Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting
(2307.11494)Abstract
Diffusion models have achieved state-of-the-art performance in generative modeling tasks across various domains. Prior works on time series diffusion models have primarily focused on developing conditional models tailored to specific forecasting or imputation tasks. In this work, we explore the potential of task-agnostic, unconditional diffusion models for several time series applications. We propose TSDiff, an unconditionally-trained diffusion model for time series. Our proposed self-guidance mechanism enables conditioning TSDiff for downstream tasks during inference, without requiring auxiliary networks or altering the training procedure. We demonstrate the effectiveness of our method on three different time series tasks: forecasting, refinement, and synthetic data generation. First, we show that TSDiff is competitive with several task-specific conditional forecasting methods (predict). Second, we leverage the learned implicit probability density of TSDiff to iteratively refine the predictions of base forecasters with reduced computational overhead over reverse diffusion (refine). Notably, the generative performance of the model remains intact -- downstream forecasters trained on synthetic samples from TSDiff outperform forecasters that are trained on samples from other state-of-the-art generative time series models, occasionally even outperforming models trained on real data (synthesize).
Overview
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Introduces TSDiff, a self-guiding diffusion model designed for flexible and generalizable time series forecasting, diverging from traditional task-specific models.
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TSDiff utilizes an unconditional training regime, enabling adaptation to various forecasting tasks during inference through observation self-guidance.
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Features a novel self-guidance mechanism allowing the model to iteratively refine forecasts based on observed data without prior detailed context.
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Showcases competitive performance in quantitative evaluations against task-specific models and introduces the Linear Predictive Score (LPS) for future research assessment.
Predict, Refine, Synthesize: Exploring the Capacities of Self-Guiding Diffusion Models for Time Series Forecasting
An Overview of TSDiff: A Self-Guiding Diffusion Model
Time series forecasting plays a pivotal role in numerous applications, ranging from financial market analysis to energy demand prediction. Traditional models have often been designed for specific imputation or forecasting tasks, raising questions about their flexibility and generalizability. In contrast, the work by Kollovieh et al. introduces TSDiff, a diffusion model that diverges from this trend by adopting an unconditional training regime. This approach not only maintains the generative prowess of diffusion models but also facilitates their application to a wide array of forecasting tasks through a novel self-guidance mechanism during inference.
Core Contributions of the Study
Unconditional Training for Versatile Forecasting
TSDiff's unconditional training approach stands out by not restricting the model to specific forecasting tasks during the training phase. Instead, it relies on observation self-guidance, a method allowing the model to adapt to various forecasting scenarios during inference without additional training or auxiliary networks. This flexibility is proving vital as it means TSDiff can be adjusted for different tasks post-training, making it a powerful tool for a broad spectrum of applications.
The Self-Guidance Mechanism
One of the paper's novel contributions is the self-guidance mechanism. This mechanism enables the model to generate forecasts conditional on observed data without detailed prior knowledge about the forecast's context or missing data patterns. It effectively uses the model's learned implicit probability density to iteratively refine base forecaster's predictions, demonstrating competitive performance against task-specific models in numerous benchmarks.
Quantitative Evaluation and Linear Predictive Score
Empirical results showcase TSDiff's ability to rival and occasionally surpass task-specific conditional models, using the newly introduced Linear Predictive Score (LPS) metric among others for evaluation. The LPS, defined as the test forecast performance of a linear ridge regression model trained on synthetic samples, serves both as a testament to TSDiff's generative capabilities and a reliable metric for evaluating synthetic sample quality in future research.
Implications and Future Directions
The introduction of TSDiff and its self-guidance mechanism presents a significant pivot from the task-specific training of traditional forecasting models. This methodology harbors practical implications, notably its efficiency and the reduced need for retraining models to accommodate new forecasting tasks. By encapsulating a wide range of forecasting scenarios within a single model framework, TSDiff not only streamlines the forecasting process but also opens up new avenues for research into more dynamic, adaptable AI forecasting systems.
Looking forward, the scalability of such an approach in handling high-dimensional multivariate time series or real-time forecasting scenarios poses intriguing research questions. Further exploration into optimizing the self-guidance mechanism for computational efficiency and the potential integration of real-time data adaptation might bolster its applicability in more immediate, data-intensive environments.
Conclusion
TSDiff, with its unconditionally-trained diffusion process and self-guiding inference mechanism, marks a promising advancement in the domain of time series forecasting. By offering a versatile solution that stands on par with task-specific models while affording greater adaptability and efficiency, it paves the way for future developments toward more generalizable, real-time forecasting models. The exploration raises critical considerations for the ongoing development of AI-driven forecasting tools, hinting at a future where models gracefully adapt to evolving data landscapes without the need for constant retraining.
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