Original Paper: https://arxiv.org/abs/2310.04948

By: Defu Cao, Furong Jia, Sercan O Arik, Tomas Pfister, Yixiang Zheng, Wen Ye, Yan Liu

Abstract:

The past decade has witnessed significant advances in time series modeling with deep learning. While achieving state-of-the-art results, the best-performing architectures vary highly across applications and domains. Meanwhile, for natural language processing, the Generative Pre-trained Transformer (GPT) has demonstrated impressive performance via training one general-purpose model across various textual datasets. It is intriguing to explore whether GPT-type architectures can be effective for time series, capturing the intrinsic dynamic attributes and leading to significant accuracy improvements. In this paper, we propose a novel framework, TEMPO, that can effectively learn time series representations. We focus on utilizing two essential inductive biases of the time series task for pre-trained models: (i) decomposition of the complex interaction between trend, seasonal and residual components; and (ii) introducing the design of prompts to facilitate distribution adaptation in different types of time series. TEMPO expands the capability for dynamically modeling real-world temporal phenomena from data within diverse domains. Our experiments demonstrate the superior performance of TEMPO over state-of-the-art methods on zero shot setting for a number of time series benchmark datasets. This performance gain is observed not only in scenarios involving previously unseen datasets but also in scenarios with multi-modal inputs. This compelling finding highlights TEMPO's potential to constitute a foundational model-building framework.

Summary Notes

Enhancing Time Series Forecasting with TEMPO: A GPT-Based Approach

Time series forecasting is essential in sectors like finance, healthcare, and transportation, where accurate predictions drive better decision-making and efficiency.

However, capturing the complex patterns in time series data, such as seasonality and trends, has been a challenge for traditional deep learning methods.

This is where TEMPO comes in, using a Generative Pre-trained Transformer (GPT) framework designed specifically for the unique requirements of time series forecasting.

Understanding Time Series Forecasting Challenges

Time series forecasting plays a vital role in predicting future events across various industries. The complexity of time series data, marked by its temporal dependencies and sudden changes, poses significant challenges.

Traditional models often struggle to effectively capture these dynamics, leading to less accurate forecasts.

The Role of Generative Pre-trained Transformers in Forecasting

The use of Large Language Models (LLMs) like GPT has expanded beyond language tasks to time series forecasting. By treating forecasting as a sequence generation problem, similar to language modeling, there's a new potential to improve prediction accuracy.

Background Studies

• Research shows that LLMs can adapt to time series data by treating it in a text-like format.
• Techniques like prompt tuning have been effective in applying LLMs to time series forecasting, requiring minimal adjustments to the pre-trained models.

Introducing TEMPO

TEMPO stands out by breaking down time series into basic elements: trend, seasonality, and residuals. This approach helps it capture and predict the patterns in data more accurately.

Key Features of TEMPO:

• Model Design: TEMPO's design focuses on accurately modeling the separate components of time series data for precise forecasts.
• Inductive Biases: It integrates trends and seasonality into its architecture, helping it naturally handle the cyclical patterns in time series data.
• Soft Prompting: The model uses soft prompts to better focus on and predict the decomposed elements of the time series.
• Experimentation: Tested across various datasets, TEMPO has shown superior forecasting ability, especially in scenarios involving new, unseen data types.

TEMPO's Methodology

TEMPO simplifies time series forecasting by breaking down the data, making it easier for the model to learn and predict future values.

The use of adaptable soft prompts allows TEMPO to encode detailed knowledge about time series elements, enhancing its predictions.

Results and Impact

TEMPO consistently outperforms leading models in benchmarks, showcasing its capability with different types of time series data.

These results confirm the effectiveness of TEMPO's approach and suggest great potential for GPT-based models in forecasting.

Conclusion

TEMPO marks a significant advancement in time series forecasting by leveraging GPT architectures and introducing strategies like trend and seasonality decomposition and soft prompts.

It sets a new standard for accuracy and efficiency in forecasting across various industries.

Future Directions

The success of TEMPO opens up avenues for further research, including handling more complex data types, expanding TEMPO to other sequence modeling tasks, and refining its techniques for even better performance.

Acknowledgments

This work introduces a pioneering GPT-based forecasting model, showcasing the importance of understanding time series data and the potential of soft prompts in adapting models for complex tasks.

The contributions have been supported by grants and collaborations, whose insights have been crucial.

Keywords: Time Series Forecasting, GPT, Trend Decomposition, Seasonality, Soft Prompts, Zero-shot Learning.

In summary, TEMPO not only advances time series forecasting but also demonstrates the versatile application of GPT models, pointing towards the future of predictive analytics and informed decision-making.