Exploratory analysis of time series data: Detection of partial similarities, clustering, and visualization (original) (raw)
Related papers
2012
This paper develops a new method for analyzing time series data observed at different locations. Spatiotemporal data such as temperature, wind velocity, vegetation, population count, and crime rate are monitored continuously at different locations in various time scales. They are usually represented as a collection of time series data and their exploratory analysis include visualization, comparison, classification, and description of their mutual relations. The method proposed in this paper serves such exploratory analysis of spatiotemporal data. It detects similar patterns in time series data occurring simultaneously at different locations in various scales from local to global. A graph-based representation visualizes the process of pattern detection and permits us to grasp the relations among the data and to cluster the data into similar groups. In addition, significance of detected patterns is evaluated by numerical measures that reflect the degree of clustering both within each group and in the whole set of data. A case study of spatiotemporal population distribution during a day in Salt Lake County, Utah demonstrates technical soundness of the proposed method.
Visual Mining of Spatial Time Series Data
Lecture Notes in Computer Science, 2004
CommonGIS is a system comprising a number of tools for visual data analysis. In this paper we demonstrate our recent developments for analysis of spatial time series data.
Timeseriespaths: Projection-based explorative analysis of multivarate time series data
Journal of WSCG, 2012
The analysis of time-dependent data is an important problem in many application domains, and interactive visualization of time-series data can help in understanding patterns in large time series data. Many effective approaches already exist for visual analysis of univariate time series supporting tasks such as assessment of data quality, detection of outliers, or identification of periodically or frequently occurring patterns. However, much fewer approaches exist which support multivariate time series. The existence of multiple values per time stamp makes the analysis task per se harder, and existing visualization techniques often do not scale well. We introduce an approach for visual analysis of large multivariate time-dependent data, based on the idea of projecting multivariate measurements to a 2D display, visualizing the time dimension by trajectories. We use visual data aggregation metaphors based on grouping of similar data elements to scale with multivariate time series. Aggregation procedures can either be based on statistical properties of the data or on data clustering routines. Appropriately defined user controls allow to navigate and explore the data and interactively steer the parameters of the data aggregation to enhance data analysis. We present an implementation of our approach and apply it on a comprehensive data set from the field of earth observation, demonstrating the applicability and usefulness of our approach.
TimeSeriesPaths: Projection-Based Explorative Analysis of Multivariate Time Series Data
The analysis of time-dependent data is an important problem in many application domains, and interactive visualization of time-series data can help in understanding patterns in large time series data. Many effective approaches already exist for visual analysis of univariate time series supporting tasks such as assessment of data quality, detection of outliers, or identification of periodically or frequently occurring patterns. However, much fewer approaches exist which support multivariate time series. The existence of multiple values per time stamp makes the analysis task per se harder, and existing visualization techniques often do not scale well. We introduce an approach for visual analysis of large multivariate time-dependent data, based on the idea of projecting multivariate measurements to a 2D display, visualizing the time dimension by trajectories. We use visual data aggregation metaphors based on grouping of similar data elements to scale with multivariate time series. Aggregation procedures can either be based on statistical properties of the data or on data clustering routines. Appropriately defined user controls allow to navigate and explore the data and interactively steer the parameters of the data aggregation to enhance data analysis. We present an implementation of our approach and apply it on a comprehensive data set from the field of earth observation, demonstrating the applicability and usefulness of our approach.
Binarizing Change for Fast Trend Similarity Based Clustering of Time Series Data
Lecture Notes in Computer Science, 2015
It is observed that traditional clustering methods do not necessarily perform well on time-series data because of the temporal relationships in the observed values over a period of time. Another issue with time series is that databases contain bulk amount of data in terms of dimension and size. Clustering algorithms based on traditional measures of dissimilarity find trade-offs between efficiency and accuracy. In addition, time series analysis should be more concerned with the patterns in change and the points of change rather than the values of change. In this paper a new representation technique and similarity measure have been proposed for agglomerative hierarchical clustering.
Visual Analytics of Spatial Time Series Data
Wien, 2021
Hiermit erkläre ich, dass ich diese Arbeit selbständig verfasst habe, dass ich die verwendeten Quellen und Hilfsmittel vollständig angegeben habe und dass ich die Stellen der Arbeit-einschließlich Tabellen, Karten und Abbildungen-, die anderen Werken oder dem Internet im Wortlaut oder dem Sinn nach entnommen sind, auf jeden Fall unter Angabe der Quelle als Entlehnung kenntlich gemacht habe.
Visualizing temporal cluster changes using Relative Density Self-Organizing Maps
Knowledge and Information Systems
We introduce a Self-Organizing Map (SOM)-based visualization method that compares cluster structures in temporal datasets using Relative Density SOM (ReDSOM) visualization. ReDSOM visualizations combined with distance matrix-based visualizations and cluster color linking, is capable of visually identifying emerging clusters, disappearing clusters, split clusters, merged clusters, enlarging clusters, contracting clusters, the shifting of cluster centroids, and changes in cluster density. As an example, when a region in a SOM becomes significantly more dense compared to an earlier SOM, and is well separated from other regions, then the new region can be said to represent a new cluster. The capabilities of ReDSOM are demonstrated using synthetic datasets, as well as real-life datasets from the World Bank and the Australian Taxation Office. The results on the real-life datasets demonstrate that changes identified interactively can be related to actual changes. The identification of such...
Time-series Bitmaps: a Practical Visualization Tool for Working with Large Time Series Databases
2005
The increasing interest in time series data mining in the last decade has resulted in the introduction of a variety of similarity measures, representations, and algorithms. Surprisingly, this massive research effort has had little impact on real world applications. Real world practitioners who work with time series on a daily basis rarely take advantage of the wealth of tools that the data mining community has made available. In this work, we attempt to address this problem by introducing a simple parameter-light tool that allows users to efficiently navigate through large collections of time series. Our system has the unique advantage that it can be embedded directly into any standard graphical user interfaces, such as Microsoft Windows, thus making deployment easier. Our approach extracts features from a time series of arbitrary length and uses information about the relative frequency of its features to color a bitmap in a principled way. By visualizing the similarities and differences within a collection of bitmaps, a user can quickly discover clusters, anomalies, and other regularities within their data collection. We demonstrate the utility of our approach with a set of comprehensive experiments on real datasets from a variety of domains.