Interpolation of financial time series data in a virtual geographic environment
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Authors
Borna, Kambiz
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2022-06
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Conference Contribution - Oral Presentation
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Keyword
Bitcoin
financial time series data
time series data
financial markets
spatialisation
algorithms
financial time series data
time series data
financial markets
spatialisation
algorithms
ANZSRC Field of Research Code (2020)
Citation
Borna, Kambiz. (2022, June). Interpolation of financial time series data in a virtual geographic environment. Paper presented at the 62nd Annual Conference of the New Zealand Association of Economists, Wellington.
Abstract
This paper introduces a new approach to visualising and interpolating financial time series data, e.g., Bitcoin prices, in a spatial domain using the notion of spatialization: forming a spatial representation of non-spatial phenomena. The proposed algorithm first utilises the temporal components of the observations, i.e., date and time, to build a 2D virtual geographic map. It then uses the assigned coordinates to the observations and their values to estimate unknown values and construct a 3D topographic map. We assess the 3D maps using the price time series of Bitcoin with 30-minute frequency, and the results show the reliability of the 3D maps in analysing the time series data.
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