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    exponential-smoothing-stream

    3.0.0 • Public • Published

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    Exponential Smoothing

    You may have seen graphs like this (e.g. stock market):

    The algorithm that is used to calculate the smoothed graphs is called "exponential smoothing" and can be described via this formula (source: Wikipedia):

    Module

    This module implements a stream that takes values and transforms them via the above mentioned formula.

    Installation

    $ npm install exponential-smoothing-stream

    Invocation

    var ESS = require('exponential-smoothing-stream');
     
    var a = new ESS({
        smoothingFactor: 0.5
    });
     
    var valueList = [];
     
    a.write(2);
    a.write(2);
    a.write(3);
    a.write(2);
    a.write(1);
     
    a.end();
     
    a.on('data', function(data) {
        valueList.push(data);
    });
     
    a.on('end', function() {
        //value list now equals: [2, 2, 2.5, 2.25, 1.625]
    });

    Constructor(options)

    Creates a new exponential-smoothing-stream. Takes an optional configuration hash that consists of the following elements:

    • smoothingFactor: Defines the smoothing factor
    • initialStrategy: Defines the strategy that is used to determine the initial value of the series, defaults to the First strategy

    Initial Strategy

    The strategy the is used for the first series element is expressed via a Strategy instance. Built in there are five different strategies:

    First

    Uses the first element from the series to determine the first smoothed value.

    var ESS = require('exponential-smoothing-stream');
     
    var a = new ESS({
        smoothingFactor: 0.5,
        initialStrategy: new ESS.strategies.InitialStrategyFirst()
    });

    Fixed

    Uses a fixed value that is used as the smoothed value.

    Uses the first element from the series to determine the first smoothed value.

    var ESS = require('exponential-smoothing-stream');
     
    var a = new ESS({
        smoothingFactor: 0.5,
        initialStrategy: new ESS.strategies.InitialStrategyFixed(10)
    });

    Percentage

    Uses a percentage value (of the first element of the series) as the first smoothed value.

    var ESS = require('exponential-smoothing-stream');
     
    var a = new ESS({
        smoothingFactor: 0.5,
        initialStrategy: new ESS.strategies.InitialStrategyPercentage(0.7) //has to be an positive number that's smaller than 1
    });

    Average

    Uses the first n values and calculates the average which will be used as the initial stream value. The stream itself will queue up the stream values until the strategy returns with a valid number.

    var ESS = require('exponential-smoothing-stream');
     
    var a = new ESS({
        smoothingFactor: 0.5,
        initialStrategy: new ESS.strategies.InitialStrategyAverage(4) //will use the first four elements from the stream to determine the average
    });

    Median

    Uses the first n values and calculates the median which will be used as the initial stream value. The stream itself will queue up the stream values until the strategy returns with a valid number.

    var ESS = require('exponential-smoothing-stream');
     
    var a = new ESS({
        smoothingFactor: 0.5,
        initialStrategy: new ESS.strategies.InitialStrategyMedian(4) //will use the first four elements from the stream to determine the median
    });

    Custom

    If you want to specify your own logic simply create a class that follows this interface

    function InitialStrategyCustom () {
     
    }
     
    /**
     * determines the first value of a smoothed series based on the first series element
     *
     * @param value Number the first element of the series
     * @returns Number
     */
    InitialStrategyCustom.prototype.determine = function (value) {
        return value;
    };

    If the call to determine returns null the stream will buffer all stream values until determine returns a number. This allows e.g. for an initial value that's the average of the first four stream elements.

    Smoothing Factor

    It is tricky to determine exactly which smoothing factor is the best fitting factor. To get a rough idea on how the smoothing factor effects the smoothed series this table can be used:

    On each row a certain smoothing factor is listed, the belonging columns show how many steps it takes until only a certain percentag of the initial value is left.

    factor 10% 25% 50% 75% 90%
    0,75 8 5 3 1
    0,8 10 6 3 1
    0,85 14 8 4 2 1
    0,9 21 13 7 3 1
    0,93 31 19 9 4 1
    0,95 44 27 13 6 2
    0,96 56 34 17 7 2
    0,97 75 45 23 9 3
    0,98 98 68 34 14 5
    0,985 150 91 45 19 7
    0,987 175 109 53 22 8
    0,989 199 119 60 25 9
    0,99 228 138 69 29 10
    0,993 327 197 99 41 14
    0,995 449 277 139 58 21
    0,997 766 460 230 95 33

    Install

    npm i exponential-smoothing-stream

    DownloadsWeekly Downloads

    41

    Version

    3.0.0

    License

    MIT

    Last publish

    Collaborators

    • zaphod1984