Time series forecasting (TSF) has been a challenging research area, and various models have been developed to address this task. However, almost all these models are trained with numerical time series data, which is not as effectively processed by the neural system as visual information. To address this challenge, this paper proposes a novel machine vision assisted deep time series analysis (MV-DTSA) framework. The MV-DTSA framework operates by analyzing time series data in a novel binary machine vision time series metric space, which includes a mapping and an inverse mapping function from the numerical time series space to the binary machine vision space, and a deep machine vision model designed to address the TSF task in the binary space. A comprehensive computational analysis demonstrates that the proposed MV-DTSA framework outperforms state-of-the-art deep TSF models, without requiring sophisticated data decomposition or model customization. The code for our framework is accessible at https://github.com/IkeYang/ machine-vision-assisted-deep-time-series-analysis-MV-DTSA-.

翻译：时间序列预测(TSF)历来是一个具有挑战性的研究领域，为此已开发出多种模型。然而，几乎所有模型都采用数值型时间序列数据进行训练，这类数据在神经系统中远不如视觉信息处理高效。为解决该挑战，本文提出了一种新颖的机器视觉辅助深度时间序列分析(MV-DTSA)框架。该框架通过在一个新型二值机器视觉时间序列度量空间中分析时间序列数据来运作，该空间包含从数值型时间序列空间到二值机器视觉空间的映射及其逆映射函数，以及一个专为在二值空间中解决TSF任务而设计的深度机器视觉模型。全面计算分析表明，所提出的MV-DTSA框架无需复杂的数据分解或模型定制，即可超越最先进的深度TSF模型。本框架的代码可通过https://github.com/IkeYang/ machine-vision-assisted-deep-time-series-analysis-MV-DTSA- 获取。