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  2. Moving average - Wikipedia

    en.wikipedia.org/wiki/Moving_average

    In statistics, a moving average (rolling average or running average or moving mean [1] or rolling mean) is a calculation to analyze data points by creating a series of averages of different selections of the full data set. Variations include: simple, cumulative, or weighted forms. Mathematically, a moving average is a type of convolution.

  3. Exponential smoothing - Wikipedia

    en.wikipedia.org/wiki/Exponential_smoothing

    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 ...

  4. Moving-average model - Wikipedia

    en.wikipedia.org/wiki/Moving-average_model

    In time series analysis, the moving-average model (MA model), also known as moving-average process, is a common approach for modeling univariate time series. [ 1 ] [ 2 ] The moving-average model specifies that the output variable is cross-correlated with a non-identical to itself random-variable.

  5. Autoregressive moving-average model - Wikipedia

    en.wikipedia.org/wiki/Autoregressive_moving...

    The notation ARMAX(p, q, b) refers to a model with p autoregressive terms, q moving average terms and b exogenous inputs terms. The last term is a linear combination of the last b terms of a known and external time series d t {\displaystyle d_{t}} .

  6. Double exponential moving average - Wikipedia

    en.wikipedia.org/wiki/Double_exponential_moving...

    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.

  7. Savitzky–Golay filter - Wikipedia

    en.wikipedia.org/wiki/Savitzky–Golay_filter

    These functions are shown in the plot at the right. For example, with a 9-point linear function (moving average) two thirds of the noise is removed and with a 9-point quadratic/cubic smoothing function only about half the noise is removed. Most of the noise remaining is low-frequency noise(see Frequency characteristics of convolution filters, below

  8. Moving average crossover - Wikipedia

    en.wikipedia.org/wiki/Moving_average_crossover

    This indicator uses two (or more) moving averages, a slower moving average and a faster moving average. The faster moving average is a short term moving average. For end-of-day stock markets, for example, it may be 5-, 10- or 25-day period while the slower moving average is medium or long term moving average (e.g. 50-, 100- or 200-day period).

  9. Rolling hash - Wikipedia

    en.wikipedia.org/wiki/Rolling_hash

    A rolling hash (also known as recursive hashing or rolling checksum) is a hash function where the input is hashed in a window that moves through the input.. A few hash functions allow a rolling hash to be computed very quickly—the new hash value is rapidly calculated given only the old hash value, the old value removed from the window, and the new value added to the window—similar to the ...