  We propose SutraNets, a novel method for neural probabilistic forecasting of
long-sequence time series. SutraNets use an autoregressive generative model to
factorize the likelihood of long sequences into products of conditional
probabilities. When generating long sequences, most autoregressive approaches
suffer from harmful error accumulation, as well as challenges in modeling
long-distance dependencies. SutraNets treat long, univariate prediction as
multivariate prediction over lower-frequency sub-series. Autoregression
proceeds across time and across sub-series in order to ensure coherent
multivariate (and, hence, high-frequency univariate) outputs. Since sub-series
can be generated using fewer steps, SutraNets effectively reduce error
accumulation and signal path distances. We find SutraNets to significantly
improve forecasting accuracy over competitive alternatives on six real-world
datasets, including when we vary the number of sub-series and scale up the
depth and width of the underlying sequence models.
