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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 ...
An exponential moving average (EMA), also known as an exponentially weighted moving average (EWMA), [5] is a first-order infinite impulse response filter that applies weighting factors which decrease exponentially. The weighting for each older datum decreases exponentially, never reaching zero. This formulation is according to Hunter (1986). [6]
The simple moving average, or SMA, is one of the most common pieces of technical data that investors rely on. In the case of the 200-day SMA, it shows you the stock's average price over the past ...
Laplacian smoothing: algorithm to smooth a polygonal mesh. [4] [5] Local regression also known as "loess" or "lowess" a generalization of moving average and polynomial regression. fitting simple models to localized subsets of the data to build up a function that describes the deterministic part of the variation in the data, point by point
Smooth that series using another N-day EMA. Smooth a third time, using a further N-day EMA. Calculate the percentage difference between today's and yesterday's value in that final smoothed series. 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 ...
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Moving average crossover of a 15-day exponential close-price MA (red) crossing over a 50-day exponential close-price MA (yellow) In the statistics of time series, and in particular the stock market technical analysis, a moving-average crossover occurs when, on plotting two moving averages each based on different degrees of smoothing, the traces of these moving averages cross.
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