Time series forecasting plays a crucial role in decision-making across
various domains, but it presents significant challenges. Recent studies have
explored image-driven approaches using computer vision models to address these
challenges, often employing lineplots as the visual representation of time
series data. In this paper, we propose a novel approach that uses
time-frequency spectrograms as the visual representation of time series data.
We introduce the use of a vision transformer for multimodal learning,
showcasing the advantages of our approach across diverse datasets from
different domains. To evaluate its effectiveness, we compare our method against
statistical baselines (EMA and ARIMA), a state-of-the-art deep learning-based
approach (DeepAR), other visual representations of time series data (lineplot
images), and an ablation study on using only the time series as input. Our
experiments demonstrate the benefits of utilizing spectrograms as a visual
representation for time series data, along with the advantages of employing a
vision transformer for simultaneous learning in both the time and frequency
domains.

通过使用时间频谱图作为时间序列数据的可视表示，使用视觉变换器进行多模态学习的一种新方法，与统计基线、深度学习方法、其他视觉表示方法和只使用时间序列作为输入的研究进行对比，表明利用频谱图作为时间序列数据的可视表示具有优势，并且同时学习时间和频率域的优势。