  The ability to forecast far into the future is highly beneficial to many
applications, including but not limited to climatology, energy consumption, and
logistics. However, due to noise or measurement error, it is questionable how
far into the future one can reasonably predict. In this paper, we first
mathematically show that due to error accumulation, sophisticated models might
not outperform baseline models for long-term forecasting. To demonstrate, we
show that a non-parametric baseline model based on periodicity can actually
achieve comparable performance to a state-of-the-art Transformer-based model on
various datasets. We further propose FreDo, a frequency domain-based neural
network model that is built on top of the baseline model to enhance its
performance and which greatly outperforms the state-of-the-art model. Finally,
we validate that the frequency domain is indeed better by comparing univariate
models trained in the frequency v.s. time domain.
