PreprocessingModel implements IProcResults { public static Map<String, Class> dictionary(boolean compact) { LinkedHashMap<String, Class> map = new LinkedHashMap<>(); fillDictionary(null, map, compact); return map; } PreprocessingModel(ModelDescription description, ModelEstimation estimation); int getNcasts(); void setNcasts(int ncasts); static ComponentType outlierComponent(OutlierType type); static OutlierType[] outlierTypes(ComponentType cmp); void backTransform(TsData s, boolean T, boolean S); void updateModel(); void addProcessingInformation(ProcessingInformation info); void addProcessingInformation(Collection<ProcessingInformation> info); IFunction likelihoodFunction(); IFunctionInstance maxLikelihoodFunction(); MissingValueEstimation[] missings(boolean unbiased); TsData interpolatedSeries(boolean bTransformed); TsData linearizedSeries(); TsData linearizedSeries(boolean includeUndefinedReg); TsData preadjustmentEffect(TsDomain domain); TsData preadjustmentEffect(TsDomain domain, final ComponentType type); TsData regressionEffect(TsDomain domain); TsData preadjustmentEffect(TsDomain domain, Class<T> tclass); TsData regressionEffect(TsDomain domain, Class<T> tclass); TsData preadjustmentEffect(TsDomain domain, Predicate<PreadjustmentVariable> selector); TsData deterministicEffect(TsDomain domain); TsData deterministicEffect(TsDomain domain, Predicate<ITsVariable> selector); TsData deterministicEffect(TsDomain domain, Class<T> tclass); TsData tradingDaysEffect(TsDomain domain); TsData movingHolidaysEffect(TsDomain domain); TsData outliersEffect(TsDomain domain); TsData outliersEffect(TsDomain domain, final ComponentType type); OutlierEstimation[] outliersEstimation(boolean unbiased, boolean prespecified); List<TsData> regressors(TsDomain domain); TsData deterministicEffect(TsDomain domain, final ComponentType type); TsData userEffect(TsDomain domain, final ComponentType type); TsData userEffect(TsDomain domain); TsData linearizedForecast(int nf); TsData linearizedForecast(int nf, boolean includeUndefinedReg); TsData linearizedBackcast(int nf, boolean includeUndefinedReg); Forecasts forecasts(int nf); TsData linearizedBackcast(int nb); TsData forecast(int nf, boolean transformed); TsData backcast(int nb, boolean transformed); DeterministicComponent getDeterministicComponent(); @Override Map<String, Class> getDictionary(); @Override T getData(String id, Class<T> tclass
    ); @Override Map<String, T> searchAll(String wc, Class<T> tclass
    ); @Override boolean contains(String id
    ); @Override List<ProcessingInformation> getProcessingInformation(); static void fillDictionary(String prefix, Map<String, Class> map, boolean compact); static Map<String, Class> dictionary(boolean compact); TsData getForecastError(); TsData getFullResiduals(); boolean isMultiplicative(); TsFrequency getFrequency(); RegressionItem getRegressionItem(Class<V> tclass, int vpos); static InformationMapping<PreprocessingModel> getMapping(); static void setMapping(String name, Class<T> tclass, Function<PreprocessingModel, T> extractor); static void setTsData(String name, Function<PreprocessingModel, TsData> extractor); final ModelDescription description; final ModelEstimation estimation; public InformationSet info_; static final String LOG; }
@Test @Ignore public void demoDictionary() { Map<String, Class> dic = PreprocessingModel.dictionary(false); for (Entry<String, Class> o : dic.entrySet()) { System.out.print(o.getKey()); System.out.print('\t'); System.out.println(o.getValue().getSimpleName()); } dic = PreprocessingModel.dictionary(true); for (Entry<String, Class> o : dic.entrySet()) { System.out.print(o.getKey()); System.out.print('\t'); System.out.println(o.getValue().getSimpleName()); } }