  The contribution of this study is a novel approach to introduce mean
reversion in multi-step-ahead forecasts of state-space models. This approach is
demonstrated in a prawn pond water quality forecasting application. The mean
reversion constrains forecasts by gradually drawing them to an average of
previously observed dynamics. This corrects deviations in forecasts caused by
irregularities such as chaotic, non-linear, and stochastic trends. The key
features of the approach include (1) it enforces mean reversion, (2) it
provides a means to model both short and long-term dynamics, (3) it is able to
apply mean reversion to select structural state-space components, and (4) it is
simple to implement. Our mean reversion approach is demonstrated on various
state-space models and compared with several time-series models on a prawn pond
water quality dataset. Results show that mean reversion reduces long-term
forecast errors by over 60% to produce the most accurate models in the
comparison.
