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Tile-based parallel coordinates and its application in financial visualization

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Tile-based parallel coordinates and its application in financial visualization Jamal Alsakran, Ye Zhao Kent State University, Department of Computer Science, Kent, OH – PowerPoint PPT presentation

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Title: Tile-based parallel coordinates and its application in financial visualization


1
Tile-based parallel coordinates and its
application in financial visualization
  • Jamal Alsakran, Ye Zhao
  • Kent State University, Department of Computer
    Science, Kent, OH

and Xinlei Zhao Kent State University,
Department of Finance, Kent, OH Office of the
Comptroller of the Currency, Washington, USA
2
Motivation
  • Visual clutter usually weakens or even diminishes
    parallel coordinates ability when the data size
    increases
  • Visualization interactivity allows users to gain
    wider insight into the data
  • Financial data analysis is a significant
    application domain for visual analytics

3
Background
  • Johansson et al (05,06) propose high textures to
    represent the data, and first introduced an
    opacity transfer function to reveal structures of
    the data
  • Zhou et al (08) propose energy minimization to
    perform visual clustering, where they used
    transfer functions to assign opacity and colors
    to different clusters
  • In financial data, Theme River, Growth Matrix,
    Pixel-based etc

4
Tile-based Parallel Coordinates
  • Parallel coordinates plotting area defines an
    image, I(W,H), with width W and height H
  • Each data item q is projected as a polyline on
    the image, I(W,H)
  • For each fragment I(x,y), where 0 x lt W and 0
    y lt H, we compute the number of lines
    intersecting with it, denoting as D(x,y)
  • A polyline-intersection density image D(W,H) is
    generated.

5
Tile-based Parallel Coordinates
  • Tile-based PC promotes the traditional
    pixel-based perspective of plotting to a new
    stage, by defining each fragment as a rectangular
    region of the image space with a user-specified
    size
  • A classical PC plot can simply be achieved by
    assigning each fragment for one pixel in the
    image space

6
Tile-based Parallel Coordinates
X
Y

















H
I(x,y)
W
7
Tile-based Parallel Coordinates
X
Y

















H
I(x,y)
W
8
Tile-based Parallel Coordinates
X
Y

















H
I(x,y)
W
9
Tile-based Parallel Coordinates
X
Y

















H
I(x,y)
W
10
Color and Opacity TFs
  • Transfer functions are employed to assign local
    optical attributes according to the density
    values
  • For each fragment I(x,y), we define four transfer
    functions TF to determine the three color
    elements, R, G, B, and the opacity, O, from its
    density value D(x,y)
  • The histogram of the densities is plotted to
    facilitate the manipulation of the transfer
    functions

11
Color and Opacity TFs
Occurrence
density
Histogram
12
Fast Computing of Line-Tile Intersection
  • Immediate visual feedback when users continuously
    change the tile size is crucial to guarantee
    interactivity
  • A fast computing algorithm is employed (Bresenham
    algorithm)
  • To fully utilize Bresenham algorithm, we perform
    a coordinates transformation, which scales each
    tile to one pixel

13
Fast Computing of Line-Tile Intersection
14
Example
Original plot
tiles 450
tiles 150
tiles 20
15
U.S. stocks during years (2000 to 2007) 477,074
data items
16
Case Study Mutual Funds
  • Mutual fund allows a group of investors to pool
    their money together and invest.
  • In our study, we have 5785 funds
  • Each data item represents one mutual fund, whose
    characteristics are investigated to find its
    correlation with the annual return
  • The study examines the most significant
    characteristics including total net asset size,
    cash holdings, front-end load, rear-end load,
    expense ratios, and turnovers

17
Front Load vs. Return
of tiles 100
of tiles 20
18
Turnover vs. Return with Outliers
  • It easily accommodate emphasized outliers
    together with the main trend
  • It emphasizes crucial data items while keeping
    the whole data as a background view
  • outliers are more easily to be compared with
    mainstream data

19
Analyzing Statistical Regression with
Visualization
  • Tile-based PC is used to visually analyze the
    performance of a traditional statistical method
    widely used by financial analysts
  • The standard linear regression model that assumes
    a linear relation between the explanatory
    variables and the dependent variable
  • Estimated return coef characteristic
    interp.
  • Comparison shows that our method is more
    informative

20
Analyzing Statistical Regression with
Visualization
Regression Data
Real Data
21
Multiple Clusters Visualization
22
Full Attributes Visualization with Outliers
  • The red polyline represents the best performer,
    Dreyfus Premier Greater China B (DPCBX), which
    produced 85 return for investors.
  • The purple polyline is the second-best mutual
    fund, Old Mutual Clay Finlay China C (OMNCX)
  • The best performers achievement in the year 2006
    has no direct relation with their fund properties
    and managing activities

23
Conclusion
  • A novel tile-based density and transfer functions
    to for visual cluttering reduction
  • The tile-based parallel coordinates technique
    improves the performance, yields more
    controllability and promotes the visual
    understanding
  • Visual analytical results on financial data set
    of 2006 U.S mutual funds illustrate the potential
    of using the method in financial economics
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