////  OpenForecast - open source, general-purpose forecasting package.//  Copyright (C) 2002-2004  Steven R. Gould////  This library is free software; you can redistribute it and/or//  modify it under the terms of the GNU Lesser General Public//  License as published by the Free Software Foundation; either//  version 2.1 of the License, or (at your option) any later version.////  This library is distributed in the hope that it will be useful,//  but WITHOUT ANY WARRANTY; without even the implied warranty of//  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU//  Lesser General Public License for more details.////  You should have received a copy of the GNU Lesser General Public//  License along with this library; if not, write to the Free Software//  Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA//package net.sourceforge.openforecast.models;import net.sourceforge.openforecast.DataSet;/** * A moving average forecast model is based on an artificially constructed * time series in which the value for a given time period is replaced by the * mean of that value and the values for some number of preceding and * succeeding time periods. As you may have guessed from the description, this * model is best suited to time-series data; i.e. data that changes over time. * For example, many charts of individual stocks on the stock market show 20, * 50, 100 or 200 day moving averages as a way to show trends. * * <p>Since the forecast value for any given period is an average of the * previous periods, then the forecast will always appear to "lag" behind * either increases or decreases in the observed (dependent) values. For * example, if a data series has a noticable upward trend then a moving average * forecast will generally provide an underestimate of the values of the * dependent variable. * * <p>The moving average method has an advantage over other forecasting models * in that it does smooth out peaks and troughs (or valleys) in a set of * observations. However, it also has several disadvantages. In particular this * model does not produce an actual equation. Therefore, it is not all that * useful as a medium-long range forecasting tool. It can only reliably be * used to forecast one or two periods into the future. * * <p>The moving average model is a special case of the more general weighted * moving average. In the simple moving average, all weights are equal. * @author Steven R. Gould * @since 0.3 */public class MovingAverageModel extends WeightedMovingAverageModel{    /**     * Constructs a new moving average forecasting model. For a valid model to     * be constructed, you should call init and pass in a data set containing     * a series of data points with the time variable initialized to identify     * the independent variable.     */    public MovingAverageModel()    {    }        /**     * Constructs a new moving average forecasting model, using the given name     * as the independent variable.     * @param independentVariable the name of the independent variable to use     * in this model.     * @deprecated As of 0.4, replaced by {@link #MovingAverageModel}.     */    public MovingAverageModel( String independentVariable )    {        super( independentVariable );    }        /**     * Constructs a new moving average forecasting model, using the specified     * period. For a valid model to be constructed, you should call init and     * pass in a data set containing a series of data points with the time     * variable initialized to identify the independent variable.     *     * <p>The period value is used to determine the number of observations to     * be used to calculate the moving average. For example, for a 50-day     * moving average where the data points are daily observations, then the     * period should be set to 50.     *     * <p>The period is also used to determine the amount of future periods     * that can effectively be forecast. With a 50 day moving average, then we     * cannot reasonably - with any degree of accuracy - forecast more than     * 50 days beyond the last period for which data is available. This may be     * more beneficial than, say a 10 day period, where we could only     * reasonably forecast 10 days beyond the last period.     * @param period the number of observations to be used to calculate the     * moving average.     */    public MovingAverageModel( int period )    {        double[] weights = new double[period];        for ( int p=0; p<period; p++ )            weights[p] = 1.0/period;                setWeights( weights );    }        /**     * Constructs a new moving average forecasting model, using the given name     * as the independent variable and the specified period.     * @param independentVariable the name of the independent variable to use     * in this model.     * @param period the number of observations to be used to calculate the     * moving average.     * @deprecated As of 0.4, replaced by {@link #MovingAverageModel(int)}.     */    public MovingAverageModel( String independentVariable, int period )    {        super( independentVariable );                double[] weights = new double[period];        for ( int p=0; p<period; p++ )            weights[p] = 1.0/period;                setWeights( weights );    }        /**     * Used to initialize the moving average model. This method must be     * called before any other method in the class. Since the moving     * average model does not derive any equation for forecasting, this     * method uses the input DataSet to calculate forecast values for all     * valid values of the independent time variable.     * @param dataSet a data set of observations that can be used to     * initialize the forecasting parameters of the forecasting model.     */    public void init( DataSet dataSet )    {        if ( getNumberOfPeriods() <= 0 )            {                // Number of periods has not yet been defined                //  - what's a reasonable number to use?                                // Use maximum number of periods as a default                int period = getNumberOfPeriods();                                // Set weights for moving average model                double[] weights = new double[period];                for ( int p=0; p<period; p++ )                    weights[p] = 1/period;                                setWeights( weights );            }                super.init( dataSet );    }        /**     * Returns a one or two word name of this type of forecasting model. Keep     * this short. A longer description should be implemented in the toString     * method.     * @return a string representation of the type of forecasting model     *         implemented.     */    public String getForecastType()    {        return "Moving average";    }        /**     * This should be overridden to provide a textual description of the     * current forecasting model including, where possible, any derived     * parameters used.     * @return a string representation of the current forecast model, and its     *         parameters.     */    public String toString()    {        return "Moving average model, spanning " + getNumberOfPeriods()            + " periods and using an independent variable of "            + getIndependentVariable()+".";    }}// Local Variables:// tab-width: 4// End: