Explaining human mobility predictions through a pattern matching algorithm

The name of the file indicate information:
{type of sequence}_{type of measure}_{sequence properites}_{additional information}.csv

{type of sequence} - 'synth' for synthetic or 'london' for real mobility data from London, UK.
{type of measure} - 'r2' for R-squared measure or 'corr' for Spearman's correlation
{sequence properties} - for synthetic data there are three types of sequences, described in the research article (random, markovian, nonstationary). For real mobility data this part includes information about data processing parameters: (...)_london_{type of mobility sequence}_{DBSCAN epsilon value}_{DBSCAN min_pts value}. {type of mobility sequence} is 'seq' for next-place sequences and '30min' or '1H' for the next time-bin sequences and indicate the size of the time-bin.
Files with 'predictability' at the end of the file contain R-squared and Spearman's correlation of measures calculated in relation to the predictability measure.

R2 files include values of R-squared for all types of modelled regression functions.
'line' indicates {y = ax + b} for single variable and {y = ax + by + c} for two variables.
'expo' indicates {y = a*x^b + c} for single variable and {y = a*x^b + c*y^d + e} for two variables
'log' indicates {y = a*log(x*b) + c} for single variable and {y = a * x + c * log(y) + e + d*x * log(y)} for two variables.
'logf' indicates {y = a*log(x) + c * log(y) + e + b*log(x) * log(y)} for two variables