  To what extent can we forecast a time series without fitting to historical
data? Can universal patterns of probability help in this task? Deep relations
between pattern Kolmogorov complexity and pattern probability have recently
been used to make \emph{a priori} probability predictions in a variety of
systems in physics, biology and engineering. Here we study \emph{simplicity
bias} (SB) -- an exponential upper bound decay in pattern probability with
increasing complexity -- in discretised time series extracted from the World
Bank Open Data collection. We predict upper bounds on the probability of
discretised series patterns, without fitting to trends in the data. Thus we
perform a kind of `forecasting without training data', predicting time series
shape patterns \emph{a priori}, but not the actual numerical value of the
series. Additionally we make predictions about which of two discretised series
is more likely with accuracy of $\sim$80\%, much higher than a 50\% baseline
rate, just by using the complexity of each series. These results point to a
promising perspective on practical time series forecasting and integration with
machine learning methods.
