  A common forecasting setting in real world applications considers a set of
possibly heterogeneous time series of the same domain. Due to different
properties of each time series such as length, obtaining forecasts for each
individual time series in a straight-forward way is challenging. This paper
proposes a general framework utilizing a similarity measure in Dynamic Time
Warping to find similar time series to build neighborhoods in a k-Nearest
Neighbor fashion, and improve forecasts of possibly simple models by averaging.
Several ways of performing the averaging are suggested, and theoretical
arguments underline the usefulness of averaging for forecasting. Additionally,
diagnostics tools are proposed allowing a deep understanding of the procedure.
