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Simple exponential smoothing does not do well when there is a trend in the data. [1] In such situations, several methods were devised under the name "double exponential smoothing" or "second-order exponential smoothing," which is the recursive application of an exponential filter twice, thus being termed "double exponential smoothing".
The Double Exponential Moving Average (DEMA) indicator was introduced in January 1994 by Patrick G. Mulloy, in an article in the "Technical Analysis of Stocks & Commodities" magazine: "Smoothing Data with Faster Moving Averages" [1] [2] It attempts to remove the inherent lag associated with Moving Averages by placing more weight on recent values.
Smoothing may be distinguished from the related and partially overlapping concept of curve fitting in the following ways: . curve fitting often involves the use of an explicit function form for the result, whereas the immediate results from smoothing are the "smoothed" values with no later use made of a functional form if there is one;
The idea is do a regular exponential moving average (EMA) calculation but on a de-lagged data instead of doing it on the regular data. Data is de-lagged by removing the data from "lag" days ago thus removing (or attempting to) the cumulative effect of the moving average.
The TSI is a "double smoothed" indicator; meaning that a moving average applied to the data (daily momentum in this case) is smoothed again by a second moving average. The calculation for TSI uses exponential moving averages. The formula for the TSI is:
Cook, John D, "Double Exponential Integration" with source code. Dennes, Graeme, "Numerical Integration With Tanh-Sinh Quadrature" A Microsoft Excel workbook containing fourteen quadrature programs which demonstrate the Tanh-Sinh and other quadrature methods. Demonstrates the astounding speed and accuracy of the Tanh-Sinh method in particular ...
A double exponential function (red curve) compared to a single exponential function (blue curve). A double exponential function is a constant raised to the power of an exponential function . The general formula is f ( x ) = a b x = a ( b x ) {\displaystyle f(x)=a^{b^{x}}=a^{(b^{x})}} (where a >1 and b >1), which grows much more quickly than an ...
This is an important technique for all types of time series analysis, especially for seasonal adjustment. [2] It seeks to construct, from an observed time series, a number of component series (that could be used to reconstruct the original by additions or multiplications) where each of these has a certain characteristic or type of behavior.