SpectraNet: Multivariate Forecasting and Imputation under Distribution Shifts and Missing Data. (arXiv:2210.12515v1 [cs.LG])

In this work, we tackle two widespread challenges in real applications for
time-series forecasting that have been largely understudied: distribution
shifts and missing data. We propose SpectraNet, a novel multivariate
time-series forecasting model that dynamically infers a latent space spectral
decomposition to capture current temporal dynamics and correlations on the
recent observed history. A Convolution Neural Network maps the learned
representation by sequentially mixing its components and refining the output.
Our proposed approach can simultaneously produce forecasts and interpolate past …

arxiv data distribution forecasting imputation multivariate