tsMorph: generation of semi-synthetic time series to understand
  algorithm performance
Moisés Santos André de Carvalho Carlos Soares
Time series forecasting is a subject of significant scientific and industrial
importance. Despite the widespread utilization of forecasting methods, there is
a dearth of research aimed at comprehending the conditions under which these
methods yield favorable or unfavorable performances. Empirical studies,
although common, encounter challenges due to the limited availability of
datasets, impeding the extraction of reliable insights. To address this, we
present tsMorph, a straightforward approach for generating semi-synthetic time
series through dataset morphing. tsMorph operates by creating a sequence of
datasets derived from two original datasets. These newly generated datasets
exhibit a progressive departure from the characteristics of one dataset and a
convergence toward the attributes of the other. This method provides a valuable
alternative for obtaining substantial datasets. In this paper, we demonstrate
the utility of tsMorph by assessing the performance of the Long Short-Term
Memory Network forecasting algorithm. The time series under examination are
sourced from the NN5 Competition. The findings reveal compelling insights.
Notably, the performance of the Long Short-Term Memory Network improves
proportionally with the frequency of the time series. These experiments affirm
that tsMorph serves as an effective tool for gaining an understanding of
forecasting algorithm behaviors, offering a pathway to overcome the limitations
posed by empirical studies and enabling more extensive and reliable
experimentation.