Time series dataset with configurable past/future windows and autocorrelation analysis. More...

#include <time_series_dataset.h>

Inheritance diagram for opennn::TimeSeriesDataset:

Public Member Functions
	TimeSeriesDataset (const Index=0, const Shape &={}, const Shape &={})
	Creates a time series dataset with the given sample count and input/target shapes.

	TimeSeriesDataset (const filesystem::path &, const string &, bool=true, bool=false, const Codification &=Codification::UTF8)
	Creates a time series dataset by reading the given file with the given separator.

void	fill_gaps ()
	Fills missing rows between time stamps so the series has a uniform cadence.

Index	get_past_time_steps () const
	Returns the number of past time steps used as input context.

Index	get_future_time_steps () const
	Returns the number of future time steps used as prediction horizon.

Index	get_time_variable_index () const
	Returns the index of the variable acting as the time axis.

bool	get_multi_target () const
	Returns whether the dataset is configured for multi-target forecasting.

Tensor3	get_data (const string &sample_role, const string &feature_role) const
	Returns the windowed 3D tensor for the given sample and feature roles.

void	set_past_time_steps (const Index)
	Sets the number of past time steps used as input context.

void	set_future_time_steps (const Index)
	Sets the number of future time steps used as prediction horizon.

void	set_time_variable_index (const Index)
	Sets the index of the variable acting as the time axis.

void	set_multi_target (const bool)
	Sets whether the dataset is configured for multi-target forecasting.

MatrixR	calculate_autocorrelations (const Index=10) const
	Returns the autocorrelation matrix up to the given maximum lag.

Tensor3	calculate_cross_correlations (const Index=10) const
	Returns the Pearson cross-correlations between variables up to the given lag.

Tensor3	calculate_cross_correlations_spearman (const Index=10) const
	Returns the Spearman cross-correlations between variables up to the given lag.

void	to_JSON (JsonWriter &) const override
	Writes dataset state to a JSON writer.

void	from_JSON (const JsonDocument &) override
	Loads dataset state from a JSON document.

void	read_csv ()
	Reads the configured CSV file into the time series dataset.

void	impute_missing_values_unuse () override
	Marks samples around missing values as unused (time-series aware).

void	impute_missing_values_interpolate () override
	Interpolates missing values along the time axis.

void	fill_inputs (const vector< Index > &, const vector< Index > &, float *, bool is_training, bool parallelize=true, int contiguous=-1) const override
	Copies the past-window input features of the selected samples into the destination buffer.

void	fill_targets (const vector< Index > &, const vector< Index > &, float *, bool is_training, bool parallelize=true, int contiguous=-1) const override
	Copies the future-window target features of the selected samples into the destination buffer.

void	resize_input_shape (Index) override
	Resizes the input shape, accounting for the configured past/future windows.

Public Member Functions inherited from opennn::TabularDataset
	TabularDataset (const Index=0, const Shape &={0}, const Shape &={0})
	Creates a tabular dataset with the given sample count and input/target shapes.

	TabularDataset (const filesystem::path &, const string &, bool=true, bool=false, const Codification &=Codification::UTF8)
	Creates a tabular dataset by reading the given file with the given separator.

void	set (const filesystem::path &, const string &, bool=true, bool=false, const Codification &=Codification::UTF8)
	Resets the dataset from the given file using the given parsing options.

void	set (const filesystem::path &)
	Resets the dataset by reading from the given path with default parsing options.

vector< string >	get_feature_scalers (const string &) const
	Returns the scaler names for variables matching the given role.

void	set_variable_scalers (const string &)
	Sets every variable's scaler from the given name.

void	set_variable_scalers (const vector< string > &)
	Sets each variable's scaler from the corresponding name in the list.

void	set_default_variable_scalers ()
	Assigns default scalers based on each variable's type.

void	set_gmt (const Index new_gmt)

DateFormat	infer_dataset_date_format (const vector< Variable > &, const vector< vector< string_view > > &, bool, const string &)
	Infers the date/time format used in the given file preview rows.

MissingValuesMethod	get_missing_values_method () const

string	get_missing_values_method_string () const
	Returns the missing-values method as its enumerator name.

const string &	get_missing_values_label () const

Index	get_missing_values_number () const

void	set_missing_values_label (string label)

void	set_missing_values_method (const MissingValuesMethod &method)

void	set_missing_values_method (const string &)
	Sets the missing-values method from its enumerator name.

bool	has_missing_values (const vector< string_view > &) const
	Returns true if any field in the row matches the missing-values label.

void	scrub_missing_values () override
	Removes or imputes missing values using the configured MissingValuesMethod.

void	calculate_missing_values_statistics ()
	Refreshes per-variable and per-row missing-value counts.

void	impute_missing_values_statistic (const MissingValuesMethod &)
	Imputes missing values using the given statistic-based method (Mean/Median).

vector< string >	unuse_uncorrelated_variables (const float=0.25f)
	Marks input variables whose absolute correlation with targets is below the threshold as unused.

vector< string >	unuse_collinear_variables (const float=0.95f)
	Marks input variables that are highly collinear (above the threshold) as unused.

vector< Descriptives >	calculate_feature_descriptives () const
	Returns descriptive statistics for every feature across all samples.

vector< Descriptives >	calculate_feature_descriptives (const string &) const override
	Returns descriptive statistics for features with the given role name.

vector< Descriptives >	calculate_variable_descriptives_positive_samples () const
	Returns variable descriptives computed only over samples with positive target.

vector< Descriptives >	calculate_variable_descriptives_negative_samples () const
	Returns variable descriptives computed only over samples with negative target.

vector< Descriptives >	calculate_variable_descriptives_categories (const Index) const
	Returns variable descriptives restricted to the given target class index.

vector< Histogram >	calculate_variable_distributions (const Index=10) const
	Returns per-variable histograms with the given bin count.

vector< BoxPlot >	calculate_variables_box_plots () const
	Returns per-variable Tukey box-plot summaries.

Tensor< Correlation, 2 >	calculate_input_variable_correlations (Correlation(*)(const MatrixR &, const MatrixR &), Correlation::Method, const string &) const
	Returns the input-input correlation matrix using the given correlation function and method.

Tensor< Correlation, 2 >	calculate_input_variable_pearson_correlations () const
	Returns the Pearson correlation matrix among input variables.

Tensor< Correlation, 2 >	calculate_input_variable_spearman_correlations () const
	Returns the Spearman correlation matrix among input variables.

Tensor< Correlation, 2 >	calculate_input_target_variable_correlations (Correlation(*)(const MatrixR &, const MatrixR &), const string &) const
	Returns the input-target correlation matrix using the given correlation function.

Tensor< Correlation, 2 >	calculate_input_target_variable_pearson_correlations () const override
	Returns the Pearson correlation matrix between input and target variables.

Tensor< Correlation, 2 >	calculate_input_target_variable_spearman_correlations () const
	Returns the Spearman correlation matrix between input and target variables.

VectorI	calculate_correlations_rank () const override
	Returns input variable indices ranked by absolute correlation with the target.

vector< Descriptives >	scale_data ()
	Scales every feature column using its configured scaler; returns the applied descriptives.

vector< Descriptives >	scale_features (const string &) override
	Scales the features with the given role using their configured scalers.

void	unscale_features (const string &, const vector< Descriptives > &) override
	Reverts the scaling of features with the given role using the supplied descriptives.

VectorI	calculate_target_distribution () const override
	Returns the distribution of target classes for classification datasets.

vector< vector< Index > >	calculate_Tukey_outliers (const float=1.5f, bool=false)
	Detects Tukey outliers per variable using the given fence multiplier.

vector< vector< Index > >	replace_Tukey_outliers_with_NaN (const float=1.5f)
	Replaces detected Tukey outliers with NaN and returns the affected indices.

void	unuse_Tukey_outliers (const float=1.5f)
	Marks samples containing Tukey outliers as unused.

void	set_data_random () override
	Fills the data matrix with random values.

void	set_data_integer (const Index vocabulary_size) override
	Fills the data matrix with random integers up to the given vocabulary size.

void	set_data_rosenbrock ()
	Fills the data matrix with samples drawn from the Rosenbrock function (regression test data).

void	set_data_binary_classification ()
	Fills the data matrix with a synthetic binary classification dataset.

void	read_csv ()
	Reads the configured CSV file into the dataset, inferring types and headers.

void	set (const Index=0, const Shape &={}, const Shape &={})
	Resets the dataset with the given sample count and input/target shapes.

Public Member Functions inherited from opennn::Dataset
virtual	~Dataset ()=default

virtual Index	get_samples_number () const
	Returns the total number of samples (rows) in the data matrix.

Index	get_samples_number (const string &) const
	Returns the number of samples with the given role ("Training", "Validation", ...).

Index	get_used_samples_number () const
	Returns the number of samples whose role is not None.

vector< Index >	get_sample_indices (const string &) const
	Returns indices of samples with the given role name.

vector< Index >	get_used_sample_indices () const
	Returns indices of all samples whose role is not None.

const vector< SampleRole > &	get_sample_roles () const
	Returns the per-sample role vector.

vector< Index >	get_sample_roles_vector () const
	Returns the per-sample roles as integer indices.

VectorI	get_sample_role_numbers () const
	Returns the per-sample roles as a tensor of integer indices.

Index	get_variables_number () const
	Returns the total number of variables (columns descriptors).

Index	get_variables_number (const string &) const
	Returns the number of variables with the given role name.

Index	get_used_variables_number () const
	Returns the number of variables whose role is in use.

const vector< Variable > &	get_variables () const
	Returns the variable descriptors.

vector< Variable >	get_variables (const string &) const
	Returns the variables with the given role name.

Index	get_variable_index (const string &) const
	Returns the index of the variable with the given name.

Index	get_variable_index (const Index) const
	Returns the variable index corresponding to a flat feature index.

vector< Index >	get_variable_indices (const string &) const
	Returns the indices of variables matching the given role name.

vector< Index >	get_used_variables_indices () const
	Returns the indices of all in-use variables.

vector< string >	get_variable_names () const
	Returns the names of all variables.

vector< string >	get_variable_names (const string &) const
	Returns the names of variables with the given role name.

VariableType	get_variable_type (const Index index) const
	Returns the VariableType of the variable at the given index.

vector< VariableType >	get_variable_types (const vector< Index > &indices) const
	Returns the VariableType for each of the given variable indices.

Index	get_features_number () const
	Returns the total number of features (data matrix columns).

Index	get_features_number (const string &) const
	Returns the number of features for variables with the given role name.

Index	get_used_features_number () const
	Returns the number of features for in-use variables.

vector< string >	get_feature_names () const
	Returns the expanded feature names (one entry per data matrix column).

vector< string >	get_feature_names (const string &) const
	Returns the expanded feature names restricted to the given role.

vector< vector< Index > >	get_feature_indices () const
	Returns the data column indices grouped per variable.

vector< Index >	get_feature_indices (const Index) const
	Returns the data column indices that belong to the given variable index.

vector< Index >	get_feature_indices (const string &) const
	Returns the data column indices that belong to variables with the given role name.

vector< Index >	get_used_feature_indices () const
	Returns the data column indices that belong to in-use variables.

vector< Index >	get_feature_dimensions () const
	Returns the per-variable feature dimension counts.

Shape	get_shape (const string &) const
	Returns the configured Shape for the given role ("Input", "Target", "Decoder").

virtual void	get_batches (const vector< Index > &, Index, bool, vector< vector< Index > > &) const
	Splits sample indices into batches and writes them into `batches`.

const vector< vector< string > > &	get_data_file_preview () const
	Returns the parsed preview rows captured during the last file read.

const filesystem::path &	get_data_path () const
	Returns the configured data file path.

const Separator &	get_separator () const
	Returns the current field Separator.

string	get_separator_string () const
	Returns the separator as the literal character used in files.

string	get_separator_name () const
	Returns the separator as its enumerator name ("Space", "Tab", ...).

const Codification &	get_codification () const
	Returns the configured text Codification.

string	get_codification_string () const
	Returns the codification as its enumerator name.

bool	get_display () const
	Returns whether progress messages are printed.

virtual bool	is_empty () const
	Returns true when the dataset contains no samples.

Shape	get_input_shape () const
	Returns the configured input tensor shape.

Shape	get_target_shape () const
	Returns the configured target tensor shape.

const MatrixR &	get_data () const
	Returns the raw data matrix (rows = samples, columns = features).

void	set_data (const MatrixR &)
	Replaces the underlying data matrix.

void	set_data_constant (const float)
	Fills the data matrix with the given constant value.

void	set_default ()
	Restores default dataset settings.

void	set_sample_roles (const string &)
	Assigns the same role to every sample.

void	set_sample_role (const Index, const string &)
	Sets the role of a single sample by index.

void	set_sample_roles (const vector< string > &)
	Assigns a role per sample from a string vector.

void	set_sample_roles (const vector< Index > &, const string &)
	Assigns the same role to the given sample indices.

void	set_variables (const vector< Variable > &new_variables)

void	set_default_variable_names ()
	Assigns default placeholder names to all variables.

void	set_variable_roles (const vector< string > &)
	Sets the role of every variable from the given string list.

void	set_variable_indices (const vector< Index > &, const vector< Index > &)
	Sets which variable indices are inputs and which are targets.

void	set_input_variables_unused ()
	Marks all input variables as unused (role None).

void	set_variable_role (const Index, const string &)
	Sets the role of the variable at the given index.

void	set_variable_role (const string &, const string &)
	Sets the role of the variable with the given name.

void	set_variable_type (const Index, const VariableType &)
	Sets the type of the variable at the given index.

void	set_variable_type (const string &, const VariableType &)
	Sets the type of the variable with the given name.

void	set_variable_types (const VariableType &)
	Sets every variable to the given type.

void	set_binary_variables ()
	Detects and marks variables with binary values as VariableType::Binary.

void	set_variable_names (const vector< string > &)
	Assigns names to all variables from the given list.

void	set_variables_number (const Index new_size)

void	set_feature_names (const vector< string > &)
	Assigns expanded feature names, propagating categories back to variables.

void	set_variable_roles (const string &)
	Assigns the same role to every variable.

void	set_shape (const string &, const Shape &)
	Sets the tensor Shape associated with the given role ("Input"/"Target"/"Decoder").

void	set_data_path (const filesystem::path &new_data_path)

void	set_has_header (bool new_has_header)

void	set_has_ids (bool new_has_ids)

void	set_separator (const Separator &new_separator)

void	set_separator_string (const string &)
	Sets the separator from its literal character.

void	set_separator_name (const string &)
	Sets the separator from its enumerator name.

void	set_codification (const Codification &new_codification)

void	set_codification (const string &)
	Sets the codification from its enumerator name.

void	set_display (bool new_display)

bool	is_sample_used (const Index i) const
	Returns true if the sample at `i` has a role other than None.

bool	has_binary_variables () const
	Returns true if any variable has type Binary.

bool	has_categorical_variables () const
	Returns true if any variable has type Categorical.

bool	has_binary_or_categorical_variables () const
	Returns true if any variable is Binary or Categorical.

bool	has_time_variable () const
	Returns true if any variable has role Time.

bool	has_validation () const
	Returns true if any sample is assigned the Validation role.

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 roles, optionally shuffled.

void	split_samples_sequential (const float training_ratio=0.6f, float selection_ratio=0.2f, float testing_ratio=0.2f)
	Splits samples sequentially without shuffling.

void	split_samples_random (const float training_ratio=0.6f, float selection_ratio=0.2f, float testing_ratio=0.2f)
	Splits samples randomly across roles.

vector< vector< Index > >	split_samples (const vector< Index > &, Index) const
	Splits the given indices into chunks of the requested size.

MatrixR	get_data (const string &, const string &) const
	Returns the data submatrix for the given sample role and feature role.

MatrixR	get_data_from_indices (const vector< Index > &, const vector< Index > &) const
	Returns a submatrix from the data using explicit row and column indices.

VectorR	get_sample_data (const Index) const
	Returns the row of the data matrix at the given sample index.

MatrixR	get_variable_data (const Index) const
	Returns the data columns belonging to the variable at `index`.

MatrixR	get_variable_data (const Index, const vector< Index > &) const
	Returns the variable data restricted to the given sample indices.

MatrixR	get_variable_data (const string &) const
	Returns the data columns of the variable with the given name.

MatrixR	get_feature_data (const string &) const
	Returns the data columns belonging to features with the given role name.

void	set (const Index=0, const Shape &={}, const Shape &={})
	Resets the dataset with the given sample count and input/target shapes.

bool	has_nan () const
	Returns true if any entry in the data matrix is NaN.

bool	has_nan_row (const Index) const
	Returns true if the sample at the given row contains a NaN.

VectorI	count_nans_per_variable () const
	Returns the NaN count for each variable.

Index	count_variables_with_nan () const
	Returns the number of variables that contain at least one NaN.

Index	count_rows_with_nan () const
	Returns the number of rows that contain at least one NaN.

Index	count_nan () const
	Returns the total NaN count in the data matrix.

void	save (const filesystem::path &) const
	Saves the dataset metadata to a JSON file at the given path.

void	load (const filesystem::path &)
	Loads the dataset metadata from the JSON file at the given path.

void	save_data () const
	Saves the data matrix to the configured data path.

void	save_data_binary (const filesystem::path &) const
	Saves the data matrix to the given path in binary form.

void	load_data_binary ()
	Loads the data matrix from the binary file at the configured data path.

virtual void	augment_inputs (float *, Index) const
	Applies data augmentation in place to the input buffer (no-op in base class).

virtual void	fill_decoder (const vector< Index > &, const vector< Index > &, float *, bool is_training, bool parallelize=true, int contiguous=-1) const
	Copies decoder input features into a destination buffer (sequence models).

Additional Inherited Members
Public Types inherited from opennn::TabularDataset
enum class	MissingValuesMethod { Unuse , Mean , Median , Interpolation }
	Strategy for handling missing values in tabular data. More...

Public Types inherited from opennn::Dataset
enum class	Codification { UTF8 , SHIFT_JIS }
	Text encoding of the source data file. More...

enum class	Separator { Space , Tab , Comma , Semicolon }
	Field separator used when reading delimited text files. More...

Protected Member Functions inherited from opennn::TabularDataset
void	missing_values_to_JSON (JsonWriter &) const

void	missing_values_from_JSON (const Json *)

void	infer_column_types (const vector< vector< string_view > > &)

vector< Index >	filter_used_samples_by_column (Index, bool) const

void	apply_scaler (Index, const string &, const Descriptives &, bool)

Protected Member Functions inherited from opennn::Dataset
	Dataset ()=default

void	set_default_variable_roles ()

void	set_default_variable_roles_forecasting ()

void	infer_variable_types_from_data ()

void	read_data_file_preview (const vector< vector< string_view > > &)

void	check_separators (string_view) const

void	samples_from_JSON (const Json *)

void	variables_to_JSON (JsonWriter &) const

void	samples_to_JSON (JsonWriter &) const

void	preview_data_to_JSON (JsonWriter &) const

void	variables_from_JSON (const Json *)

void	preview_data_from_JSON (const Json *)

Protected Attributes inherited from opennn::TabularDataset
string	missing_values_label = "NA"

MissingValuesMethod	missing_values_method = MissingValuesMethod::Mean

Index	missing_values_number = 0

VectorI	variables_missing_values_number

Index	rows_missing_values_number = 0

Index	gmt = 0

Protected Attributes inherited from opennn::Dataset
Shape	input_shape

Shape	target_shape

Shape	decoder_shape

vector< SampleRole >	sample_roles

vector< string >	sample_ids

vector< Variable >	variables

MatrixR	data

filesystem::path	data_path

Separator	separator = Separator::Comma

bool	has_header = false

bool	has_sample_ids = false

Codification	codification = Codification::UTF8

vector< vector< string > >	data_file_preview

bool	display = true

const vector< string >	positive_words = {"1", "yes", "positive", "+", "true", "good", "si", "sí", "Sí"}

const vector< string >	negative_words = {"0", "no", "negative", "-", "false", "bad", "not", "No"}

Detailed Description

Time series dataset with configurable past/future windows and autocorrelation analysis.

Constructor & Destructor Documentation

◆ TimeSeriesDataset() [1/2]

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

Creates a time series dataset with the given sample count and input/target shapes.

Parameters

sample_count	Number of samples to allocate.
input_shape	Shape of input features.
target_shape	Shape of target features.

◆ TimeSeriesDataset() [2/2]

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

Creates a time series dataset by reading the given file with the given separator.

Parameters

data_path	Path to the source CSV/text file.
separator	Field separator name.
has_header	Whether the first row contains column names.
has_ids	Whether the first column contains sample identifiers.
codification	Text encoding of the file.

Member Function Documentation

◆ calculate_autocorrelations()

MatrixR opennn::TimeSeriesDataset::calculate_autocorrelations ( const Index = 10 ) const

Returns the autocorrelation matrix up to the given maximum lag.

◆ calculate_cross_correlations()

Tensor3 opennn::TimeSeriesDataset::calculate_cross_correlations ( const Index = 10 ) const

Returns the Pearson cross-correlations between variables up to the given lag.

◆ calculate_cross_correlations_spearman()

Tensor3 opennn::TimeSeriesDataset::calculate_cross_correlations_spearman ( const Index = 10 ) const

Returns the Spearman cross-correlations between variables up to the given lag.

◆ fill_gaps()

void opennn::TimeSeriesDataset::fill_gaps ( )

Fills missing rows between time stamps so the series has a uniform cadence.

◆ fill_inputs()

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

overridevirtual

Copies the past-window input features of the selected samples into the destination buffer.

Reimplemented from opennn::Dataset.

◆ fill_targets()

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

overridevirtual

Copies the future-window target features of the selected samples into the destination buffer.

Reimplemented from opennn::Dataset.

◆ from_JSON()

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

overridevirtual

Loads dataset state from a JSON document.

Reimplemented from opennn::TabularDataset.

◆ get_data()

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

Returns the windowed 3D tensor for the given sample and feature roles.

◆ get_future_time_steps()

Index opennn::TimeSeriesDataset::get_future_time_steps ( ) const

Returns the number of future time steps used as prediction horizon.

◆ get_multi_target()

bool opennn::TimeSeriesDataset::get_multi_target ( ) const

Returns whether the dataset is configured for multi-target forecasting.

◆ get_past_time_steps()

Index opennn::TimeSeriesDataset::get_past_time_steps ( ) const

Returns the number of past time steps used as input context.

◆ get_time_variable_index()

Index opennn::TimeSeriesDataset::get_time_variable_index ( ) const

Returns the index of the variable acting as the time axis.

◆ impute_missing_values_interpolate()

void opennn::TimeSeriesDataset::impute_missing_values_interpolate ( )

overridevirtual

Interpolates missing values along the time axis.

Reimplemented from opennn::TabularDataset.

◆ impute_missing_values_unuse()

void opennn::TimeSeriesDataset::impute_missing_values_unuse ( )

overridevirtual

Marks samples around missing values as unused (time-series aware).

Reimplemented from opennn::TabularDataset.

◆ read_csv()

void opennn::TimeSeriesDataset::read_csv ( )

Reads the configured CSV file into the time series dataset.

◆ resize_input_shape()

void opennn::TimeSeriesDataset::resize_input_shape ( Index )

overridevirtual

Resizes the input shape, accounting for the configured past/future windows.

Reimplemented from opennn::Dataset.

◆ set_future_time_steps()

void opennn::TimeSeriesDataset::set_future_time_steps ( const Index )

Sets the number of future time steps used as prediction horizon.

◆ set_multi_target()

void opennn::TimeSeriesDataset::set_multi_target ( const bool )

Sets whether the dataset is configured for multi-target forecasting.

◆ set_past_time_steps()

void opennn::TimeSeriesDataset::set_past_time_steps ( const Index )

Sets the number of past time steps used as input context.

◆ set_time_variable_index()

void opennn::TimeSeriesDataset::set_time_variable_index ( const Index )

Sets the index of the variable acting as the time axis.

◆ to_JSON()

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

overridevirtual

Writes dataset state to a JSON writer.

Reimplemented from opennn::TabularDataset.

Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ TimeSeriesDataset() [1/2]

◆ TimeSeriesDataset() [2/2]

Member Function Documentation

◆ calculate_autocorrelations()

◆ calculate_cross_correlations()

◆ calculate_cross_correlations_spearman()

◆ fill_gaps()

◆ fill_inputs()

◆ fill_targets()

◆ from_JSON()

◆ get_data()

◆ get_future_time_steps()

◆ get_multi_target()

◆ get_past_time_steps()

◆ get_time_variable_index()

◆ impute_missing_values_interpolate()

◆ impute_missing_values_unuse()

◆ read_csv()

◆ resize_input_shape()

◆ set_future_time_steps()

◆ set_multi_target()

◆ set_past_time_steps()

◆ set_time_variable_index()

◆ to_JSON()