A Survey on Semi-parametric Machine Learning Technique for Time Series Forecasting

• URL: http://arxiv.org/abs/2104.00871v1
• Date: Fri, 2 Apr 2021 03:26:20 GMT
• Title: A Survey on Semi-parametric Machine Learning Technique for Time Series Forecasting
• Authors: Khwaja Mutahir Ahmad, Gang He, Wenxin Yu, Xiaochuan Xu, Jay Kumar, Muhammad Asim Saleem
• Abstract summary: Grey Machine Learning (GML) 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.
• Score: 4.9341230675162215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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