Fits an EWMA model to a time series.
Fits an EWMA model to a time series. Uses the first point in the time series as a starting value. Uses sum squared error as an objective function to optimize to find smoothing parameter The model for EWMA is recursively defined as S_t = (1 - a) * X_t + a * S_{t-1}, where a is the smoothing parameter, X is the original series, and S is the smoothed series Note that the optimization is performed as unbounded optimization, although in its formal definition the smoothing parameter is <= 1, which corresponds to an inequality bounded optimization. Given this, the resulting smoothing parameter should always be sanity checked https://en.wikipedia.org/wiki/Exponential_smoothing
the time series to which we want to fit an EWMA model
EWMA model
Fits an Exponentially Weight Moving Average model (EWMA) (aka. Simple Exponential Smoothing) to a time series. The model is defined as S_t = (1 - a) * X_t + a * S_{t - 1}, where a is the smoothing parameter, X is the original series, and S is the smoothed series. For more information, please see https://en.wikipedia.org/wiki/Exponential_smoothing.