Abstract
Missing data widely exist in the raw or processed data, implying information loss. In many cases, missing values have to be accurately imputed for further use. In this paper, an extreme case, consecutively missing data in large-length and mainly remaining data in small-length, is discussed for time series varying with operating conditions, very universal in industrial processes. Firstly, to fully utilize the information of remaining data, a similar conditions screening scheme is provided, efficient to improve imputation accuracy. Then, multiple receding imputation via Gaussian process regression (GPR) and long short term memory (LSTM) neural network are proposed, deducing generic multiple combination imputation and bidirectional imputation structures. At last, applied for data imputation of extremely missing wind power data, condition-dependent on wind speed, imputation effects of the proposed methods are carefully compared. Simulation results reveal effectiveness of these methods to impute missing data under the extreme case, laying very important foundation for data-driven applications.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Knowledge and Data Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
