  Periodic signals play an important role in daily lives. Although conventional
sequential models have shown remarkable success in various fields, they still
come short in modeling periodicity; they either collapse, diverge or ignore
details. In this paper, we introduce a novel framework inspired by Fourier
series to generate periodic signals. We first decompose the given signals into
multiple sines and cosines and then conditionally generate periodic signals
with the output components. We have shown our model efficacy on three tasks:
reconstruction, imputation and conditional generation. Our model outperforms
baselines in all tasks and shows more stable and refined results.
