/*!
 * forecast.js
 * Copyright (C) 2012 Eric Lippmann
 * forecast.js may be freely distributed under the MIT license.
 */

/**
 * Time series forecasting models.
 */
; (function () {
    'use strict';

    var // Reference to `window` in the browser, `exports`, `global` on
        // the server
        root = this;

    function forecast () {
        if (!(this instanceof forecast)) {
            return new forecast();
        }
    }

    root.forecast = forecast();
}.call(this));

@@ -434,16 +434,2026 @@
 %0A    %7D%0A%0A
+    /**%0A     * Returns forecasts for the next %60c%60 periods of time series data%0A     * %60oberservations%60 using the (Holt-Winter's) additive triple exponential%0A     * smoothing model.%0A     *%0A     * @param %7BNumber%5B%5D%7D observations Time series data (a one dimensional array of y-values)%0A     * @param %7BNumber%7D lengthOfSeason%0A     * @param %7BNumber%7D c Number of periods in the seasonal pattern ahead to forecast%0A     * @param %7BObject%7D smoothingConstants%0A     * @returns %7BNumber%5B%5D%7D Forecast for the next %60c%60 periods%0A     */%0A    forecast.prototype.HoltWinters = function (observations, lengthOfSeason, c,%0A                                               smoothingConstants%0A    ) %7B%0A        /*!%0A         * The additive Holt-Winter's prediction model is denoted by the%0A         * following equations:%0A         *%0A         * Lt = a * ( Yt - St-c ) + ( 1 - a ) * ( Lt-1 + Tt-1 )%0A         * Tt = b * ( Lt - Lt-1 ) + ( 1 - b ) * Tt-1%0A         * St = y * ( Yt - Lt ) + ( 1 - y ) * St-c%0A         * Ft+h = Lt + h * Tt + St-p+1+(h-1)%25p%0A         * %0A         * **%0A         * %0A         * The multiplicative Holt-Winter's prediction model is denoted by the%0A         * following equations:%0A         *%0A         * Lt = a * ( Yt / St-c ) + ( 1 - a ) * ( Lt-1 + Tt-1 )%0A         * Tt = b * ( Lt - Lt-1 ) + ( 1 - b ) * Tt-1%0A         * St = y * ( Yt / Lt ) + ( 1 - y ) * St-c%0A         * Ft+h = ( Lt + h * Tt) * St-p+1+(h-1)%25p%0A         *%0A         * Where:%0A         *%0A         * Lt is the deseasonalized level at time t%0A         * a is the smoothing constant alpha, used to smooth Lt%0A         * Yt is the observed value at time t%0A         * St is the seasonal index/component at time t%0A         * c is the number of periods in the seasonal pattern ahead to forecast%0A         * Tt is the trend of the series at time t%0A         * b is the Smoothing constant beta, used to smooth Tt%0A         * y is the Smoothing constant gamma, used to Smoot St%0A         * Ft+h is the forecast for period h%0A         * p is the length of one seasons%0A         */%0A    %7D%0A%0A
     root

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