  The dynamic time scan forecasting method relies on the premise that the most
important pattern in a time series precedes the forecasting window, i.e., the
last observed values. Thus, a scan procedure is applied to identify similar
patterns, or best matches, throughout the time series. As oppose to euclidean
distance, or any distance function, a similarity function is dynamically
estimated in order to match previous values to the last observed values.
Goodness-of-fit statistics are used to find the best matches. Using the
respective similarity functions, the observed values proceeding the best
matches are used to create a forecasting pattern, as well as forecasting
intervals. Remarkably, the proposed method outperformed statistical and machine
learning approaches in a real case wind speed forecasting problem.
