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