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)是一个具有挑战性的研究领域,已经开发了各种模型来完成这项任务,然而,几乎所有这些模型都经过数字时间序列数据的培训,而神经系统没有像视觉信息那样有效地处理这些数据。为了应对这一挑战,本文件提出一个新的机器愿景协助深度时间序列分析(MV-DTSA)框架。MV-DTSA框架的运作方式是,在一个新型的二进制机器愿景时间序列指标空间分析时间序列数据,其中包括从数字时间序列空间到二进制机器视觉空间的绘图和反向绘图功能,以及一个旨在处理二进制空间中TSF任务的深层次机器愿景模型。一项综合计算分析表明,拟议的MV-DTSA框架超越了最新先进的深层TSF模型,而不需要复杂的数据解剖或模型定制。我们的框架代码可在https://github.com/IkeYang/ mach-vision-Aid-deep-time-setraction-MV-DTSA查阅。</s>