| ▼ src | |
| ADF_comp_pvalue_util.hpp | Helper function and constant values to evaluate ADF-test p-value from the value of the statistic: how much the statistic has extreme negative values |
| ADF_comp_stat.hpp | Implement the class to compute Augmented Dickey-Fuller (ADF) test statistic |
| ADF_lr.hpp | Performing lineat regression |
| ADF_policies.hpp | Implement the policies, through functors and template parameter, for considering or not bigger lag orders to perform pointwisely Augmented Dickey-Fuller (ADF) test on the fts |
| ADF_test.hpp | Contains a class to perform pointwisely Augmented Dickey-Fuller (ADF) test on the fts |
| ADF_test_imp.hpp | Implement the class to perform pointwisely Augmented Dickey-Fuller (ADF) test on the fts |
| CV.hpp | Class for performing cross-validation |
| CV_alpha.hpp | Derived-from-CV_base class for performing cross-validation on the regularization parameter alpha |
| CV_alpha_k.hpp | Derived-from-CV_base class for performing cross-validation on both the regularization parameter and the number of retained PPCs |
| CV_err_valid_eval.hpp | Implementation of tag-dispatched function for evaluating the validation error |
| cv_eval_valid_err.hpp | Containing the definitions of the different ways of evaluating the validation error |
| CV_include.hpp | |
| CV_k.hpp | Derived-from-CV_base class for performing cross-validation on the number of retained PPCs |
| data_reader.hpp | Contains the method to read data |
| domain.cpp | Class unidimensioanl domain definition |
| domain.hpp | Contains the class for an unidimensioanl domain |
| Factory_cv_strategy.hpp | Factory for generating the training-validation splitting according to the strategy given |
| Factory_ko.hpp | Factory for generating the PPCKO solver |
| interp1D.hpp | Contains the template class for interpolating an unidimensional-univariate function in a given point |
| interp1D_util.hpp | Contains a specialization for the template class for interpolating an unidimensional-univariate function in a given point |
| interp_func.hpp | Contains a functor to perform unidimensional-univariate function interpolation |
| mesh.cpp | Contains the implementation of the class for an unidimensioanl mesh |
| mesh.hpp | Contains the class for an unidimensioanl mesh |
| meshGenerators.cpp | Contains implementation of the class for generating an unidimensional mesh. Little modification: retained only the part for an uniform mesh |
| meshGenerators.hpp | Contains the class for generating an unidimensional mesh. Little modification: retained only the part for an uniform mesh |
| parameters_wrapper.hpp | Contains methods to check and wrap R-inputs into PPCKO-coherent ones |
| PPC_KO.hpp | Class for computing PPCKO algortihm. Hierarchy of classes, for different algorithm versions. Static polymorphism |
| PPC_KO_CV_alpha.hpp | Class for computing PPCKO algortihm with cross-validation on the regularization parameter: k can be a parameter or selected through explanatory power criterion |
| PPC_KO_CV_alpha_k.hpp | Class for computing PPCKO algortihm with cross-validation on both regularization parameter and number of retained PPCs |
| PPC_KO_CV_k.hpp | Class for computing PPCKO algortihm with cross-validation on the number of retained PPCs: regularization parameter is a passed parameter |
| PPC_KO_imp.hpp | Definition of methods of the base class for computing PPCKO |
| PPC_KO_include.hpp | |
| PPC_KO_NoCV.hpp | Class for computing PPCKO algortihm without cross-validation: alpha as parameter, k can be a parameter or selected through explanatory power criterion |
| PPC_KO_Rinterface.cpp | Contains the R-interfaced functions of the package 'PPCKO', which implement Principal Predictive Components (PPC) Kargin-Onatski (KO) algorithm to perform one-step ahead prediction of Functional Time Series (FTS) |
| PPC_KO_wrapper.hpp | Hierarchy of classes, for different algorithm versions. Correct child class is constructed by the KO_factory, providing the solver. Wrap the real class for computations |
| PPC_KO_wrapper_imp.hpp | Implementation of the overriding of the virtual base method, depending on the child class |
| removing_nan.hpp | Contains the class to remove (dummy and not dummy) NaNs |
| removing_nan_cleaner_imp.hpp | Implementation of dummy NaNs removal |
| removing_nan_imp.hpp | Implementation of non-dummy NaNs removal |
| strategy_cv.hpp | Contains the class for creating the split training/validation set |
| strategy_cv_imp.hpp | Definition of methods for creating training/validation splitting and recovering corresponding training/validation sets |
| traits_ko.hpp | Contains customized types and enumerator for customized template parameters, exploited in the algorithm |
| utils.hpp | Contains helpers functions |
1.13.1