  We study the price dynamics of cryptocurrencies using adaptive complementary
ensemble empirical mode decomposition (ACE-EMD) and Hilbert spectral analysis.
This is a multiscale noise-assisted approach that decomposes any time series
into a number of intrinsic mode functions, along with the corresponding
instantaneous amplitudes and instantaneous frequencies. The decomposition is
adaptive to the time-varying volatility of each cryptocurrency price evolution.
Different combinations of modes allow us to reconstruct the time series using
components of different timescales. We then apply Hilbert spectral analysis to
define and compute the instantaneous energy-frequency spectrum of each
cryptocurrency to illustrate the properties of various timescales embedded in
the original time series.
