Relevance Vector Machines-Based Time Series Prediction for Incomplete Training Dataset: Two Comparative Approaches
Abstract
Considering that real-life time series mixed with missing points cannot be directly modeled by using most of the supervised machine learning methods, this paper proposes a novel time series prediction method based on relevance vector machines for incomplete training dataset. Given the regularity between the missing inputs and outputs constructed by the phase space reconstruction, this paper imputes the missing inputs during the learning process...
Paper Details
Title
Relevance Vector Machines-Based Time Series Prediction for Incomplete Training Dataset: Two Comparative Approaches
Published Date
Aug 1, 2021
Volume
51
Issue
8
Pages
4298 - 4311
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