  Arctic sea ice plays integral roles in both polar and global environmental
systems, notably ecosystems, communities, and economies. As sea ice continues
to decline due to climate change, it has become imperative to accurately
predict the future of sea ice extent (SIE). Using datasets of Arctic
meteorological and SIE variables spanning 1979 to 2021, we propose
architectures capable of processing multivariate time series and spatiotemporal
data. Our proposed framework consists of ensembled stacked Fourier Transform
signals (FFTstack) and Gradient Boosting models. In FFTstack, grid search
iteratively detects the optimal combination of representative FFT signals, a
process that improves upon current FFT implementations and deseasonalizers. An
optimized Gradient Boosting Regressor is then trained on the residual of the
FFTstack output. Through experiment, we found that the models trained on both
multivariate and spatiotemporal time series data performed either similar to or
better than models in existing research. In addition, we found that integration
of FFTstack improves the performance of current multivariate time series
models. We conclude that the high flexibility and performance of this
methodology have promising applications in guiding future adaptation,
resilience, and mitigation efforts in response to Arctic sea ice retreat.
