List of usage examples for weka.classifiers.timeseries WekaForecaster forecast
@Override public List<List<NumericPrediction>> forecast(int numSteps, PrintStream... progress) throws Exception
From source file:org.datahack.forecast.BayExample.java
public static void main(String[] args) { try {/*ww w .j a v a2 s . co m*/ // path to the Australian wine data included with the time series forecasting // package File output = File.createTempFile("tempWineData", "arff"); output.deleteOnExit(); String dataFileName = "sample-data/wine.arff"; URL resource = BayExample.class.getResource("/" + dataFileName); FileUtils.copyURLToFile(resource, output); String pathToWineData = output.getPath(); // load the wine data Instances wine = new Instances(new BufferedReader(new FileReader(pathToWineData))); InstanceQuery q = new InstanceQuery(); // new forecaster WekaForecaster forecaster = new WekaForecaster(); // set the targets we want to forecast. This method calls // setFieldsToLag() on the lag maker object for us forecaster.setFieldsToForecast("Fortified,Dry-white"); // default underlying classifier is SMOreg (SVM) - we'll use // gaussian processes for regression instead forecaster.setBaseForecaster(new GaussianProcesses()); forecaster.getTSLagMaker().setTimeStampField("Date"); // date time stamp forecaster.getTSLagMaker().setMinLag(1); forecaster.getTSLagMaker().setMaxLag(12); // monthly data // add a month of the year indicator field forecaster.getTSLagMaker().setAddMonthOfYear(true); // add a quarter of the year indicator field forecaster.getTSLagMaker().setAddQuarterOfYear(true); // build the model forecaster.buildForecaster(wine, System.out); // prime the forecaster with enough recent historical data // to cover up to the maximum lag. In our case, we could just supply // the 12 most recent historical instances, as this covers our maximum // lag period forecaster.primeForecaster(wine); // forecast for 12 units (months) beyond the end of the // training data List<List<NumericPrediction>> forecast = forecaster.forecast(12, System.out); // output the predictions. Outer list is over the steps; inner list is over // the targets for (int i = 0; i < 12; i++) { List<NumericPrediction> predsAtStep = forecast.get(i); for (int j = 0; j < 2; j++) { NumericPrediction predForTarget = predsAtStep.get(j); System.out.print("" + predForTarget.predicted() + " "); } System.out.println(); } // we can continue to use the trained forecaster for further forecasting // by priming with the most recent historical data (as it becomes available). // At some stage it becomes prudent to re-build the model using current // historical data. } catch (Exception ex) { ex.printStackTrace(); } }
From source file:org.datahack.forecast.TimeSeriesExample.java
public static void main(String[] args) { try {/*from w w w .j ava 2s . c o m*/ // path to the Australian wine data included with the time series forecasting // package File output = File.createTempFile("tempWineData", "arff"); output.deleteOnExit(); String dataFileName = "sample-data/parking344.arff"; URL resource = TimeSeriesExample.class.getResource("/" + dataFileName); FileUtils.copyURLToFile(resource, output); String pathToWineData = output.getPath(); // load the wine data Instances wine = new Instances(new BufferedReader(new FileReader(pathToWineData))); // new forecaster WekaForecaster forecaster = new WekaForecaster(); // set the targets we want to forecast. This method calls // setFieldsToLag() on the lag maker object for us forecaster.setFieldsToForecast("occupiedSpaces"); // default underlying classifier is SMOreg (SVM) - we'll use // gaussian processes for regression instead forecaster.setBaseForecaster(new GaussianProcesses()); forecaster.getTSLagMaker().setTimeStampField("eventTime"); // date time stamp forecaster.getTSLagMaker().setMinLag(1); forecaster.getTSLagMaker().setMaxLag(12); // monthly data // add a month of the year indicator field forecaster.getTSLagMaker().setAddMonthOfYear(true); // add a quarter of the year indicator field forecaster.getTSLagMaker().setAddQuarterOfYear(true); // build the model forecaster.buildForecaster(wine, System.out); // prime the forecaster with enough recent historical data // to cover up to the maximum lag. In our case, we could just supply // the 12 most recent historical instances, as this covers our maximum // lag period forecaster.primeForecaster(wine); // forecast for 12 units (months) beyond the end of the // training data List<List<NumericPrediction>> forecast = forecaster.forecast(12, System.out); // output the predictions. Outer list is over the steps; inner list is over // the targets for (int i = 0; i < 12; i++) { List<NumericPrediction> predsAtStep = forecast.get(i); for (int j = 0; j < 2; j++) { NumericPrediction predForTarget = predsAtStep.get(j); System.out.print("" + predForTarget.predicted() + " "); } System.out.println(); } // we can continue to use the trained forecaster for further forecasting // by priming with the most recent historical data (as it becomes available). // At some stage it becomes prudent to re-build the model using current // historical data. } catch (Exception ex) { ex.printStackTrace(); } }
From source file:Prediccion.Prediccion.java
License:Open Source License
@Override public void run() { try {//from w w w . j av a 2 s . c om ArrayList<Instances> pasos = cargarDatos(); System.err.println(pasos.size()); //Instanciamos el predictor ArrayList<WekaForecaster> forecaster = new ArrayList<>(24); for (int a = 0; a < 24; a++) { forecaster.add(new WekaForecaster()); } int a = 0; for (WekaForecaster fore : forecaster) { //Defimimos el atributo que queremos predecir fore.setFieldsToForecast("Total"); //Definimos el mtodo de prediccin a emplear. En este caso, regresin lineal porque //en el artculo es el que mejor ha funcionado fore.setBaseForecaster(new LinearRegression()); //Defimimos el atributo que "marca" el tiempo y su peridiocidad fore.getTSLagMaker().setTimeStampField("Intervalo"); fore.getTSLagMaker().setMinLag(1); fore.getTSLagMaker().setMaxLag(1); fore.getTSLagMaker().setPeriodicity(TSLagMaker.Periodicity.WEEKLY); fore.buildForecaster(pasos.get(a), System.out); // System.err.println(pasos.get(a).toString()); //System.err.printf("Termin"); fore.primeForecaster(pasos.get(a)); List<List<NumericPrediction>> forecast = fore.forecast(1, System.out); System.err.println("==== " + a + " ===="); // output the predictions. Outer list is over the steps; inner list is over // the targets for (int i = 0; i < 1; i++) { List<NumericPrediction> predsAtStep = forecast.get(i); for (int j = 0; j < 1; j++) { NumericPrediction predForTarget = predsAtStep.get(j); System.err.print("" + predForTarget.predicted() + " "); } System.err.println(); } a++; } /* // path to the Australian wine data included with the time series forecasting // package String pathToWineData = weka.core.WekaPackageManager.PACKAGES_DIR.toString() + File.separator + "timeseriesForecasting" + File.separator + "sample-data" + File.separator + "wine.arff"; // load the wine data Instances wine = new Instances(new BufferedReader(new FileReader(pathToWineData))); // new forecaster WekaForecaster forecaster = new WekaForecaster(); // set the targets we want to forecast. This method calls // setFieldsToLag() on the lag maker object for us forecaster.setFieldsToForecast("Fortified,Dry-white"); // default underlying classifier is SMOreg (SVM) - we'll use // gaussian processes for regression instead forecaster.setBaseForecaster(new GaussianProcesses()); forecaster.getTSLagMaker().setTimeStampField("Date"); // date time stamp forecaster.getTSLagMaker().setMinLag(1); forecaster.getTSLagMaker().setMaxLag(12); // monthly data // add a month of the year indicator field forecaster.getTSLagMaker().setAddMonthOfYear(true); // add a quarter of the year indicator field forecaster.getTSLagMaker().setAddQuarterOfYear(true); // build the model forecaster.buildForecaster(wine, System.out); // prime the forecaster with enough recent historical data // to cover up to the maximum lag. In our case, we could just supply // the 12 most recent historical instances, as this covers our maximum // lag period forecaster.primeForecaster(wine); // forecast for 12 units (months) beyond the end of the // training data <<List<List<NumericPrediction>> forecast = forecaster.forecast(12, System.out); // output the predictions. Outer list is over the steps; inner list is over // the targets for (int i = 0; i < 12; i++) { List<NumericPrediction> predsAtStep = forecast.get(i); for (int j = 0; j < 2; j++) { NumericPrediction predForTarget = predsAtStep.get(j); System.out.print("" + predForTarget.predicted() + " "); } System.out.println(); } // we can continue to use the trained forecaster for further forecasting // by priming with the most recent historical data (as it becomes available). // At some stage it becomes prudent to re-build the model using current // historical data. */ } catch (Exception ex) { ex.printStackTrace(); } }