Smoothing Functions
Smoothing is very often used (and abused) in the industry to make a quick visual examination of the data properties (e.g. trend, seasonality, etc.), fit in missing values, and conduct a quick out-of-sample forecast. This section lists the supported time series smoothing techniques in NumXL.
- NxEMA - exponentially-weighted moving (rolling/running) average
- NxMA - Moving (rolling) average using prior data points
- WMA - Weighted-Moving Average
- SESMTH - (Brown's) Simple Exponential Smoothing
- DESMTH - (Holt's) Double Exponential Smoothing
- LESMTH - (Brown's) Linear Exponential Smoothing
- TESMTH - (Holt-Winters's) Triple Exponential Smoothing
- NxCMA - Centered Moving-average Filter
- GESMTH - General Exponential Smoothing Function
- NxSMA - Seasonal Moving-average Filter
- NxTrend - Deterministic trend in a time series