficiency even when trained on limited data, surpassing several dedicated models.

Theoretical and Empirical Insights

A significant contribution of the paper is the exploration of how self-attention mechanisms in transformers resemble principal component analysis (PCA). Both theoretical analysis and empirical experiments support the notion that attention layers, through pre-training, acquire a domain-agnostic capability similar to PCA. This observation not only bridges the domain gap between NLP and time series data but also unravels part of the mystery behind transformers’ universal applicability.

Implications and Future Directions

The implications of this research are profound, suggesting that models pre-trained on large language or image datasets can be repurposed as general-purpose engines for time series data. Practically, this consolidates efforts in multi-task learning for time series data into a single adaptable framework, potentially reducing the resource-intense need for model-specific designs.

Moving forward, the paper hints at exploring parameter-efficient fine-tuning methods and expanding the framework’s applicability to even more complex domains. Additionally, understanding the theoretical aspects of transformer generality, particularly from an n-gram perspective, could further enhance their adaptability across data types.

Conclusion

This research underscores the flexibility and power of pre-trained models beyond their original domains. By adapting LLMs for time series tasks, the authors set a precedent for future works aiming to harness the latent capabilities of transformers across interdisciplinary applications, highlighting a tangible step towards universal computation engines.