timeseries.models.Model Maven / Gradle / Ivy

Go to download
Show more of this group Show more artifacts with this name
Show all versions of java-timeseries
Time Series Analysis in Java
There is a newer version: 0.2.1
/*
 * Copyright (c) 2016 Jacob Rachiele
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy of this software
 * and associated documentation files (the "Software"), to deal in the Software without restriction
 * including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense
 * and/or sell copies of the Software, and to permit persons to whom the Software is furnished to
 * do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all copies or
 * substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED
 * INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR
 * PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
 * LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
 * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE
 * USE OR OTHER DEALINGS IN THE SOFTWARE.
 *
 * Contributors:
 *
 * Jacob Rachiele
 */
package timeseries.models;

import timeseries.TimeSeries;

/**
 * A time series model. An attempt to capture the most important characteristics of the underlying process(es).
 *
 * @author Jacob Rachiele
 *
 */
public interface Model {

  /**
   * Produce a time series of point forecasts from this model up to the given number of steps ahead.
   * 
   * To obtain additional information about the forecast, such as prediction intervals, use the {@link #forecast}
   * method and the resulting {@link Forecast} object.
   * 
   *
   * @param steps the number of time periods ahead to forecast.
   * @return a time series of point forecasts from this model up to the given number of steps ahead.
   */
  TimeSeries pointForecast(int steps);

  /**
   * Produce a new forecast up to the given number of steps and with the given α significance level for
   * computing prediction intervals.
   * @param steps the number of time periods ahead to forecast.
   * @param alpha the probability that a future observation will fall outside the associated (1 - α)100%
   * prediction interval, given that the model is "correct". Note that the correctness of the model often comes with
   * a high degree of uncertainty and this should be taken into account when making decisions. In other words, the
   * provided prediction intervals will often be overly optimistic.
   * @return a new forecast up to the given number of steps ahead with the given significance level.
   */
  Forecast forecast(int steps, double alpha);

  /**
   * Get the series of observations.
   * @return the series of observations.
   */
  TimeSeries timeSeries();

  /**
   * Get the model fitted values, which are in-sample one-step ahead forecasts.
   * @return the model fitted values.
   */
  TimeSeries fittedSeries();

  /**
   * Get the model residuals, the difference between the observed values and the model fitted values.
   * @return the model residuals.
   */
  TimeSeries residuals();

  /**
   * Plot the model fit, which often displays the model fitted values and the observations in the same plot area.
   */
  void plotFit();

  /**
   * Plot the model residuals.
   */
  void plotResiduals();

}