Modeling and Imputation of Large Incomplete Multidimensional Datasets
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Nearest neighbour approach in the least-squares data imputation algorithms
Information Sciences: an International Journal
Semi-supervised time series classification
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Imputing incomplete time-series data based on varied-window similarity measure of data sequences
Pattern Recognition Letters
Toward accurate dynamic time warping in linear time and space
Intelligent Data Analysis
Imputing time series data by regional-gradient-guided bootstrapping algorithm
ISCIT'09 Proceedings of the 9th international conference on Communications and information technologies
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In this paper new algorithms with the combination between the Regional-Gradient-Guided Bootstrapping Algorithm and Dynamics Time Warping Technique for imputing incomplete time series data are proposed. The new measurement for curve similarity comparison by using the changing of slope of time series data are used. The main contribution of this paper is to propose new technique for imputing the fluctuate time series data. We compare our new method with Cubic interpolation, Multiple imputation, Windows Varies Similarity Measurement algorithms and Regional-Gradient-Guided Bootstrapping Algorithm. The experimental results showed that our new algorithms are outperform than these method.