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A Comparative Analysis of Machine Learning and Grey Models

arXiv 2104.00871 ↗First posted 2 Apr 2021cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 192 references that are in the map. First posted on arXiv 2 Apr 2021.

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15 of 192 references.

References · 15

  1. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 903
  2. 2014Deep Learning in Neural Networks: An Overviewcited by 765
  3. 2015Deep Speech 2: End-to-End Speech Recognition in English and Mandarincited by 449
  4. 2018Regularisation of Neural Networks by Enforcing Lipschitz Continuitycited by 311
  5. 2018Learning from positive and unlabeled data: a surveycited by 218
  6. 2018An Introduction to Deep Reinforcement Learningcited by 201
  7. 2017Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentationcited by 109
  8. 2019Evaluating time series forecasting models: An empirical study on performance estimation methodscited by 52
  9. 2018Did you hear that? Adversarial Examples Against Automatic Speech Recognitioncited by 48
  10. 2021An Experimental Review on Deep Learning Architectures for Time Series Forecastingcited by 47
  11. 2021A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospectscited by 13
  12. 2020Combining Parametric Land Surface Models with Machine Learningcited by 2
3 more
  1. 2018A brief introduction to the Grey Machine Learningcited by 1
  2. 2019Machine Learning on Biomedical Images: Interactive Learning, Transfer Learning, Class Imbalance, and Beyondcited by 1
  3. 2020A Rolling Optimized Nonlinear Grey Bernoulli Model RONGBM(1,1) and application in predicting total COVID-19 infected casescited by 1

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