nivariate time series forecasting method which provides innovative solutions to issues faced during
the initialization and optimization stages of existing methods.
Forecasting performance of the Ata method is superior to existing methods both in terms of easy implementation and accurate 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.
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