A comparison of methods for trend estimation

Bianchi, Marco; Boyle, Martin; Hollingsworth, T. Déirdre

doi:10.1080/135048599353726

Cited by 50 publications

(35 citation statements)

References 2 publications

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“…If there is not much variation for any variable, it is possible to fit a regression line or curve and plot it as a trend line or trend curve. More formally, trend estimation is a statistical technique for identifying these trend lines or trend curves [5]. For purposes of discussion in this paper, we will focus only on informal trends that can be perceived visually without statistical trend estimation.…”

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confidence: 99%

Effectiveness of Animation in Trend Visualization

Robertson

Fernandez

Fisher

et al. 2008

IEEE Trans. Visual. Comput. Graphics

309

236

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Abstract-Animation has been used to show trends in multi-dimensional data. This technique has recently gained new prominence for presentations, most notably with Gapminder Trendalyzer. In Trendalyzer, animation together with interesting data and an engaging presenter helps the audience understand the results of an analysis of the data. It is less clear whether trend animation is effective for analysis. This paper proposes two alternative trend visualizations that use static depictions of trends: one which shows traces of all trends overlaid simultaneously in one display and a second that uses a small multiples display to show the trend traces side-by-side. The paper evaluates the three visualizations for both analysis and presentation. Results indicate that trend animation can be challenging to use even for presentations; while it is the fastest technique for presentation and participants find it enjoyable and exciting, it does lead to many participant errors. Animation is the least effective form for analysis; both static depictions of trends are significantly faster than animation, and the small multiples display is more accurate.Index Terms-Information visualization, animation, trends, design, experiment. INTRODUCTION: TREND VISUALIZATIONInformally, the term trend means to have a general tendency (Webster's Dictionary). A trend in data is an observed general tendency. The most common way to see a trend in data is to plot a variable's change over time on a line chart or bar chart. If there is a general increase or decrease over time, this is perceived as a trend up or down. If there is a general increase/decrease that reverses direction, it is perceived as a reversing trend (for up to a few reversals). If there are more than a few reversals, it appears to be cyclic or noisy data, and no trend is perceived. Plotting multiple variables on a timeline (as in a multiple line chart) sometimes allows the user to see counter-trends. For example, if most of the variables are generally increasing and a few are decreasing, the decreasing variables can pop out and be perceived as counter-trends. If there is not much variation for any variable, it is possible to fit a regression line or curve and plot it as a trend line or trend curve. More formally, trend estimation is a statistical technique for identifying these trend lines or trend curves [5]. For purposes of discussion in this paper, we will focus only on informal trends that can be perceived visually without statistical trend estimation.The simple approach described above only works for a number of variables along one dimension plotted against another dimension (usually time). What is the best way to see trends in two or three dimensions simultaneously?Gapminder Trendalyzer [8] is an animated bubble chart designed to show trends over time in three dimensions. Both the size and locations of bubbles smoothly animate as time passes. This technique appears to be very effective in presentations, where a presenter tells the observer where to focus attention. It makes the ...

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mentioning

confidence: 99%

Effectiveness of Animation in Trend Visualization

Robertson

Fernandez

Fisher

et al. 2008

IEEE Trans. Visual. Comput. Graphics

309

236

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“…Another line of research known as pooling has explored combining crosssectional data with time-series data [15]. Fitting trends through data [16] is a common approach and is related in some ways to the idea of identifying a trajectory. Another related area of research is sequence reconstruction.…”

Section: Discussionmentioning

confidence: 99%

The Pseudotemporal Bootstrap for Predicting Glaucoma From Cross-Sectional Visual Field Data

Tucker

Garway-Heath

2010

IEEE Trans. Inform. Technol. Biomed.

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Abstract-Progressive loss of the field of vision is characteristic of a number of eye diseases such as glaucoma, a leading cause of irreversible blindness in the world. Recently, there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration, including visual field (VF) test, retinal image, and frequent intraocular pressure measurements. Like the progression of many biological and medical processes, VF progression is inherently temporal in nature. However, many datasets associated with the study of such processes are often cross sectional and the time dimension is not measured due to the expensive nature of such studies. In this paper, we address this issue by developing a method to build artificial time series, which we call pseudo time series from cross-sectional data. This involves building trajectories through all of the data that can then, in turn, be used to build temporal models for forecasting (which would otherwise be impossible without longitudinal data). Glaucoma, like many diseases, is a family of conditions and it is, therefore, likely that there will be a number of key trajectories that are important in understanding the disease. In order to deal with such situations, we extend the idea of pseudo time series by using resampling techniques to build multiple sequences prior to model building. This approach naturally handles outliers and multiple possible disease trajectories. We demonstrate some key properties of our approach on synthetic data and present very promising results on VF data for predicting glaucoma.

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“…The second limit will show the smallest increase in the quality of the test images. The leastsquares fitting process give Equation of Trend estimation for samples [13]:…”

Section: Peak-to-peak Signal-to-noise Ratiomentioning

confidence: 99%

“…In the case of noise it seems more reasonable to use metrics like PSNR. The least-squares fitting process gives Equation of Trend estimation for samples [13]:…”

Section: Dct-based Video Quality Evaluationmentioning

confidence: 99%

Measurement of noise in the Monte Carlo point sampling method

Guzek¹,

Napieralski²

2011

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract. This paper gives a concise overview of the techniques we have used to find out the degree of measuring the quality of rendered images and a level of noise in particular. First part of the paper presents designed and conducted psychophysical experiment involving human subjective judgment. Then, two of the existing numerical image comparison methods are considered in the context of assessing the level of noise produced by global illuminations algorithms. The results of the participants' subjective responses are correlated with the data obtained from objective mathematical metrics. The main goal of research was to determine the objective and perceptual measure of quality for images with fixed sampling strategy. The results will help to establish the measures of identifying the human perception in the assessment of images generated with global illumination algorithms.

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A comparison of methods for trend estimation

Cited by 50 publications

References 2 publications

Effectiveness of Animation in Trend Visualization

Effectiveness of Animation in Trend Visualization

The Pseudotemporal Bootstrap for Predicting Glaucoma From Cross-Sectional Visual Field Data

Measurement of noise in the Monte Carlo point sampling method

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