  Modeling long-range dependencies in sequential data is a crucial step in
sequence learning. A recently developed model, the Structured State Space (S4),
demonstrated significant effectiveness in modeling long-range sequences.
However, It is unclear whether the success of S4 can be attributed to its
intricate parameterization and HiPPO initialization or simply due to State
Space Models (SSMs). To further investigate the potential of the deep SSMs, we
start with exponential smoothing (ETS), a simple SSM, and propose a stacked
architecture by directly incorporating it into an element-wise MLP. We augment
simple ETS with additional parameters and complex field to reduce the inductive
bias. Despite increasing less than 1\% of parameters of element-wise MLP, our
models achieve comparable results to S4 on the LRA benchmark.
