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  3. Smoothing Functions

Smoothing Functions

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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
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