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Exponential smoothing or exponential moving average (EMA) is a rule of thumb technique for smoothing time series data using the exponential window function. Whereas in the simple moving average the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time. It is an easily learned ...
Like any moving average, the triple EMA is just a smoothing of price data and, therefore, is trend-following. A rising or falling line is an uptrend or downtrend and Trix shows the slope of that line, so it's positive for a steady uptrend, negative for a downtrend, and a crossing through zero is a trend-change, i.e. a peak or trough in the ...
The trend-cycle component can just be referred to as the "trend" component, even though it may contain cyclical behavior. [3] For example, a seasonal decomposition of time series by Loess (STL) [ 4 ] plot decomposes a time series into seasonal, trend and irregular components using loess and plots the components separately, whereby the cyclical ...
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;
When the simple moving median above is central, the smoothing is identical to the median filter which has applications in, for example, image signal processing. The Moving Median is a more robust alternative to the Moving Average when it comes to estimating the underlying trend in a time series.
These methods are usually applied to short- or intermediate-range decisions. Examples of quantitative forecasting methods are [citation needed] last period demand, simple and weighted N-Period moving averages, simple exponential smoothing, Poisson process model based forecasting [15] and multiplicative seasonal indexes. Previous research shows ...
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.
Examples of Time Series Methods are: [2] Exponential smoothing – This method is based on a moving average of the data being analyzed, e.g. a moving average of sales figures; Cyclical and seasonal trends – This method focuses on previous data to help define a pattern or trend that occurs in cyclic or seasonal periods. Researchers can then ...