Hierarchical Meta-Learning in Time Series Forecasting for Improved Interference-Less Machine Learning

Hierarchical Meta-Learning in Time Series Forecasting for Improved Interference-Less Machine Learning

The importance of an interference-less machine learning scheme in time series prediction is crucial, as an oversight can have a negative cumulative effect, especially when predicting many steps ahead of the currently available data. The on-going research on noise elimination in time series forecasti...

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Bibliographic Details
Main Authors: David Afolabi, Sheng-Uei Guan, Ka Lok Man, Prudence W. H. Wong, Xuan Zhao
Format: Article
Language:English
Published: MDPI AG 2017-11-01
Series:Symmetry
Subjects:
component trends
empirical mode decomposition
interference-less machine learning
long short-term memory
meta-learning
moving average
noise reduction
nonlinear autoregressive neural network
time series forecasting
Online Access:https://www.mdpi.com/2073-8994/9/11/283

Internet

https://www.mdpi.com/2073-8994/9/11/283

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