////  OpenForecast - open source, general-purpose forecasting package.//  Copyright (C) 2002  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;/** * Defines a consistent interface that must be implemented by all Forecasting * Models. Note that any forecasting model should first be initialized by * calling init. Once init has been called, any of the other methods can be * expected to return reasonable results. * @author Steven R. Gould */public interface ForecastingModel{	/**	 * Used to initialize the model-specific parameters and customize them	 * to the given data set. This method must be called before any other	 * method in the class.	 * @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 );	/**	 * Returns the bias - the arithmetic mean of the errors - obtained from	 * applying the current forecasting model to the initial data set to try	 * and predict each data point. The result is an indication of the accuracy	 * of the model when applied to your initial data set - the closer the bias	 * is to zero, the more accurate the model.	 * @return the bias - mean of the errors - when the current model was	 *         applied to the initial data set.	 */	public double getBias();	/**	 * Returns the mean absolute deviation obtained from applying the current	 * forecasting model to the initial data set to try and predict each data	 * point. The result is an indication of the accuracy of the model when	 * applied to your initial data set - the smaller the Mean Absolute	 * Deviation (MAD), the more accurate the model.	 * @return the mean absolute deviation (MAD) when the current model was	 *         applied to the initial data set.	 */	public double getMAD();	/**	 * Returns the mean absolute percentage error obtained from applying the	 * current forecasting model to the initial data set to try and predict	 * each data point. The result is an indication of the accuracy of the	 * model when applied to the initial data set - the smaller the Mean	 * Absolute Percentage Error (MAPE), the more accurate the model.	 * @return the mean absolute percentage error (MAPE) when the current model	 *         was applied to the initial data set.	 */	public double getMAPE();	/**	 * Returns the mean square of the errors (MSE) obtained from applying the	 * current forecasting model to the initial data set to try and predict	 * each data point. The result is an indication of the accuracy of the	 * model when applied to your initial data set - the smaller the Mean	 * Square of the Errors, the more accurate the model.	 * @return the mean square of the errors (MSE) when the current model was	 *         applied to the initial data set.	 */	public double getMSE();	/**	 * Returns the sum of the absolute errors (SAE) obtained from applying the	 * current forecasting model to the initial data set to try and predict	 * each data point. The result is an indication of the accuracy of the	 * model when applied to your initial data set - the smaller the Sum of	 * Absolute Errors, the more accurate the model.	 * @return the sum of the absolute errors (SAE) when the current model was	 *         applied to the initial data set.	 */	public double getSAE();	/**	 * Using the current model parameters (initialized in init), apply the	 * forecast model to the given data point. The data point must have valid	 * values for the independent variables. Upon return, the value of the	 * dependent variable will be updated with the forecast value computed for	 * that data point.	 * @param dataPoint the data point for which a forecast value (for the	 *        dependent variable) is required.	 * @return the same data point passed in but with the dependent value	 *         updated to contain the new forecast value.	 */	public double forecast( DataPoint dataPoint );	/**	 * Using the current model parameters (initialized in init), apply the	 * forecast model to the given data set. Each data point in the data set	 * must have valid values for the independent variables. Upon return, the	 * value of the dependent variable will be updated with the forecast	 * values computed.	 * @param dataSet the set of data points for which forecast values (for	 *        the dependent variable) are required.	 * @return the same data set passed in but with the dependent values	 *         updated to contain the new forecast values.	 */	public DataSet forecast( DataSet 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();	/**	 * 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();}