opennn::TimeSeriesDataset Class Reference

Dataset specialization for time series with explicit past / future windows. More...

#include <time_series_dataset.h>

Inheritance diagram for opennn::TimeSeriesDataset:

Public Member Functions
	TimeSeriesDataset (const Index samples_number=0, const Shape &input_shape={}, const Shape &target_shape={})
	Constructs an empty TimeSeriesDataset of given dimensions.
	TimeSeriesDataset (const filesystem::path &path, const string &separator, bool has_header=true, bool has_sample_index=false, const Codification &codification=Codification::UTF8)
	Constructs a TimeSeriesDataset by loading from a CSV file.
void	fill_gaps ()
	Fills missing time steps with imputed rows so the time variable becomes evenly spaced.
Index	get_past_time_steps () const
	Number of past time steps used as inputs.
Index	get_future_time_steps () const
	Number of future time steps used as targets.
Index	get_time_variable_index () const
	Column index of the time variable.
bool	get_multi_target () const
	Whether the dataset has more than one target variable.
Tensor3	get_data (const string &sample_role, const string &feature_role) const
	Returns the data tensor for the given sample / feature roles.
void	set_past_time_steps (const Index)
	Sets the input window length.
void	set_future_time_steps (const Index)
	Sets the forecast horizon.
void	set_time_variable_index (const Index)
	Sets the column index of the time variable.
void	set_multi_target (const bool)
	Sets whether the dataset has more than one target variable.
MatrixR	calculate_autocorrelations (const Index maximum_lag=10) const
	Computes the autocorrelation function of every variable.
Tensor3	calculate_cross_correlations (const Index maximum_lag=10) const
	Computes the Pearson cross-correlation between every variable pair.
Tensor3	calculate_cross_correlations_spearman (const Index maximum_lag=10) const
	Computes the Spearman cross-correlation between every variable pair.
void	to_JSON (JsonWriter &) const override
	Writes dataset metadata (windows, time variable) to JSON.
void	from_JSON (const JsonDocument &) override
	Loads dataset metadata (windows, time variable) from JSON.
void	read_csv () override
	Reads time-series rows from the configured CSV file.
void	impute_missing_values_unuse () override
	Marks rows with missing values as None (excluded from training).
void	impute_missing_values_interpolate () override
	Imputes missing values via temporal interpolation.
void	fill_inputs (const vector< Index > &, const vector< Index > &, float *, bool=true, int contiguous=-1) const override
	Fills the input batch buffer with past-window data.
void	fill_targets (const vector< Index > &, const vector< Index > &, float *, bool=true, int contiguous=-1) const override
	Fills the target batch buffer with future-window data.
Public Member Functions inherited from opennn::Dataset
	Dataset (const Index samples_number=0, const Shape &input_shape={0}, const Shape &target_shape={0})
	Constructs an empty dataset of given dimensions.
	Dataset (const filesystem::path &data_path, const string &separator, bool has_header=true, bool has_ids=false, const Codification &codification=Codification::UTF8)
	Constructs a dataset by loading a delimited text file.
Index	get_samples_number () const
	Returns the total number of samples (rows of data).
Index	get_samples_number (const string &role_name) const
	Returns the number of samples assigned to a given role.
Index	get_used_samples_number () const
	Returns the number of samples that are not "None".
vector< Index >	get_sample_indices (const string &role_name) const
	Returns the indices of the samples assigned to a given role.
vector< Index >	get_used_sample_indices () const
	Returns the indices of all samples that are not "None".
const vector< SampleRole > &	get_sample_roles () const
	Returns the per-sample role assignments.
vector< Index >	get_sample_roles_vector () const
	Returns the per-sample role indices as plain integers.
VectorI	get_sample_role_numbers () const
	Counts the samples assigned to each role.
Index	get_variables_number () const
	Returns the total number of variables (columns of data).
Index	get_variables_number (const string &role_name) const
	Returns the number of variables assigned to a given role.
Index	get_used_variables_number () const
	Returns the number of variables that are not "Unused".
const vector< Variable > &	get_variables () const
	Returns the per-variable metadata.
vector< Variable >	get_variables (const string &role_name) const
	Returns the variables assigned to a given role.
Index	get_variable_index (const string &name) const
	Returns the column index of the variable with a given name.
Index	get_variable_index (const Index id) const
	Returns the column index of the variable with a given numeric id.
vector< Index >	get_variable_indices (const string &role_name) const
	Returns the column indices of the variables assigned to a given role.
vector< Index >	get_used_variables_indices () const
	Returns the column indices of all variables that are not "Unused".
vector< string >	get_variable_names () const
	Returns the names of every variable.
vector< string >	get_variable_names (const string &role_name) const
	Returns the names of the variables assigned to a given role.
VariableType	get_variable_type (const Index index) const
	Returns the type of a variable (Numeric, Binary, Categorical, ...).
vector< VariableType >	get_variable_types (const vector< Index > indices) const
	Returns the types of a list of variables.
Index	get_features_number () const
	Returns the total number of features.
Index	get_features_number (const string &role_name) const
	Returns the number of features assigned to a given role.
Index	get_used_features_number () const
	Returns the number of features that are not "Unused".
vector< string >	get_feature_names () const
	Returns the names of every feature.
vector< string >	get_feature_names (const string &role_name) const
	Returns the names of the features assigned to a given role.
vector< vector< Index > >	get_feature_indices () const
	Returns the per-variable feature indices.
vector< Index >	get_feature_indices (const Index variable_index) const
	Returns the feature indices for a single variable.
vector< Index >	get_feature_indices (const string &role_name) const
	Returns the feature indices for variables of a given role.
vector< Index >	get_used_feature_indices () const
	Returns the feature indices for all variables that are not "Unused".
vector< Index >	get_feature_dimensions () const
	Returns the per-variable feature dimension (1 for Numeric, N for Categorical).
Shape	get_shape (const string &role_name) const
	Returns the input or target shape used by the network.
vector< string >	get_feature_scalers (const string &role_name) const
	Returns the scaler chosen for each variable of a given role.
virtual void	get_batches (const vector< Index > &sample_indices, Index batch_size, bool shuffle, vector< vector< Index > > &batches) const
	Splits a list of sample indices into batches.
const MatrixR &	get_data () const
	Returns the raw data matrix.
MatrixR	get_feature_data (const string &role_name) const
	Returns the data matrix restricted to the features of a given role.
MatrixR	get_data (const string &sample_role, const string &variable_role) const
	Returns the data restricted to a sample-role and variable-role intersection.
MatrixR	get_data_from_indices (const vector< Index > &sample_indices, const vector< Index > &variable_indices) const
	Returns the data restricted to specific samples and variables.
VectorR	get_sample_data (const Index sample_index) const
	Returns a single sample as a row vector.
MatrixR	get_variable_data (const Index variable_index) const
	Returns the data for a single variable across all samples.
MatrixR	get_variable_data (const Index variable_index, const vector< Index > &sample_indices) const
	Returns the data for a single variable on a subset of samples.
MatrixR	get_variable_data (const string &variable_name) const
	Returns the data for a single variable identified by name.
const vector< vector< string > > &	get_data_file_preview () const
	Returns the cached preview of the source file (first rows).
MissingValuesMethod	get_missing_values_method () const
	Returns the configured missing-value strategy.
string	get_missing_values_method_string () const
	Returns the missing-value strategy as a string.
const filesystem::path &	get_data_path () const
	Returns the path to the source data file.
const Separator &	get_separator () const
	Returns the configured field separator.
string	get_separator_string () const
	Returns the field separator as the actual delimiter character(s).
string	get_separator_name () const
	Returns the field separator as a human-readable name.
const Codification &	get_codification () const
	Returns the configured source-file codification.
const string	get_codification_string () const
	Returns the codification as a string.
const string &	get_missing_values_label () const
	Returns the label that marks missing values in the source file.
bool	get_display () const
	Reports whether progress messages are printed.
bool	is_empty () const
	Reports whether the data matrix is empty.
Shape	get_input_shape () const
	Returns the input shape.
Shape	get_target_shape () const
	Returns the target shape.
void	set (const Index samples_number=0, const Shape &input_shape={}, const Shape &target_shape={})
	Resets the dataset to a synthetic shape.
void	set (const filesystem::path &data_path, const string &separator, bool has_header=true, bool has_ids=false, const Dataset::Codification &codification=Codification::UTF8)
	Resets the dataset by loading a delimited text file.
void	set (const filesystem::path &file_name)
	Resets the dataset by loading a previously serialized JSON state.
void	set_default ()
	Resets configuration members to defaults.
void	set_sample_roles (const string &role_name)
	Assigns the same role to every sample.
void	set_sample_role (const Index sample_index, const string &role_name)
	Assigns a role to a single sample.
void	set_sample_roles (const vector< string > &role_names)
	Assigns roles to all samples from a parallel string vector.
void	set_sample_roles (const vector< Index > &sample_indices, const string &role_name)
	Assigns the same role to a list of samples.
void	set_variables (const vector< Variable > &new_variables)
	Replaces the per-variable metadata.
void	set_default_variable_names ()
	Sets default names ("variable_1", "variable_2", ...) for every variable.
virtual void	set_variable_roles (const vector< string > &role_names)
	Assigns roles to all variables from a parallel string vector.
void	set_variables (const string &description)
	Re-creates the variables vector from an input/target shape descriptor.
void	set_variable_indices (const vector< Index > &input_indices, const vector< Index > &target_indices)
	Marks selected variables as Input and others as Target.
void	set_input_variables_unused ()
	Marks all input variables as Unused.
void	set_variable_role (const Index variable_index, const string &role_name)
	Sets the role of a single variable by index.
void	set_variable_role (const string &variable_name, const string &role_name)
	Sets the role of a single variable by name.
void	set_variable_type (const Index variable_index, const VariableType &type)
	Sets the type of a single variable by index.
void	set_variable_type (const string &variable_name, const VariableType &type)
	Sets the type of a single variable by name.
void	set_variable_types (const VariableType &type)
	Sets every variable to a given type.
void	set_variable_names (const vector< string > &new_variable_names)
	Replaces the names of every variable.
void	set_variables_number (const Index new_size)
	Resizes the variables vector.
void	set_variable_scalers (const string &scaler_name)
	Sets the same scaler on every variable.
void	set_variable_scalers (const vector< string > &scaler_names)
	Sets one scaler per variable.
void	set_binary_variables ()
	Detects binary variables (two distinct values) and tags them accordingly.
void	set_feature_names (const vector< string > &new_feature_names)
	Names every feature.
void	set_variable_roles (const string &role_name)
	Assigns the same role to every variable.
void	set_shape (const string &role_name, const Shape &new_shape)
	Sets the input or target shape.
void	set_data (const MatrixR &new_data)
	Replaces the data matrix.
void	set_data_path (const filesystem::path &new_data_path)
	Sets the path to the source data file.
void	set_has_header (bool new_has_header)
	Sets whether the source file has a header row.
void	set_has_ids (bool new_has_ids)
	Sets whether the source file has a sample-id column.
void	set_separator (const Separator &new_separator)
	Sets the field separator.
void	set_separator_string (const string &new_separator_string)
	Sets the field separator from its delimiter character(s).
void	set_separator_name (const string &new_separator_name)
	Sets the field separator from its human-readable name.
void	set_codification (const Codification &new_codification)
	Sets the source-file codification.
void	set_codification (const string &new_codification)
	Sets the source-file codification from its name.
void	set_missing_values_label (string label)
	Sets the label used for missing values in the source file.
void	set_missing_values_method (const MissingValuesMethod &method)
	Sets the missing-value handling strategy.
void	set_missing_values_method (const string &method_name)
	Sets the missing-value handling strategy from its name.
void	set_gmt (const Index new_gmt)
	Sets the GMT offset for time variables.
void	set_display (bool new_display)
	Toggles progress messages.
bool	is_sample_used (const Index i) const
	Reports whether a sample is used (any role other than None).
bool	has_binary_variables () const
	Reports whether at least one variable is binary.
bool	has_categorical_variables () const
	Reports whether at least one variable is categorical.
bool	has_binary_or_categorical_variables () const
	Reports whether the dataset has any binary or categorical variable.
bool	has_time_variable () const
	Reports whether at least one variable plays the Time role.
bool	has_validation () const
	Reports whether at least one sample is assigned to Validation.
bool	has_missing_values (const vector< string > &labels) const
	Reports whether the dataset has missing values matching any of the supplied labels.
void	split_samples (const float training_ratio=0.6f, float selection_ratio=0.2f, float testing_ratio=0.2f, bool shuffle=true)
	Splits samples into training/validation/testing partitions.
void	split_samples_sequential (const float training_ratio=0.6f, float selection_ratio=0.2f, float testing_ratio=0.2f)
	Splits samples into partitions in their original order.
void	split_samples_random (const float training_ratio=0.6f, float selection_ratio=0.2f, float testing_ratio=0.2f)
	Splits samples into partitions after random shuffling.
vector< string >	unuse_uncorrelated_variables (const float minimum_correlation=0.25f)
	Marks variables with low correlation against the target as Unused.
vector< string >	unuse_collinear_variables (const float maximum_correlation=0.95f)
	Marks variables strongly correlated against another input as Unused.
void	set_data_constant (const float value)
	Fills the data matrix with a constant value.
vector< Descriptives >	calculate_feature_descriptives () const
	Computes descriptive statistics for every feature.
vector< Descriptives >	calculate_variable_descriptives_positive_samples () const
	Computes descriptives for inputs restricted to positive-target samples.
vector< Descriptives >	calculate_variable_descriptives_negative_samples () const
	Computes descriptives for inputs restricted to negative-target samples.
vector< Descriptives >	calculate_variable_descriptives_categories (const Index variable_index) const
	Computes descriptives per category of a categorical variable.
vector< Descriptives >	calculate_feature_descriptives (const string &role_name) const
	Computes feature descriptives for a single role.
vector< Histogram >	calculate_variable_distributions (const Index bins_number=10) const
	Builds histograms of every variable.
vector< BoxPlot >	calculate_variables_box_plots () const
	Computes box-plot statistics for every variable.
Tensor< Correlation, 2 >	calculate_input_variable_correlations (Correlation(*correlation_function)(const MatrixR &, const MatrixR &), Correlation::Method method, const string &samples_role) const
	Computes a custom correlation between every pair of input variables.
Tensor< Correlation, 2 >	calculate_input_variable_pearson_correlations () const
	Computes Pearson correlations between every pair of input variables.
Tensor< Correlation, 2 >	calculate_input_variable_spearman_correlations () const
	Computes Spearman rank correlations between every pair of input variables.
Tensor< Correlation, 2 >	calculate_input_target_variable_correlations (Correlation(*correlation_function)(const MatrixR &, const MatrixR &), const string &samples_role) const
	Computes a custom correlation between inputs and targets.
Tensor< Correlation, 2 >	calculate_input_target_variable_pearson_correlations () const
	Computes Pearson correlations between inputs and targets.
Tensor< Correlation, 2 >	calculate_input_target_variable_spearman_correlations () const
	Computes Spearman rank correlations between inputs and targets.
VectorI	calculate_correlations_rank () const
	Returns the rank of every input variable by absolute Pearson correlation against targets.
void	set_default_variable_scalers ()
	Picks default scalers for every variable based on its type.
vector< Descriptives >	scale_data ()
	Scales the entire data matrix.
virtual vector< Descriptives >	scale_features (const string &role_name)
	Scales the features of a given role.
void	unscale_features (const string &role_name, const vector< Descriptives > &feature_descriptives)
	Inverse-scales the features of a given role.
VectorI	calculate_target_distribution () const
	Counts the samples of every target class.
vector< vector< Index > >	calculate_Tukey_outliers (const float tukey_factor=1.5f, bool replace=false)
	Detects Tukey outliers per variable.
vector< vector< Index > >	replace_Tukey_outliers_with_NaN (const float tukey_factor=1.5f)
	Detects Tukey outliers and replaces them with NaN.
void	unuse_Tukey_outliers (const float tukey_factor=1.5f)
	Marks Tukey-outlier samples as Unused.
virtual void	set_data_random ()
	Fills the data matrix with uniform random values.
virtual void	set_data_integer (const Index vocabulary_size)
	Fills the data matrix with random integers in [0, vocabulary_size).
void	set_data_rosenbrock ()
	Fills the data matrix with samples from the Rosenbrock function.
void	set_data_binary_classification ()
	Fills the data matrix with synthetic binary-classification data.
void	save (const filesystem::path &file_name) const
	Saves the dataset state to a JSON file on disk.
void	load (const filesystem::path &file_name)
	Loads the dataset state from a JSON file on disk.
void	save_data () const
	Saves the data matrix back to the configured source file.
void	save_data_binary (const filesystem::path &file_name) const
	Saves the data matrix as a binary file.
void	load_data_binary ()
	Loads the data matrix from a binary file produced by save_data_binary().
Index	get_missing_values_number () const
	Returns the total number of cells flagged as missing.
bool	has_nan () const
	Reports whether the data matrix contains any NaN.
bool	has_nan_row (const Index row_index) const
	Reports whether a row contains any NaN.
void	impute_missing_values_statistic (const MissingValuesMethod &method)
	Replaces missing values with a per-variable statistic.
void	scrub_missing_values ()
	Removes samples that contain missing values.
void	calculate_missing_values_statistics ()
	Updates the cached missing-value statistics (counts, indices).
VectorI	count_nans_per_variable () const
	Counts NaN cells per variable.
Index	count_variables_with_nan () const
	Counts variables that contain at least one NaN.
Index	count_rows_with_nan () const
	Counts samples that contain at least one NaN.
Index	count_nan () const
	Counts the total number of NaN cells.
vector< vector< Index > >	split_samples (const vector< Index > &indices, Index parts_number) const
	Splits a list of sample indices into chunks of (roughly) equal size.
DateFormat	infer_dataset_date_format (const vector< Variable > &variables, const vector< vector< string > > &data_file_preview, bool has_header, const string &missing_values_label)
	Infers the date format used by date-typed variables in the source file.
virtual void	augment_inputs (float *buffer, Index batch_size) const
	Optionally augments inputs in-place after fill_inputs() (e.g. random crops).
virtual void	fill_decoder (const vector< Index > &sample_indices, const vector< Index > &feature_indices, float *buffer, bool transpose=true, int contiguous=-1) const
	Fills a contiguous buffer with decoder-side inputs (transformer-style models).

Additional Inherited Members
Public Types inherited from opennn::Dataset
enum class	Codification { UTF8 , SHIFT_JIS }
	Source-file character encoding. More...
enum class	Separator { Space , Tab , Comma , Semicolon }
	Field-separator type for tabular files. More...
enum class	MissingValuesMethod { Unuse , Mean , Median , Interpolation }
	Strategy for replacing missing values. More...
Protected Member Functions inherited from opennn::Dataset
void	set_default_variable_roles ()
	Sets default Input/Target roles based on column position (last column = target).
void	set_default_variable_roles_forecasting ()
	Sets default roles for forecasting (typical pattern: lagged inputs + future target).
void	variables_to_JSON (JsonWriter &) const
	Serializes the variables vector to JSON.
void	samples_to_JSON (JsonWriter &) const
	Serializes the per-sample roles to JSON.
void	missing_values_to_JSON (JsonWriter &) const
	Serializes the missing-value statistics to JSON.
void	preview_data_to_JSON (JsonWriter &) const
	Serializes the source-file preview to JSON.
void	variables_from_JSON (const Json *)
	Restores the variables vector from JSON.
void	samples_from_JSON (const Json *)
	Restores the per-sample roles from JSON.
void	missing_values_from_JSON (const Json *)
	Restores the missing-value statistics from JSON.
void	preview_data_from_JSON (const Json *)
	Restores the source-file preview from JSON.
Protected Attributes inherited from opennn::Dataset
MatrixR	data
	Dense data matrix [samples x variables].
Shape	input_shape
	Shape of the input portion (rank may exceed 1 for image/sequence data).
Shape	target_shape
	Shape of the target portion.
Shape	decoder_shape
	Shape of the decoder portion (transformer-style models).
vector< SampleRole >	sample_roles
	Per-sample role (Training/Validation/Testing/None).
vector< string >	sample_ids
	Optional per-sample identifiers (when the source file has an id column).
vector< Variable >	variables
	Per-variable metadata (name, role, type, scaler).
filesystem::path	data_path
	Path to the source data file.
Separator	separator = Separator::Comma
	Field separator used in the source file.
string	missing_values_label = "NA"
	Label that marks missing values in the source file.
bool	has_header = false
	Whether the source file's first row contains column names.
bool	has_sample_ids = false
	Whether the source file's first column contains sample identifiers.
Codification	codification = Codification::UTF8
	Source-file character encoding.
vector< vector< string > >	data_file_preview
	Cached preview of the first rows of the source file.
Index	gmt = 0
	GMT offset for time variables, in hours.
MissingValuesMethod	missing_values_method = MissingValuesMethod::Mean
	Strategy used to handle missing values.
Index	missing_values_number = 0
	Total number of missing cells.
VectorI	variables_missing_values_number
	Per-variable missing-value count.
Index	rows_missing_values_number = 0
	Number of rows that contain at least one missing value.
bool	display = true
	Whether to print progress messages.
const vector< string >	positive_words = {"1", "yes", "positive", "+", "true", "good", "si", "sí", "Sí"}
	Strings interpreted as positive when parsing binary variables.
const vector< string >	negative_words = {"0", "no", "negative", "-", "false", "bad", "not", "No"}
	Strings interpreted as negative when parsing binary variables.

Detailed Description

Dataset specialization for time series with explicit past / future windows.

Stores a temporally ordered table of samples and exposes per-sample (past_time_steps, future_time_steps) windows that the network consumes as inputs and targets respectively. Supports auto- and cross-correlation analysis, gap filling and several missing-value imputation strategies.

Constructor & Destructor Documentation

TimeSeriesDataset() [1/2]

opennn::TimeSeriesDataset::TimeSeriesDataset	(	const Index	samples_number = 0,
		const Shape &	input_shape = {},
		const Shape &	target_shape = {} )

Constructs an empty TimeSeriesDataset of given dimensions.

Parameters

samples_number	Number of samples (time steps).
input_shape	Per-sample input shape.
target_shape	Per-sample target shape.

TimeSeriesDataset() [2/2]

opennn::TimeSeriesDataset::TimeSeriesDataset	(	const filesystem::path &	path,
		const string &	separator,
		bool	has_header = true,
		bool	has_sample_index = false,
		const Codification &	codification = Codification::UTF8 )

Constructs a TimeSeriesDataset by loading from a CSV file.

Parameters

path	Path to the CSV file.
separator	Column separator character.
has_header	Whether the first row contains column names.
has_sample_index	Whether the first column is a sample index.
codification	Source-file character encoding.

Member Function Documentation

calculate_autocorrelations()

MatrixR opennn::TimeSeriesDataset::calculate_autocorrelations

(

const Index

maximum_lag = 10

)

const

Computes the autocorrelation function of every variable.

Parameters

maximum_lag

Maximum lag (in time steps) considered.

Returns

Matrix with one row per variable and one column per lag.

calculate_cross_correlations()

Tensor3 opennn::TimeSeriesDataset::calculate_cross_correlations

(

const Index

maximum_lag = 10

)

const

Computes the Pearson cross-correlation between every variable pair.

Parameters

maximum_lag

Maximum lag (in time steps) considered.

Returns

Rank-3 tensor (variable_a, variable_b, lag).

calculate_cross_correlations_spearman()

Tensor3 opennn::TimeSeriesDataset::calculate_cross_correlations_spearman

(

const Index

maximum_lag = 10

)

const

Computes the Spearman cross-correlation between every variable pair.

Parameters

maximum_lag

Maximum lag (in time steps) considered.

Returns

Rank-3 tensor (variable_a, variable_b, lag).

fill_gaps()

void opennn::TimeSeriesDataset::fill_gaps

(

)

Fills missing time steps with imputed rows so the time variable becomes evenly spaced.

fill_inputs()

void opennn::TimeSeriesDataset::fill_inputs ( const vector< Index > & , const vector< Index > & , float * , bool = true, int contiguous = -1 ) const

overridevirtual

Fills the input batch buffer with past-window data.

Receives the sample indices, the input feature indices, the device buffer pointer, an unused legacy flag, and an optional contiguous stride hint (-1 to ignore).

Reimplemented from opennn::Dataset.

fill_targets()

void opennn::TimeSeriesDataset::fill_targets ( const vector< Index > & , const vector< Index > & , float * , bool = true, int contiguous = -1 ) const

overridevirtual

Fills the target batch buffer with future-window data.

Receives the sample indices, the target feature indices, the device buffer pointer, an unused legacy flag, and an optional contiguous stride hint (-1 to ignore).

Reimplemented from opennn::Dataset.

from_JSON()

void opennn::TimeSeriesDataset::from_JSON ( const JsonDocument & )

overridevirtual

Loads dataset metadata (windows, time variable) from JSON.

Reimplemented from opennn::Dataset.

get_data()

Tensor3 opennn::TimeSeriesDataset::get_data	(	const string &	sample_role,
		const string &	feature_role ) const

Returns the data tensor for the given sample / feature roles.

Parameters

sample_role	Sample role ("Training", "Validation", "Testing").
feature_role	Feature role ("Input" or "Target").

Returns

Rank-3 tensor (sample, time_step, variable).

get_future_time_steps()

Index opennn::TimeSeriesDataset::get_future_time_steps

(

)

const

Number of future time steps used as targets.

get_multi_target()

bool opennn::TimeSeriesDataset::get_multi_target

(

)

const

Whether the dataset has more than one target variable.

get_past_time_steps()

Index opennn::TimeSeriesDataset::get_past_time_steps

(

)

const

Number of past time steps used as inputs.

get_time_variable_index()

Index opennn::TimeSeriesDataset::get_time_variable_index

(

)

const

Column index of the time variable.

impute_missing_values_interpolate()

void opennn::TimeSeriesDataset::impute_missing_values_interpolate ( )

overridevirtual

Imputes missing values via temporal interpolation.

Reimplemented from opennn::Dataset.

impute_missing_values_unuse()

void opennn::TimeSeriesDataset::impute_missing_values_unuse ( )

overridevirtual

Marks rows with missing values as None (excluded from training).

Reimplemented from opennn::Dataset.

read_csv()

void opennn::TimeSeriesDataset::read_csv ( )

overridevirtual

Reads time-series rows from the configured CSV file.

Reimplemented from opennn::Dataset.

set_future_time_steps()

void opennn::TimeSeriesDataset::set_future_time_steps

(

const Index

)

Sets the forecast horizon.

Receives the number of future time steps used as targets.

set_multi_target()

void opennn::TimeSeriesDataset::set_multi_target

(

const bool

)

Sets whether the dataset has more than one target variable.

Receives true for multi-target, false for single-target.

set_past_time_steps()

void opennn::TimeSeriesDataset::set_past_time_steps

(

const Index

)

Sets the input window length.

Receives the number of past time steps used as inputs.

set_time_variable_index()

void opennn::TimeSeriesDataset::set_time_variable_index

(

const Index

)

Sets the column index of the time variable.

Receives the new time-variable column index.

to_JSON()

void opennn::TimeSeriesDataset::to_JSON ( JsonWriter & ) const

overridevirtual

Writes dataset metadata (windows, time variable) to JSON.

Reimplemented from opennn::Dataset.