![]() ![]() ![]() In lay words, this approach weights the data's seasonal variations by exponentially increasing amounts over time hence, in this example, the revenue's seasonality in 2015 has a greater impact on the forecast than the seasonality of the data in 2014, and 2014's seasonality impacts the forecast more so than 2013's seasonality, and so on. These new functions predict future values based on historical time - based data using the AAA version of the exponential smoothing (ETS) algorithm with the weights assigned to data variances over time in proportion to the terms of their geometric progression based on the following exponential scale. ![]()
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