Efficient pattern matching for uncertain time series data with optimal sampling and dimensionality reduction

Abstract

Time series data mining becomes an active research area due to the rapid proliferation of temporal-dependent applications. Dimensionality reduction and uncertainty handling play a pivotal role in extracting the time series pattern. Most of the dimensionality reduction schemes are designed based on the assumption that every class of samples follows the Gaussian distribution. Lack of this property in real time data distribution does not allow dimensionality reduction techniques to characterize the different classes well and measure the data uncertainty accurately. In addition to, applying an uncertainty measurement evenly on inconsistent time series data samples may underestimate the source of uncertainty among various sub-samples. This paper presents the Handling UNcertainty and missing value prediction in Time series (HUNT). The proposed approach employs Adaptive Reservoir Filling for sampling the time series and Discrepant Sample dependent Chebyshev inequality for handling the uncertainty. The HUNT implements the adaptive reservoir filling using discrepancy estimation over a statistical population and decides the reservoir size according to the variations in the data stream. The state of the statistical population ensures the uncertainty handling over discrepant samples. The proposed approach precisely replaces the missing values with the support of the Mean-Mode imputation method. To effectively select the key features, it applies both the indirect and direct performance measures on the statistical samples. Finally, the proposed model generates the fine-tuned statistical samples through segmentation to facilitate the time series pattern matching. The experimental results demonstrate that the HUNT approach significantly outperforms the existing time series pattern matching approaches such as KSample approach by 18% higher recall and UG-Miner approach by 20% minimum Mean Absolute Error (MAE) while testing on the Weather forecasting dataset.