Artificial Intelligence (AI) has recently shown its capabilities for almost every field of life. Machine Learning, which is a subset of AI, is a `HOT' topic for researchers. Machine Learning outperforms other classical forecasting techniques in almost all-natural applications. It is a crucial part of modern research. As per this statement, Modern Machine Learning algorithms are hungry for big data. Due to the small datasets, the researchers may not prefer to use Machine Learning algorithms. To tackle this issue, the main purpose of this survey is to illustrate, demonstrate related studies for significance of a semi-parametric Machine Learning framework called Grey Machine Learning (GML). This kind of framework is capable of handling large datasets as well as small datasets for time series forecasting likely outcomes. This survey presents a comprehensive overview of the existing semi-parametric machine learning techniques for time series forecasting. In this paper, a primer survey on the GML framework is provided for researchers. To allow an in-depth understanding for the readers, a brief description of Machine Learning, as well as various forms of conventional grey forecasting models are discussed. Moreover, a brief description on the importance of GML framework is presented.
翻译:人工智能(AI)最近展示了几乎每个生活领域的能力。作为AI的一个子集的机器学习是研究人员的一个“HOT”专题。机器学习在几乎所有自然应用中都优于其他古典预测技术。这是现代研究的一个关键部分。根据这一说法,现代机器学习算法渴望获得大数据。由于数据集小,研究人员可能不愿使用机器学习算法。为了解决这一问题,本调查的主要目的是说明、展示有关半参数机器学习框架的意义的相关研究,称为灰机器学习(GML)。这种框架能够处理大数据集和小数据集,用于时间序列预测可能的结果。这项调查全面概述了现有的半参数机器学习技术,用于时间序列预测。在这份文件中,研究人员可能不愿使用GML框架的主旨调查。为了深入了解读者,可以简要描述机器学习以及各种常规灰色预报模型。此外,还简要地介绍了GML框架的重要性。