ate forecasting.
It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend and seasonal).
This methodology performed well on the M3 and M4-competition data.
Returns ATA(p,q,phi) (E,T,S) applied to the data.</p>
<p>The Ata method (Yapar et al. (2019) <a href="https://doi.org/10.15672/hujms.461032" class="external-link">doi:10.15672/hujms.461032</a>
), an alternative to exponential smoothing (described in Yapar (2016) <a href="https://doi.o