Statistical analysis with missing data
Statistical analysis with missing data
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
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
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The problem of missing 2-dimensional time series data is one of the main problems existing in several real scientific and engineering studies. In this paper, a new technique for imputing the incomplete time series data is proposed. The imputing process combines two major steps. The first step is to estimate the potential imputing boundary regions based on the intersection of the slopes of non-missing neighbors. Then, a new bootstrap algorithm is applied to estimate the value of missing data. The experimental results show that our new algorithms outperforms in both accuracy and time efficiency when compared with Cubic interpolation, Multiple Imputation(MI) and Varies Window Similarity Measure(VWSM) algorithms under various missing rates from 10% to 70%.