Multi-Granular Trend Detection for Time-Series Analysis

Uncertainty-Aware Seasonal-Trend Decomposition Based on Loess

Seasonal-trend decomposition based on loess (STL) is a powerful tool to explore time series data visually. In this article, we present an extension of STL to uncertain data, named uncertainty-aware STL (UASTL). Our method propagates multivariate Gaussian distributions mathematically exactly through the entire analysis and visualization pipeline. Thereby, stochastic quantities shared between the components of the decomposition are preserved. Moreover, we present application scenarios with uncertainty modeling based on Gaussian processes, e.g., data with uncertain areas or missing values. Besides these mathematical results and modeling aspects, we introduce visualization techniques that address the challenges of uncertainty visualization and the problem of visualizing highly correlated components of a decomposition. The global uncertainty propagation enables the time series visualization with STL-consistent samples, the exploration of correlation between and within decomposition's components, and the analysis of the impact of varying uncertainty. Finally, we show the usefulness of UASTL and the importance of uncertainty visualization with several examples. Thereby, a comparison with conventional STL is performed.

Detecting a multigranularity event in an unequal interval time series based on self-adaptive segmenting

Analyzing the temporal behaviors and revealing the hidden rules of objects that produce time series data to detect the events that users are interested in have recently received a large amount of attention. Generally, in various application scenarios and most research works, the equal interval sampling of a time series is a requirement. However, this requirement is difficult to guarantee because of the presence of sampling errors in most situations. In this paper, a multigranularity event detection method for an unequal interval time series, called SSED (self-adaptive segmenting based event detection), is proposed. First, in view of the trend features of a time series, a self-adaptive segmenting algorithm is proposed to divide a time series into unfixed-length segmentations based on the trends. Then, by clustering the segmentations and mapping the clusters to different identical symbols, a symbol sequence is built. Finally, based on unfixed-length segmentations, the multigranularity events in the discrete symbol sequence are detected using a tree structure. The SSED is compared to two previous methods with ten public datasets. In addition, the SSED is applied to the public transport systems in Xiamen, China, using bus-speed time-series data. The experimental results show that the SSED can achieve higher efficiency and accuracy than existing algorithms.

Visual Analytics for Electromagnetic Situation Awareness in Radio Monitoring and Management

Traditional radio monitoring and management largely depend on radio spectrum data analysis, which requires considerable domain experience and heavy cognition effort and frequently results in incorrect signal judgment and incomprehensive situation awareness. Faced with increasingly complicated electromagnetic environments, radio supervisors urgently need additional data sources and advanced analytical technologies to enhance their situation awareness ability. This paper introduces a visual analytics approach for electromagnetic situation awareness. Guided by a detailed scenario and requirement analysis, we first propose a signal clustering method to process radio signal data and a situation assessment model to obtain qualitative and quantitative descriptions of the electromagnetic situations. We then design a two-module interface with a set of visualization views and interactions to help radio supervisors perceive and understand the electromagnetic situations by a joint analysis of radio signal data and radio spectrum data. Evaluations on real-world data sets and an interview with actual users demonstrate the effectiveness of our prototype system. Finally, we discuss the limitations of the proposed approach and provide future work directions.