Summary
StockIQ's simple exponential smoothing is an implementation of the oldie-but-goodie exponential smoothing. Exponential smoothing assigns exponentially decreasing weights to past observations. This means more recent data points have a greater influence on the forecast than older ones.
Settings
Exponential Smoothing is a technique where previous observations are merged with your most recent observation according to a smoothing parameter alpha ( α ), which controls the weight between previous and current observations:
Calculation
new value = (current observation * α) + (previous observations * (1-α))
As with the Rolling Average forecast algorithm, the raw simple smoothed values benefit from StockIQ's seasonality, which modifies the smoothed predictions with our detected seasonality components after the SES projections have been made.
As with the rolling average algorithm, it is in place mostly to compare as a benchmark versus StockIQ Forecast Algorithm, and generally we don't recommend its use versus StockIQ Algorithm.