Piecewise two-dimensional normal cloud representation for time-series data mining

Many high-level dimensionality reduction approaches for mining time series have been proposed, e.g., SAX, PWCA , and Feature-based. Due to the rapid performance degradation of time-series data mining in much lower dimensionality and the continuously increasing amount of time series data with uncertainty, there remains a burning need to develop new time-series representations that can retain good performance in much lower reduced space and address uncertainty efficiently. In this work, we propose a novel time series representation, namely Two-dimensional Normal Cloud Representation (2D-NCR), based on cloud model theory. The representation achieves dimensionality reduction by transforming the raw time series into a sequence of two-dimensional normal cloud models. Moreover, a new similarity measure between the transformed time series is presented. The proposed method can reflect the characteristic data distribution of the time series and capture the variation with time. We validate the performance of our representation on the various data mining tasks of classification, clustering, and query by content. The experimental results demonstrate that 2D-NCR is an effective and competitive representation for time-series data mining.