tsMorph: generation of semi-synthetic time series to understand algorithm performance

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.