Interpolation of financial time series data in a virtual geographic environment

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.