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SpectraNet: Multivariate Forecasting and Imputation under Distribution Shifts and Missing Data. (arXiv:2210.12515v2 [cs.LG] UPDATED)
Oct. 26, 2022, 1:12 a.m. | Cristian Challu, Peihong Jiang, Ying Nian Wu, Laurent Callot
cs.LG updates on arXiv.org arxiv.org
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 …
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