VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters (2408.17253v2)

Abstract: Foundation models have emerged as a promising approach in time series forecasting (TSF). Existing approaches either repurpose LLMs or build large-scale time series datasets to develop TSF foundation models for universal forecasting. However, these methods face challenges due to the severe cross-domain gap or in-domain heterogeneity. This paper explores a new road to building a TSF foundation model from rich, high-quality natural images. Our key insight is that a visual masked autoencoder, pre-trained on the ImageNet dataset, can naturally be a numeric series forecaster. By reformulating TSF as an image reconstruction task, we bridge the gap between image pre-training and TSF downstream tasks. Surprisingly, without further adaptation in the time-series domain, the proposed VisionTS could achieve superior zero-shot forecasting performance compared to existing TSF foundation models. With fine-tuning for one epoch, VisionTS could further improve the forecasting and achieve state-of-the-art performance in most cases. Extensive experiments reveal intrinsic similarities between images and real-world time series, suggesting visual models may offer a ``free lunch'' for TSF and highlight the potential for future cross-modality research. Our code is publicly available at https://github.com/Keytoyze/VisionTS.

• The paper demonstrates that visual masked autoencoders achieve effective zero-shot time series forecasting by reformulating forecasting as an image reconstruction task.
• It converts time series data into segmented images and leverages pre-trained ImageNet models to outperform traditional methods on 43 benchmark datasets with up to 84% MSE reduction.
• The study challenges conventional TSF approaches and opens new avenues for cross-modality forecasting research in machine learning.

Visual Masked Autoencoders as Zero-Shot Time Series Forecasters: A Detailed Examination

The paper entitled "VISION TS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters" explores an innovative approach to building Time Series Forecasting (TSF) foundation models. It underscores the potential utility of Visual Masked Autoencoders (VMAE), pre-trained on the ImageNet dataset, as superior zero-shot time series forecasters without substantial adaptations to the time-series domain. This paper introduces VISION TS, leveraging the intrinsic similarities between images and time series data to effectively execute forecasting tasks.

Key Insights and Contributions

The paper embarks on a novel path that departs from traditional TSF methods, which typically utilize LLMs pre-trained on text or time series data. The authors' primary insight lies in exploiting pixel variations in images, using them as proxies for temporal sequences. They assert that images and time series exhibit several inherent similarities — they are continuous modalities, represent observations from real-world physical systems, and possess extensive but redundant data features. VISION TS utilizes a visual MAE, suggesting images could serve as a more effective cross-modality bridge to TSF than other traditional text-based approaches.

The methodological innovation centers around reformulating the TSF problem into one of image reconstruction. Time series data are transformed into images via segmentation and aligned with pre-trained image data through simple techniques like patch-level filling and pixel interpolation. This enables the VISION TS model to capitalize on pre-trained visual recognitions for accurate zero-shot forecasting without further domain-specific adjustments.

Empirical Evaluation and Results

The empirical evaluation conducted involves comprehensive testing across 43 TSF benchmarks, demonstrating that VISION TS surpasses existing state-of-the-art (SOTA) in terms of zero-shot forecasting capabilities. Remarkably, the model outperformed conventional TSF methods with up to an 84% reduction in mean square error (MSE) on various datasets compared to few-shot learning baselines. These findings underscore the model's effectiveness despite the foundational pre-training being conducted entirely on images rather than time series data.

Notably, in fine-tuning experiments, VISION TS exhibited enhanced performance with only a single epoch, aligning closely with or surpassing results from models extensively trained within the TSF domain. Such results suggest that visual models can indeed forecast time series data effectively, corroborating the authors' hypothesis about the cross-modality efficacy of image-based models.

Implications and Future Directions

This research offers substantial implications for the future of TSF, particularly in cross-modality learning. By pioneering the conception of leveraging visual models to address time series tasks, the authors initiate a dialogue on the untapped potential of non-traditional models in fields they were not initially designed for. Future investigations may explore integrating more sophisticated visual models beyond MAE and exploring multivariate or distributional forecasting. Additionally, solving existing limitations such as the incorporation of exogenous covariates remains a promising avenue to bolster the adaptability and applicability of visual-based TSF models.

In conclusion, this paper challenges the constraints of conventional methods, illuminating new pathways for zero-shot TSF. VISION TS stands as a testament to how cross-disciplinary methodologies, rooted in intrinsic similarities between diverse data types, can yield high-performance and practical utility in fields like time series analysis. As such, it forms a foundation upon which future research may build, advancing our understanding and capabilities within the realms of AI and machine learning.