Abstract. Visualizing time series data is useful to support discovery of relations and patterns in financial, genomic, medical and other applications. In most time series, measurements are equally spaced over time. This paper discusses the challenges for unevenly-spaced time series data and presents four methods to represent them: sampled events, aggregated sampled events, event index and interleaved event index. We developed these methods while studying eBay auction data with TimeSearcher. We describe the advantages, disadvantages, choices for algorithms and parameters, and compare the different methods. Since each method has its advantages, this paper provides guidance for choosing the right combination of methods, algorithms, and parameters to solve a given problem for unevenly-spaced time series. Interaction issues such as screen resolution, response time for dynamic queries, and meaning of the visual display are governed by these decisions. 1

Relatively few query tools exist for data exploration and pattern identification in time series data sets. In previous work we introduced Timeboxes. Timeboxes are rectangular, direct-manipulation queries for studying time-series datasets.

The visual analysis of time dependent data is an essential task in many application fields. However, visualizing large time dependent data collected within a spatial context is still a challenging task. In this paper, we therefore describe an approach for visualizing spatio-temporal data on maps. The approach is based on two commonly used concepts: 3D information visualization and information hiding. These concepts are realized by means of novel embeddings of 3D icons into a map display for representing spatio-temporal data, and an integration of event-based methods for reducing the amount of information to be represented. Our approach is capable of visualizing multiple time dependent attributes on maps, and of emphasizing the characteristics constituted by either linear or cyclic temporal dependencies. 1.

The analysis of time-oriented data is an important task in many application scenarios. In recent years, a variety of techniques for visualizing such data have been published. This variety makes it difficult for prospective users to select methods or tools that are useful for their particular task at hand. In this article, we develop and discuss a systematic view on the diversity of methods for visualizing time-oriented data. With the proposed categorization we try to untangle the visualization of time-oriented data, which is such an important concern in Visual Analytics. The categorization is not only helpful for users, but also for researchers to identify future tasks in Visual Analytics.

Widespread interest in discovering features and trends in time- series has generated a need for tools that support interactive exploration.This paper introduces timeboxes: a powerful graphical, directmanipulation metaphor for the specification of queries over time-series datasets. Our TimeFinder implementation of timeboxes supports interactive formulation and modification of queries, thus speeding the process of exploring time-series data sets and guiding data mining. TimeFinder includes windows for timebox queries, individual time-series, and details-on-demand. Other features include drag-and-drop support for query-by-example and graphical envelopes for displaying the extent of the entire data set and result set from a given query. Extensions involving increased expressive power and general temporal data sets are discussed.

Microblogs are a tremendous repository of user-generated content about world events. However, for people trying to understand events by querying services like Twitter, a chronological log of posts makes it very difficult to get a detailed understanding of an event. In this paper, we present TwitInfo, a system for visualizing and summarizing events on Twitter. TwitInfo allows users to browse a large collection of tweets using a timeline-based display that highlights peaks of high tweet activity. A novel streaming algorithm automatically discovers these peaks and labels them meaningfully using text from the tweets. Users can drill down to subevents, and explore further via geolocation, sentiment, and popular URLs. We contribute a recall-normalized aggregate sentiment visualization to produce more honest sentiment overviews. An evaluation of the system revealed that users were able to reconstruct meaningful summaries of events in a small amount of time. An interview with a Pulitzer Prize-winning journalist suggested that the system would be especially useful for understanding a long-running event and for identifying eyewitnesses. Quantitatively, our system can identify 80-100 % of manually labeled peaks, facilitating a relatively complete view of each event studied.

Time-series forecasting has a large number of applications. Users with a partial time series for auctions, new stock offerings, or industrial processes desire estimates of the future behavior. We present a data driven forecasting method and interface called Similarity-Based Forecasting (SBF). A pattern matching search in a dataset of historical time series produces a subset of curves similar to the partial time series. The forecast is displayed graphically as a river plot showing statistical information about the SBF subset. A forecasting preview interface allows users to interactively explore alternative pattern matching parameters and see multiple forecasts simultaneously. User testing with 8 users demonstrated advantages and led to improvements. Keywords--- Forecasting; time series; river plot; simultaneous previews; visualization; user interfaces. 1

For decades, financial analysts have strived to use modern data visualization tools to improve the timeliness and quality of their analysis. As the amount of data to be processed increases rapidly and requirements on quality of financial analysis rise, the demand for analysis support systems grows. We present a system for the visual analysis of large amounts of time-dependent data using animation. For each data entity, indicators are presented in a scatter-plot framework, displaying the correlation between them. The design of the glyphs illustrates additional data dimensions. The system uses animation to handle the time-dimension of the data. It offers various features, such as focus, zoom, details on demand and time period selection to support the analysis. Financial indicators are used to demonstrate the usability of the system. The animation proves to be a powerful tool for analysing time-dependent processes in cross-sectional data sets and discovering patterns in the data. Keywords- information visualisation, financial data, time-dependent data, animation, applications. 1.

Plusieurs techniques de visualisation et d’interaction avec des espaces de données temporelles ont été proposées dans la littérature. Toutes ces techniques ont été conçues indépendamment les unes des autres. Dans ce contexte, il est difficile d’explorer l’ensemble des possibilités de conception de ce type de technique de visualisation. Dans la perspective de résoudre ce problème, cet article présente une taxonomie des techniques de visualisation de données temporelles. Sur la base de l’étude de trente sept techniques et systèmes de visualisation de données temporelles, notre taxonomie structure l’espace de conception selon les étapes du processus de visualisation de Chi. Mots-clés: Techniques de Visualisation, Données Temporelles, Processus de Visualisation, Taxonomie. A wide range of visualization techniques have been designed to assist users to visually analyze and manipulate temporal data. All the proposed techniques have been designed independently. In this context it is therefore difficult to systematically explore the set of possibilities as well as to thoroughly envision visualization techniques of temporal data. Addressing this problem, we present a taxonomy of visualization techniques of temporal data. Based on a study of thirty-seven visual tools and visualization techniques of temporal data, our taxonomy structures the design space according to the Chi's visualization process steps.

analysis pipeline view of (D) the currently selected lens; (E) a property panel showing details of the currently selected lens. A context menu (F) can be invoked to perform lens-based operations, and (G) the lens toolbar allows quick access to a lens ’ parameters. Abstract—Visual representations of time-series are useful for tasks such as identifying trends, patterns and anomalies in the data. Many techniques have been devised to make these visual representations more scalable, enabling the simultaneous display of multiple variables, as well as the multi-scale display of time-series of very high resolution or that span long time periods. There has been comparatively little research on how to support the more elaborate tasks associated with the exploratory visual analysis of timeseries, e.g., visualizing derived values, identifying correlations, or discovering anomalies beyond obvious outliers. Such tasks typically require deriving new time-series from the original data, trying different functions and parameters in an iterative manner. We introduce a novel visualization technique called ChronoLenses, aimed at supporting users in such exploratory tasks. ChronoLenses perform on-the-fly transformation of the data points in their focus area, tightly integrating visual analysis with user actions, and enabling the progressive construction of advanced visual analysis pipelines.