Index (Deep Java Library 0.30.0

A B C D E F G H I K L M N O P Q R S T U V W
All Classes and Interfaces|All Packages

A

add(FieldName, NDArray) - Method in class ai.djl.timeseries.TimeSeriesData: Adds a fieldName and value to the list.
addAccumulator(String) - Method in class ai.djl.timeseries.evaluator.Rmsse
addAgeFeature(NDManager, FieldName, FieldName, int, boolean, TimeSeriesData) - Static method in class ai.djl.timeseries.transform.feature.Feature: Adds on 'age' feature to the TimeSeriesData.
addAgeFeature(NDManager, FieldName, FieldName, int, boolean, TimeSeriesData, boolean) - Static method in class ai.djl.timeseries.transform.feature.Feature: Adds on 'age' feature to the TimeSeriesData.
addAgeFeature(NDManager, FieldName, FieldName, int, TimeSeriesData) - Static method in class ai.djl.timeseries.transform.feature.Feature: Adds on 'age' feature to the TimeSeriesData, set logScale = ture
AddAgeFeature - Class in ai.djl.timeseries.transform.feature: Add age feature through the prediction length.
AddAgeFeature(FieldName, FieldName, int) - Constructor for class ai.djl.timeseries.transform.feature.AddAgeFeature: Constructs a AddAgeFeature.
AddAgeFeature(FieldName, FieldName, int, boolean) - Constructor for class ai.djl.timeseries.transform.feature.AddAgeFeature: Constructs a AddAgeFeature.
addFeature(String, FieldName) - Method in class ai.djl.timeseries.dataset.M5Forecast.Builder: Add a feature to the features set of the filed name.
addFeature(String, FieldName, boolean) - Method in class ai.djl.timeseries.dataset.M5Forecast.Builder: Add a feature to the features set of the filed name with onehot encoding.
addFieldFeature(FieldName, Feature) - Method in class ai.djl.timeseries.dataset.CsvTimeSeriesDataset.CsvBuilder: Add the features to the correspongding FieldName.
addObservedValuesIndicator(NDManager, FieldName, FieldName, TimeSeriesData) - Static method in class ai.djl.timeseries.transform.feature.Feature: Replaces missing values in a NDArray (NaNs) with a dummy value and adds an "observed"-indicator that is "1" when values are observed and "0" when values are missing.
AddObservedValuesIndicator - Class in ai.djl.timeseries.transform.feature: Add observed value for the target data.
AddObservedValuesIndicator(FieldName, FieldName) - Constructor for class ai.djl.timeseries.transform.feature.AddObservedValuesIndicator: Constructs a AddObservedValuesIndicator.
addTimeFeature(NDManager, FieldName, FieldName, FieldName, List<BiFunction<NDManager, List<LocalDateTime>, NDArray>>, int, String, TimeSeriesData) - Static method in class ai.djl.timeseries.transform.feature.Feature: Adds a set of time features.
addTimeFeature(NDManager, FieldName, FieldName, FieldName, List<BiFunction<NDManager, List<LocalDateTime>, NDArray>>, int, String, TimeSeriesData, boolean) - Static method in class ai.djl.timeseries.transform.feature.Feature: Adds a set of time features.
AddTimeFeature - Class in ai.djl.timeseries.transform.feature: Add time feature by frequency.
AddTimeFeature(FieldName, FieldName, FieldName, List<BiFunction<NDManager, List<LocalDateTime>, NDArray>>, int, String) - Constructor for class ai.djl.timeseries.transform.feature.AddTimeFeature: Constructs a AddTimeFeature.
AffineTransformed - Class in ai.djl.timeseries.distribution: Represents the distribution of an affinely transformed random variable.
AffineTransformed(Distribution, NDArray, NDArray) - Constructor for class ai.djl.timeseries.distribution.AffineTransformed: Construct a new AffineTransformed
ai.djl.timeseries - package ai.djl.timeseries: Contains classes to support TimeSeries in djl.
ai.djl.timeseries.block - package ai.djl.timeseries.block: Contains the basic block classes.
ai.djl.timeseries.dataset - package ai.djl.timeseries.dataset: Contains the basic dataset classes.
ai.djl.timeseries.distribution - package ai.djl.timeseries.distribution: Contains classes to support distribution in djl.
ai.djl.timeseries.distribution.output - package ai.djl.timeseries.distribution.output: Contains classes to support construct distribution and project arguments.
ai.djl.timeseries.evaluator - package ai.djl.timeseries.evaluator: Contains the evaluator classes.
ai.djl.timeseries.model.deepar - package ai.djl.timeseries.model.deepar: Contains blocks for deepar models.
ai.djl.timeseries.timefeature - package ai.djl.timeseries.timefeature: Contains utils to get time feature from prediction frequency.
ai.djl.timeseries.transform - package ai.djl.timeseries.transform: Contains transform classes.
ai.djl.timeseries.transform.convert - package ai.djl.timeseries.transform.convert: Contains transform for TimeSeriesData to convert NDArray.
ai.djl.timeseries.transform.feature - package ai.djl.timeseries.transform.feature: Contains transform for TimeSeriesData to generate time feature.
ai.djl.timeseries.transform.field - package ai.djl.timeseries.transform.field: Contains transform for TimeSeriesData to CRUD name field.
ai.djl.timeseries.transform.split - package ai.djl.timeseries.transform.split: Contains transform for TimeSeriesData to split time feature for inference and train.
ai.djl.timeseries.translator - package ai.djl.timeseries.translator: Contains translators for TimeSeries models inference.
ArgProj - Class in ai.djl.timeseries.distribution.output: A Block used to map the output of a dense layer to statistical parameters, like mean and standard deviation.
ArgProj.Builder - Class in ai.djl.timeseries.distribution.output: The Builder to construct a ArgProj type of Block.
argsDim - Variable in class ai.djl.timeseries.distribution.output.DistributionOutput
asArray(FieldName, int, DataType, TimeSeriesData) - Static method in class ai.djl.timeseries.transform.convert.Convert: Converts the data type of NDArray and check its dimension.
asArray(FieldName, int, TimeSeriesData) - Static method in class ai.djl.timeseries.transform.convert.Convert: Converts the data type of NDArray and check its dimension.
AsArray - Class in ai.djl.timeseries.transform.convert: Convert the data type of NDArray and check its dimension.
AsArray(FieldName, int) - Constructor for class ai.djl.timeseries.transform.convert.AsArray: Constructs a AsArray.
AsArray(FieldName, int, DataType) - Constructor for class ai.djl.timeseries.transform.convert.AsArray: Constructs a AsArray.
availableSize() - Method in class ai.djl.timeseries.dataset.CsvTimeSeriesDataset
axis - Variable in class ai.djl.timeseries.transform.InstanceSampler

B

BaseBuilder() - Constructor for class ai.djl.timeseries.translator.BaseTimeSeriesTranslator.BaseBuilder
BaseTimeSeriesTranslator - Class in ai.djl.timeseries.translator: Built-in Translator that provides default TimeSeriesTranslator config process.
BaseTimeSeriesTranslator(BaseTimeSeriesTranslator.BaseBuilder<?>) - Constructor for class ai.djl.timeseries.translator.BaseTimeSeriesTranslator: Constructs a new TimeSeriesTranslator instance with the provided builder.
BaseTimeSeriesTranslator.BaseBuilder<T extends BaseTimeSeriesTranslator.BaseBuilder<T>> - Class in ai.djl.timeseries.translator: A builder to extend for all classes extend the BaseTimeSeriesTranslator.
batchifier - Variable in class ai.djl.timeseries.translator.BaseTimeSeriesTranslator.BaseBuilder
build() - Method in class ai.djl.timeseries.block.FeatureEmbedder.Builder: Return the constructed FeatureEmbedder.
build() - Method in class ai.djl.timeseries.block.FeatureEmbedding.Builder: Return the constructed FeatureEmbedding.
build() - Method in class ai.djl.timeseries.block.MeanScaler.Builder: Return the constructed MeanScaler.
build() - Method in class ai.djl.timeseries.block.NopScaler.Builder: Return constructed NOPScaler.
build() - Method in class ai.djl.timeseries.dataset.CsvTimeSeriesDataset.CsvBuilder: Build the new CsvTimeSeriesDataset.
build() - Method in class ai.djl.timeseries.dataset.M5Forecast.Builder: Build the new M5Forecast.
build() - Method in class ai.djl.timeseries.distribution.Distribution.DistributionBuilder: Build a Distribution.
build() - Method in class ai.djl.timeseries.distribution.NegativeBinomial.Builder: Build a Distribution.
build() - Method in class ai.djl.timeseries.distribution.output.ArgProj.Builder: Build a ArgProj block.
build() - Method in class ai.djl.timeseries.distribution.StudentT.Builder: Build a Distribution.
build() - Method in class ai.djl.timeseries.translator.DeepARTranslator.Builder: Builds the translator.
build() - Method in class ai.djl.timeseries.translator.TransformerTranslator.Builder: Builds the translator.
builder() - Static method in class ai.djl.timeseries.block.FeatureEmbedder: Return a builder to build an FeatureEmbedder.
builder() - Static method in class ai.djl.timeseries.block.FeatureEmbedding: Return a builder to build an FeatureEmbedding.
builder() - Static method in class ai.djl.timeseries.block.MeanScaler: Create a builder to build a MeanScaler.
builder() - Static method in class ai.djl.timeseries.block.NopScaler: Create a builder to build a NopScaler.
builder() - Static method in class ai.djl.timeseries.dataset.M5Forecast: Creates a builder to build a M5Forecast.
builder() - Static method in class ai.djl.timeseries.distribution.NegativeBinomial: Creates a builder to build a NegativeBinomial.
builder() - Static method in class ai.djl.timeseries.distribution.output.ArgProj: Creates a builder to build a ArgProj.
builder() - Static method in class ai.djl.timeseries.distribution.StudentT: Creates a builder to build a NegativeBinomial.
builder() - Static method in class ai.djl.timeseries.model.deepar.DeepARNetwork: Create a builder to build a DeepARTrainingNetwork or DeepARPredictionNetwork.
builder() - Static method in class ai.djl.timeseries.translator.DeepARTranslator: Creates a builder to build a DeepARTranslator.
builder() - Static method in class ai.djl.timeseries.translator.TransformerTranslator: Creates a builder to build a TransformerTranslator.
builder(Map<String, ?>) - Static method in class ai.djl.timeseries.translator.DeepARTranslator: Creates a builder to build a DeepARTranslator.
builder(Map<String, ?>) - Static method in class ai.djl.timeseries.translator.TransformerTranslator: Creates a builder to build a TransformerTranslator.
Builder() - Constructor for class ai.djl.timeseries.block.FeatureEmbedder.Builder
Builder() - Constructor for class ai.djl.timeseries.block.FeatureEmbedding.Builder
Builder() - Constructor for class ai.djl.timeseries.block.NopScaler.Builder
Builder() - Constructor for class ai.djl.timeseries.distribution.NegativeBinomial.Builder
Builder() - Constructor for class ai.djl.timeseries.distribution.output.ArgProj.Builder
Builder() - Constructor for class ai.djl.timeseries.distribution.StudentT.Builder
Builder() - Constructor for class ai.djl.timeseries.model.deepar.DeepARNetwork.Builder
buildPredictionNetwork() - Method in class ai.djl.timeseries.model.deepar.DeepARNetwork.Builder: Build a DeepARPredictionNetwork block.
buildTrainingNetwork() - Method in class ai.djl.timeseries.model.deepar.DeepARNetwork.Builder: Build a DeepARTrainingNetwork block.

C

call(NDArray) - Method in class ai.djl.timeseries.transform.ExpectedNumInstanceSampler: Call the sample process.
call(NDArray) - Method in class ai.djl.timeseries.transform.InstanceSampler: Call the sample process.
call(NDArray) - Method in class ai.djl.timeseries.transform.PredictionSplitSampler: Call the sample process.
cardinality - Variable in class ai.djl.timeseries.model.deepar.DeepARNetwork
configPostProcess(Map<String, ?>) - Method in class ai.djl.timeseries.translator.BaseTimeSeriesTranslator.BaseBuilder
configPreProcess(Map<String, ?>) - Method in class ai.djl.timeseries.translator.BaseTimeSeriesTranslator.BaseBuilder
configPreProcess(Map<String, ?>) - Method in class ai.djl.timeseries.translator.DeepARTranslator.Builder
configPreProcess(Map<String, ?>) - Method in class ai.djl.timeseries.translator.TransformerTranslator.Builder
contextLength - Variable in class ai.djl.timeseries.dataset.TimeSeriesDataset
contextLength - Variable in class ai.djl.timeseries.dataset.TimeSeriesDataset.TimeSeriesBuilder
contextLength - Variable in class ai.djl.timeseries.model.deepar.DeepARNetwork
contextLength - Variable in class ai.djl.timeseries.translator.BaseTimeSeriesTranslator.BaseBuilder
contextLength - Variable in class ai.djl.timeseries.translator.BaseTimeSeriesTranslator
Convert - Class in ai.djl.timeseries.transform.convert: A class used to convert the shape of NDArray in TimeSeriesData.
copyDim(int) - Method in class ai.djl.timeseries.SampleForecast: Returns a new Forecast object with only the selected sub-dimension.
createPredictionTransformation(NDManager) - Method in class ai.djl.timeseries.model.deepar.DeepARNetwork: Construct a prediction transformation of deepar model.
createTrainingTransformation(NDManager) - Method in class ai.djl.timeseries.model.deepar.DeepARNetwork: Construct a training transformation of deepar model.
CsvBuilder() - Constructor for class ai.djl.timeseries.dataset.CsvTimeSeriesDataset.CsvBuilder
csvFormat - Variable in class ai.djl.timeseries.dataset.CsvTimeSeriesDataset.CsvBuilder
csvFormat - Variable in class ai.djl.timeseries.dataset.CsvTimeSeriesDataset
csvRecords - Variable in class ai.djl.timeseries.dataset.CsvTimeSeriesDataset
CsvTimeSeriesDataset - Class in ai.djl.timeseries.dataset: CsvTimeSeriesDataset represents the dataset that store in a .csv file.
CsvTimeSeriesDataset(CsvTimeSeriesDataset.CsvBuilder<?>) - Constructor for class ai.djl.timeseries.dataset.CsvTimeSeriesDataset
CsvTimeSeriesDataset.CsvBuilder<T extends CsvTimeSeriesDataset.CsvBuilder<T>> - Class in ai.djl.timeseries.dataset: Used to build a CsvTimeSeriesDataset.
csvUrl - Variable in class ai.djl.timeseries.dataset.CsvTimeSeriesDataset.CsvBuilder
csvUrl - Variable in class ai.djl.timeseries.dataset.CsvTimeSeriesDataset

D

dataRequired() - Method in interface ai.djl.timeseries.dataset.TimeFeaturizer
dayOfMonth(NDManager, List<LocalDateTime>) - Static method in class ai.djl.timeseries.timefeature.TimeFeature: Computes feature by days fo the month.
dayOfWeek(NDManager, List<LocalDateTime>) - Static method in class ai.djl.timeseries.timefeature.TimeFeature: Computes feature by days of the week.
dayOfYear(NDManager, List<LocalDateTime>) - Static method in class ai.djl.timeseries.timefeature.TimeFeature: Computes feature by days of the year.
DeepARNetwork - Class in ai.djl.timeseries.model.deepar: Implements the deepar model.
DeepARNetwork.Builder - Class in ai.djl.timeseries.model.deepar: The builder to construct a DeepARTrainingNetwork or DeepARPredictionNetwork.
DeepARPredictionNetwork - Class in ai.djl.timeseries.model.deepar: A deepar implements for prediction.
DeepARTrainingNetwork - Class in ai.djl.timeseries.model.deepar: A deepar implements for training.
DeepARTranslator - Class in ai.djl.timeseries.translator: The Translator for DeepAR time series forecasting tasks.
DeepARTranslator(DeepARTranslator.Builder) - Constructor for class ai.djl.timeseries.translator.DeepARTranslator: Constructs a new DeepARTranslator instance.
DeepARTranslator.Builder - Class in ai.djl.timeseries.translator: The builder for DeepAR translator.
DeepARTranslatorFactory - Class in ai.djl.timeseries.translator: A TranslatorFactory that creates a DeepARTranslator instance.
DeepARTranslatorFactory() - Constructor for class ai.djl.timeseries.translator.DeepARTranslatorFactory
deFeaturize(float[]) - Method in interface ai.djl.timeseries.dataset.TimeFeaturizer
dim - Variable in class ai.djl.timeseries.block.Scaler
dim - Variable in class ai.djl.timeseries.block.Scaler.ScalerBuilder
distrArgs - Variable in class ai.djl.timeseries.distribution.Distribution.DistributionBuilder
Distribution - Class in ai.djl.timeseries.distribution: An abstract class representing probability distribution.
Distribution() - Constructor for class ai.djl.timeseries.distribution.Distribution
Distribution.DistributionBuilder<T extends Distribution.DistributionBuilder<T>> - Class in ai.djl.timeseries.distribution: A builder to extend for all classes extend the Distribution.
distributionBuilder() - Method in class ai.djl.timeseries.distribution.output.DistributionOutput: Return the associated DistributionBuilder, given the collection of constructor arguments and, optionally, a scale tensor.
distributionBuilder() - Method in class ai.djl.timeseries.distribution.output.NegativeBinomialOutput: Return the associated DistributionBuilder, given the collection of constructor arguments and, optionally, a scale tensor.
distributionBuilder() - Method in class ai.djl.timeseries.distribution.output.StudentTOutput: Return the associated DistributionBuilder, given the collection of constructor arguments and, optionally, a scale tensor.
DistributionBuilder() - Constructor for class ai.djl.timeseries.distribution.Distribution.DistributionBuilder
DistributionLoss - Class in ai.djl.timeseries.distribution: DistributionLoss calculates loss for a given distribution.
DistributionLoss(String, DistributionOutput) - Constructor for class ai.djl.timeseries.distribution.DistributionLoss: Calculates Distribution Loss between the label and distribution.
DistributionOutput - Class in ai.djl.timeseries.distribution.output: A class to construct a distribution given the output of a network.
DistributionOutput() - Constructor for class ai.djl.timeseries.distribution.output.DistributionOutput
distrOutput - Variable in class ai.djl.timeseries.model.deepar.DeepARNetwork
domainMap(NDList) - Method in class ai.djl.timeseries.distribution.output.DistributionOutput: Convert arguments to the right shape and domain.
domainMap(NDList) - Method in class ai.djl.timeseries.distribution.output.NegativeBinomialOutput: Convert arguments to the right shape and domain.
domainMap(NDList) - Method in class ai.djl.timeseries.distribution.output.StudentTOutput: Convert arguments to the right shape and domain.

E

embedder - Variable in class ai.djl.timeseries.model.deepar.DeepARNetwork
embeddingDimension - Variable in class ai.djl.timeseries.model.deepar.DeepARNetwork
equals(Object) - Method in class ai.djl.timeseries.TimeSeriesData: *
evaluate(NDList, NDList) - Method in class ai.djl.timeseries.distribution.DistributionLoss
evaluate(NDList, NDList) - Method in class ai.djl.timeseries.evaluator.Rmsse
evaluateHelper(NDList, NDList) - Method in class ai.djl.timeseries.evaluator.Rmsse
ExpectedNumInstanceSampler - Class in ai.djl.timeseries.transform: Keeps track of the average time series length and adjusts the probability per time point such that on average `num_instances` training examples are generated per time series.
ExpectedNumInstanceSampler(int, int, int, double) - Constructor for class ai.djl.timeseries.transform.ExpectedNumInstanceSampler: Construct a new instance of ExpectedNumInstanceSampler.

F

FEAT_AGE - Enum constant in enum class ai.djl.timeseries.dataset.FieldName
FEAT_CONST - Enum constant in enum class ai.djl.timeseries.dataset.FieldName
FEAT_DYNAMIC - Enum constant in enum class ai.djl.timeseries.dataset.FieldName
FEAT_DYNAMIC_CAT - Enum constant in enum class ai.djl.timeseries.dataset.FieldName
FEAT_DYNAMIC_REAL - Enum constant in enum class ai.djl.timeseries.dataset.FieldName
FEAT_DYNAMIC_REAL_LEGACY - Enum constant in enum class ai.djl.timeseries.dataset.FieldName
FEAT_STATIC_CAT - Enum constant in enum class ai.djl.timeseries.dataset.FieldName
FEAT_STATIC_REAL - Enum constant in enum class ai.djl.timeseries.dataset.FieldName
FEAT_TIME - Enum constant in enum class ai.djl.timeseries.dataset.FieldName
Feature - Class in ai.djl.timeseries.transform.feature: this is a class use to add feature in TimeSeriesData.
FeatureEmbedder - Class in ai.djl.timeseries.block: Embed a sequence of categorical features.
FeatureEmbedder.Builder - Class in ai.djl.timeseries.block: The builder to construct a FeatureEmbedder type of Block.
FeatureEmbedding - Class in ai.djl.timeseries.block: An implement of nn.embedding.
FeatureEmbedding.Builder - Class in ai.djl.timeseries.block: The builder to construct a FeatureEmbedding type of Block.
featurize(DynamicBuffer, String) - Method in interface ai.djl.timeseries.dataset.TimeFeaturizer
featurize(String) - Method in interface ai.djl.timeseries.dataset.TimeFeaturizer: Return the parsed time data.
featurize(String) - Method in class ai.djl.timeseries.dataset.TimeFeaturizers.ConstantTimeFeaturizer: Return the parsed time data.
featurize(String) - Method in class ai.djl.timeseries.dataset.TimeFeaturizers.PatternTimeFeaturizer: Return the parsed time data.
Field - Class in ai.djl.timeseries.transform.field: A utility class that used to operate field name in TimeSeriesData.
fieldFeatures - Variable in class ai.djl.timeseries.dataset.CsvTimeSeriesDataset.CsvBuilder
fieldFeatures - Variable in class ai.djl.timeseries.dataset.CsvTimeSeriesDataset
FieldName - Enum Class in ai.djl.timeseries.dataset: Represents the name field of elements in a TimeSeriesData as an enum.
Forecast - Class in ai.djl.timeseries: An abstract class representing the forecast results for the time series data.
Forecast(LocalDateTime, int, String) - Constructor for class ai.djl.timeseries.Forecast: Constructs a Forecast instance.
FORECAST_START - Enum constant in enum class ai.djl.timeseries.dataset.FieldName
forwardInternal(ParameterStore, NDList, boolean, PairList<String, Object>) - Method in class ai.djl.timeseries.block.FeatureEmbedder
forwardInternal(ParameterStore, NDList, boolean, PairList<String, Object>) - Method in class ai.djl.timeseries.block.FeatureEmbedding
forwardInternal(ParameterStore, NDList, boolean, PairList<String, Object>) - Method in class ai.djl.timeseries.block.MeanScaler
forwardInternal(ParameterStore, NDList, boolean, PairList<String, Object>) - Method in class ai.djl.timeseries.block.NopScaler
forwardInternal(ParameterStore, NDList, boolean, PairList<String, Object>) - Method in class ai.djl.timeseries.distribution.output.ArgProj
forwardInternal(ParameterStore, NDList, boolean, PairList<String, Object>) - Method in class ai.djl.timeseries.model.deepar.DeepARPredictionNetwork
forwardInternal(ParameterStore, NDList, boolean, PairList<String, Object>) - Method in class ai.djl.timeseries.model.deepar.DeepARTrainingNetwork
freq - Variable in class ai.djl.timeseries.Forecast
freq - Variable in class ai.djl.timeseries.model.deepar.DeepARNetwork
freq - Variable in class ai.djl.timeseries.translator.BaseTimeSeriesTranslator.BaseBuilder
freq - Variable in class ai.djl.timeseries.translator.BaseTimeSeriesTranslator
freq() - Method in class ai.djl.timeseries.Forecast: Returns the prediction frequency like "D", "H"....

G

get(NDManager, long) - Method in class ai.djl.timeseries.dataset.TimeSeriesDataset: TimeSeriesDataset override the get function so that it can preprocess the feature data as timeseries package way.
get(FieldName) - Method in class ai.djl.timeseries.TimeSeriesData: Returns the value for the fieldName.
getAccumulator(String) - Method in class ai.djl.timeseries.evaluator.Rmsse
getArgsArray() - Method in class ai.djl.timeseries.distribution.output.DistributionOutput: Return an array containing all the argument names.
getArgsProj() - Method in class ai.djl.timeseries.distribution.output.DistributionOutput: Return the corresponding projection block based on the arguments dimension of different distributions.
getArgsProj(String) - Method in class ai.djl.timeseries.distribution.output.DistributionOutput: Return the corresponding projection block based on the arguments dimension of different ditributions.
getAvailableFeatures() - Method in class ai.djl.timeseries.dataset.M5Forecast.Builder: Returns the available features of this dataset.
getBatchifier() - Method in class ai.djl.timeseries.translator.BaseTimeSeriesTranslator
getBounds(NDArray) - Method in class ai.djl.timeseries.transform.InstanceSampler: Returns the sampled indices bounds.
getCardinality() - Method in class ai.djl.timeseries.dataset.M5Forecast: Return the cardinality of the dataset.
getCell(long, String) - Method in class ai.djl.timeseries.dataset.CsvTimeSeriesDataset: Returns a cell in the dataset.
getConstantTimeFeaturizer(LocalDateTime) - Static method in class ai.djl.timeseries.dataset.TimeFeaturizers: Construct a TimeFeaturizers.ConstantTimeFeaturizer.
getContextLength() - Method in class ai.djl.timeseries.model.deepar.DeepARNetwork: Return the context length.
getForecastStartTime() - Method in class ai.djl.timeseries.TimeSeriesData: Returns the time series forecasting time.
getHistoryLength() - Method in class ai.djl.timeseries.model.deepar.DeepARNetwork: Return the history length.
getLagsForFreq(String) - Static method in class ai.djl.timeseries.timefeature.Lag: Generates a list of lags that are appropriate for the given frequency string.
getLagsForFreq(String, int) - Static method in class ai.djl.timeseries.timefeature.Lag: Generates a list of lags that are appropriate for the given frequency string.
getMultipleOfTimeOffset() - Method in class ai.djl.timeseries.timefeature.TimeOffset: Return the multiple of TimeOffset.
getName() - Method in class ai.djl.timeseries.timefeature.TimeOffset: Return the granularity name of TimeOffset.
getNumSamples() - Method in class ai.djl.timeseries.SampleForecast: Returns the number of samples representing the forecast.
getOutputShapes(Shape[]) - Method in class ai.djl.timeseries.block.FeatureEmbedder
getOutputShapes(Shape[]) - Method in class ai.djl.timeseries.block.FeatureEmbedding
getOutputShapes(Shape[]) - Method in class ai.djl.timeseries.block.Scaler
getOutputShapes(Shape[]) - Method in class ai.djl.timeseries.distribution.output.ArgProj
getOutputShapes(Shape[]) - Method in class ai.djl.timeseries.model.deepar.DeepARPredictionNetwork
getOutputShapes(Shape[]) - Method in class ai.djl.timeseries.model.deepar.DeepARTrainingNetwork
getPatternTimeFeaturizer(String) - Static method in class ai.djl.timeseries.dataset.TimeFeaturizers: Construct a TimeFeaturizers.PatternTimeFeaturizer.
getPredictionLength() - Method in class ai.djl.timeseries.Forecast: Returns the time length of forecast.
getRowFeatures(NDManager, long, List<Feature>) - Method in class ai.djl.timeseries.dataset.CsvTimeSeriesDataset: Returns the designated features (either data or label features) from a row.
getSortedSamples() - Method in class ai.djl.timeseries.SampleForecast: Returns the sorted sample array.
getStartTime() - Method in class ai.djl.timeseries.TimeSeriesData: Returns the time series start time.
getStartTime(long) - Method in class ai.djl.timeseries.dataset.CsvTimeSeriesDataset: Return the prediction start time for the given index.
getSupportedTypes() - Method in class ai.djl.timeseries.translator.DeepARTranslatorFactory
getSupportedTypes() - Method in class ai.djl.timeseries.translator.TransformerTranslatorFactory
getTimeSeriesData(NDManager, long) - Method in class ai.djl.timeseries.dataset.CsvTimeSeriesDataset: Return the TimeSeriesData for the given index from the TimeSeriesDataset.
getTimeSeriesData(NDManager, long) - Method in class ai.djl.timeseries.dataset.TimeSeriesDataset: Return the TimeSeriesData for the given index from the TimeSeriesDataset.
getValueInSupport() - Method in class ai.djl.timeseries.distribution.output.DistributionOutput: A float that will have a valid numeric value when computing the log-loss of the corresponding distribution.

H

hashCode() - Method in class ai.djl.timeseries.TimeSeriesData: *
historyLength - Variable in class ai.djl.timeseries.model.deepar.DeepARNetwork
hourOfDay(NDManager, List<LocalDateTime>) - Static method in class ai.djl.timeseries.timefeature.TimeFeature: Computes feature by hours.

I

IdentityTransform() - Constructor for class ai.djl.timeseries.transform.TimeSeriesTransform.IdentityTransform
identityTransformation() - Static method in interface ai.djl.timeseries.transform.TimeSeriesTransform: Construct a list of TimeSeriesTransform that performs identity function.
initializeChildBlocks(NDManager, DataType, Shape...) - Method in class ai.djl.timeseries.block.FeatureEmbedder
initializeChildBlocks(NDManager, DataType, Shape...) - Method in class ai.djl.timeseries.distribution.output.ArgProj
initializeChildBlocks(NDManager, DataType, Shape...) - Method in class ai.djl.timeseries.model.deepar.DeepARNetwork
InstanceSampler - Class in ai.djl.timeseries.transform: An InstanceSampler is called with the time series ``ts``, and returns a set of indices at which training instances will be generated.
InstanceSampler(int, int, int) - Constructor for class ai.djl.timeseries.transform.InstanceSampler: Constructs a new instance of InstanceSampler.
instanceSplit(NDManager, FieldName, FieldName, FieldName, FieldName, InstanceSampler, int, int, int, boolean, FieldName[], float, TimeSeriesData) - Static method in class ai.djl.timeseries.transform.split.Split: Selects training instances, by slicing the target and other time series like arrays at random points in training mode or at the last time point in prediction mode.
instanceSplit(NDManager, FieldName, FieldName, FieldName, FieldName, InstanceSampler, int, int, FieldName[], float, TimeSeriesData) - Static method in class ai.djl.timeseries.transform.split.Split: Selects training instances, by slicing the target and other time series like arrays at random points in training mode or at the last time point in prediction mode.
InstanceSplit - Class in ai.djl.timeseries.transform.split: Use the InstanceSampler to split the time series data into past and future part.
InstanceSplit(FieldName, FieldName, FieldName, FieldName, InstanceSampler, int, int, int, boolean, FieldName[], float) - Constructor for class ai.djl.timeseries.transform.split.InstanceSplit: Constructs a InstanceSplit.
InstanceSplit(FieldName, FieldName, FieldName, FieldName, InstanceSampler, int, int, FieldName[], float) - Constructor for class ai.djl.timeseries.transform.split.InstanceSplit: Constructs a InstanceSplit.
IS_PAD - Enum constant in enum class ai.djl.timeseries.dataset.FieldName
ITEM_ID - Enum constant in enum class ai.djl.timeseries.dataset.FieldName

K

keepDim - Variable in class ai.djl.timeseries.block.Scaler
keepDim - Variable in class ai.djl.timeseries.block.Scaler.ScalerBuilder

L

Lag - Class in ai.djl.timeseries.timefeature: This class contains static method for get lags from frequency.
laggedSequenceValues(List<Integer>, NDArray, NDArray) - Method in class ai.djl.timeseries.model.deepar.DeepARNetwork: Construct an NDArray of lagged values from a given sequence.
lagsSeq - Variable in class ai.djl.timeseries.model.deepar.DeepARNetwork
loc - Variable in class ai.djl.timeseries.distribution.Distribution.DistributionBuilder
logProb(NDArray) - Method in class ai.djl.timeseries.distribution.AffineTransformed: Compute the log of the probability density/mass function evaluated at target.
logProb(NDArray) - Method in class ai.djl.timeseries.distribution.Distribution: Compute the log of the probability density/mass function evaluated at target.
logProb(NDArray) - Method in class ai.djl.timeseries.distribution.NegativeBinomial: Compute the log of the probability density/mass function evaluated at target.
logProb(NDArray) - Method in class ai.djl.timeseries.distribution.StudentT: Compute the log of the probability density/mass function evaluated at target.

M

M5Forecast - Class in ai.djl.timeseries.dataset: M5 Forecasting - Accuracy from https://www.kaggle.com/competitions/m5-forecasting-accuracy
M5Forecast(M5Forecast.Builder) - Constructor for class ai.djl.timeseries.dataset.M5Forecast: Creates a new instance of M5Forecast with the given necessary configurations.
M5Forecast.Builder - Class in ai.djl.timeseries.dataset: Used to build a M5Forecast.
mean() - Method in class ai.djl.timeseries.distribution.AffineTransformed: Return the mean of the distribution.
mean() - Method in class ai.djl.timeseries.distribution.Distribution: Return the mean of the distribution.
mean() - Method in class ai.djl.timeseries.distribution.NegativeBinomial: Return the mean of the distribution.
mean() - Method in class ai.djl.timeseries.distribution.StudentT: Return the mean of the distribution.
mean() - Method in class ai.djl.timeseries.Forecast: Computes and returns the forecast mean.
mean() - Method in class ai.djl.timeseries.SampleForecast: Computes and returns the forecast mean.
MeanScaler - Class in ai.djl.timeseries.block: A class computes a scaling factor as the weighted average absolute value along dimension dim, and scales the data accordingly.
MeanScaler.Builder - Class in ai.djl.timeseries.block: The builder to construct a MeanScaler.
median() - Method in class ai.djl.timeseries.Forecast: Computes the median of forecast.
minFuture - Variable in class ai.djl.timeseries.transform.InstanceSampler
minPast - Variable in class ai.djl.timeseries.transform.InstanceSampler
minuteOfHour(NDManager, List<LocalDateTime>) - Static method in class ai.djl.timeseries.timefeature.TimeFeature: Computes feature by minutes.
monthOfYear(NDManager, List<LocalDateTime>) - Static method in class ai.djl.timeseries.timefeature.TimeFeature: Computes feature by months of the year.

N

NegativeBinomial - Class in ai.djl.timeseries.distribution: Negative binomial distribution.
NegativeBinomial.Builder - Class in ai.djl.timeseries.distribution: The builder to construct a NegativeBinomial.
NegativeBinomialOutput - Class in ai.djl.timeseries.distribution.output: NegativeBinomialOutput is a DistributionOutput for the negative binomial distribution.
NegativeBinomialOutput() - Constructor for class ai.djl.timeseries.distribution.output.NegativeBinomialOutput: Construct a negative binomial output with two arguments, total_count and logits.
newInstance(Class<I>, Class<O>, Model, Map<String, ?>) - Method in class ai.djl.timeseries.translator.DeepARTranslatorFactory
newInstance(Class<I>, Class<O>, Model, Map<String, ?>) - Method in class ai.djl.timeseries.translator.TransformerTranslatorFactory
newTestSplitSampler() - Static method in class ai.djl.timeseries.transform.PredictionSplitSampler: Creates a new instance PredictionSplitSampler for test.
newValidationSplitSampler() - Static method in class ai.djl.timeseries.transform.PredictionSplitSampler: Creates a new instance PredictionSplitSampler for validation.
NopScaler - Class in ai.djl.timeseries.block: A class assigns a scaling factor equal to 1 along dimension dim, and therefore applies no scaling to the input data.
NopScaler.Builder - Class in ai.djl.timeseries.block: The builder to construct a NopScaler.
numParallelSamples - Variable in class ai.djl.timeseries.model.deepar.DeepARNetwork

O

OBSERVED_VALUES - Enum constant in enum class ai.djl.timeseries.dataset.FieldName
optArtifactId(String) - Method in class ai.djl.timeseries.dataset.M5Forecast.Builder: Sets the optional artifactId.
optBachifier(Batchifier) - Method in class ai.djl.timeseries.translator.BaseTimeSeriesTranslator.BaseBuilder: Sets the Batchifier for the Translator.
optContextLength(int) - Method in class ai.djl.timeseries.model.deepar.DeepARNetwork.Builder: Set the optional context length.
optCsvFile(Path) - Method in class ai.djl.timeseries.dataset.CsvTimeSeriesDataset.CsvBuilder: Set the optional CSV file path.
optCsvUrl(String) - Method in class ai.djl.timeseries.dataset.CsvTimeSeriesDataset.CsvBuilder: Set the optional CSV file URL.
optDistrOutput(DistributionOutput) - Method in class ai.djl.timeseries.model.deepar.DeepARNetwork.Builder: Set the optional DistributionOutput default StudentTOutput.
optDropRate(float) - Method in class ai.djl.timeseries.model.deepar.DeepARNetwork.Builder: Set the optional number of rnn drop rate.
optEmbeddingDimension(List<Integer>) - Method in class ai.djl.timeseries.model.deepar.DeepARNetwork.Builder: Set the optional embedding dimension.
optGroupId(String) - Method in class ai.djl.timeseries.dataset.M5Forecast.Builder: Sets optional groupId.
optHiddenSize(int) - Method in class ai.djl.timeseries.model.deepar.DeepARNetwork.Builder: Set the optional number of rnn hidden size.
optKeepDim(boolean) - Method in class ai.djl.timeseries.block.Scaler.ScalerBuilder: Set whether to keep dim.
optLagsSeq(List<Integer>) - Method in class ai.djl.timeseries.model.deepar.DeepARNetwork.Builder: Set the optional lags sequence, default generate from frequency.
optLoc(NDArray) - Method in class ai.djl.timeseries.distribution.Distribution.DistributionBuilder: Set the affine location of the probability.
optMinimumScale(float) - Method in class ai.djl.timeseries.block.MeanScaler.Builder: Sets the minimum scalar of the data.
optNumLayers(int) - Method in class ai.djl.timeseries.model.deepar.DeepARNetwork.Builder: Set the optional number of rnn layers.
optNumParallelSamples(int) - Method in class ai.djl.timeseries.model.deepar.DeepARNetwork.Builder: Set the optional number parallel samples.
optPrefix(String) - Method in class ai.djl.timeseries.distribution.output.ArgProj.Builder: Set the block name prefix.
optRepository(Repository) - Method in class ai.djl.timeseries.dataset.M5Forecast.Builder: Sets the optional repository.
optScale(NDArray) - Method in class ai.djl.timeseries.distribution.Distribution.DistributionBuilder: Set the affine scale for the probability distribution.
optUsage(Dataset.Usage) - Method in class ai.djl.timeseries.dataset.M5Forecast.Builder: Sets the optional usage.
optUseFeatDynamicReal(boolean) - Method in class ai.djl.timeseries.model.deepar.DeepARNetwork.Builder: Set whether to use dynamic real feature.
optUseFeatStaticCat(boolean) - Method in class ai.djl.timeseries.model.deepar.DeepARNetwork.Builder: Set whether to use static categorical feature.
optUseFeatStaticReal(boolean) - Method in class ai.djl.timeseries.model.deepar.DeepARNetwork.Builder: Set whether to use static real feature.

P

paramProj - Variable in class ai.djl.timeseries.model.deepar.DeepARNetwork
PAST_FEAT_DYNAMIC - Enum constant in enum class ai.djl.timeseries.dataset.FieldName
PAST_FEAT_DYNAMIC_REAL - Enum constant in enum class ai.djl.timeseries.dataset.FieldName
predictionLength - Variable in class ai.djl.timeseries.Forecast
predictionLength - Variable in class ai.djl.timeseries.model.deepar.DeepARNetwork
predictionLength - Variable in class ai.djl.timeseries.translator.BaseTimeSeriesTranslator.BaseBuilder
predictionLength - Variable in class ai.djl.timeseries.translator.BaseTimeSeriesTranslator
PredictionSplitSampler - Class in ai.djl.timeseries.transform: Sampler used for prediction.
PredictionSplitSampler(int, int, int, boolean) - Constructor for class ai.djl.timeseries.transform.PredictionSplitSampler: Constructs a new instance of PredictionSplitSampler.
prepare(Progress) - Method in class ai.djl.timeseries.dataset.CsvTimeSeriesDataset
prepare(Progress) - Method in class ai.djl.timeseries.dataset.M5Forecast
prepareFeaturizers() - Method in class ai.djl.timeseries.dataset.CsvTimeSeriesDataset: Prepares the PreparedFeaturizers.
processInput(TranslatorContext, TimeSeriesData) - Method in class ai.djl.timeseries.translator.DeepARTranslator
processInput(TranslatorContext, TimeSeriesData) - Method in class ai.djl.timeseries.translator.TransformerTranslator
processOutput(TranslatorContext, NDList) - Method in class ai.djl.timeseries.translator.DeepARTranslator
processOutput(TranslatorContext, NDList) - Method in class ai.djl.timeseries.translator.TransformerTranslator

Q

quantile(float) - Method in class ai.djl.timeseries.Forecast: Computes a quantile from the predicted distribution.
quantile(float) - Method in class ai.djl.timeseries.SampleForecast: Computes a quantile from the predicted distribution.
quantile(String) - Method in class ai.djl.timeseries.Forecast: Computes a quantile from the predicted distribution.

R

remove(FieldName) - Method in class ai.djl.timeseries.TimeSeriesData: Removes the key-value pair for the FieldName.
removeFields(List<FieldName>, TimeSeriesData) - Static method in class ai.djl.timeseries.transform.field.Field: Remove fields names if present.
RemoveFields - Class in ai.djl.timeseries.transform.field: Remove the field names.
RemoveFields(List<FieldName>) - Constructor for class ai.djl.timeseries.transform.field.RemoveFields: Constructs a RemoveFields instance.
resetAccumulator(String) - Method in class ai.djl.timeseries.evaluator.Rmsse
Rmsse - Class in ai.djl.timeseries.evaluator: A class used to calculate Root Mean Squared Scaled Error.
Rmsse(DistributionOutput) - Constructor for class ai.djl.timeseries.evaluator.Rmsse: Creates an evaluator that computes Root Mean Squared Scaled Error across axis 1.
Rmsse(String, int, DistributionOutput) - Constructor for class ai.djl.timeseries.evaluator.Rmsse: Creates an evaluator that computes Root Mean Squared Scaled Error across axis 1.
rnn - Variable in class ai.djl.timeseries.model.deepar.DeepARNetwork

S

sample() - Method in class ai.djl.timeseries.distribution.Distribution: Draw samples from the distribution.
sample(int) - Method in class ai.djl.timeseries.distribution.AffineTransformed: Draw samples from the distribution.
sample(int) - Method in class ai.djl.timeseries.distribution.Distribution: Draw samples from the distribution.
sample(int) - Method in class ai.djl.timeseries.distribution.NegativeBinomial: Draw samples from the distribution.
sample(int) - Method in class ai.djl.timeseries.distribution.StudentT: Draw samples from the distribution.
SampleForecast - Class in ai.djl.timeseries: A Forecast object, where the predicted distribution is represented internally as samples.
SampleForecast(NDArray, LocalDateTime, String) - Constructor for class ai.djl.timeseries.SampleForecast: Constructs a SampleForeCast.
scale - Variable in class ai.djl.timeseries.distribution.Distribution.DistributionBuilder
scaler - Variable in class ai.djl.timeseries.model.deepar.DeepARNetwork
Scaler - Class in ai.djl.timeseries.block: An abstract class used to scale data.
Scaler.ScalerBuilder<T extends Scaler.ScalerBuilder<T>> - Class in ai.djl.timeseries.block: A builder to extend for all classes extend the Scaler.
ScalerBuilder() - Constructor for class ai.djl.timeseries.block.Scaler.ScalerBuilder
secondOfMinute(NDManager, List<LocalDateTime>) - Static method in class ai.djl.timeseries.timefeature.TimeFeature: Computes feature by seconds.
selectField(String[], TimeSeriesData) - Static method in class ai.djl.timeseries.transform.field.Field: Only keep the listed fields.
SelectField - Class in ai.djl.timeseries.transform.field: Select preset field names.
SelectField(String[]) - Constructor for class ai.djl.timeseries.transform.field.SelectField: Constructs a SelectField instance.
self() - Method in class ai.djl.timeseries.block.MeanScaler.Builder
self() - Method in class ai.djl.timeseries.block.NopScaler.Builder
self() - Method in class ai.djl.timeseries.block.Scaler.ScalerBuilder
self() - Method in class ai.djl.timeseries.dataset.CsvTimeSeriesDataset.CsvBuilder
self() - Method in class ai.djl.timeseries.dataset.M5Forecast.Builder
self() - Method in class ai.djl.timeseries.distribution.Distribution.DistributionBuilder
self() - Method in class ai.djl.timeseries.distribution.NegativeBinomial.Builder
self() - Method in class ai.djl.timeseries.distribution.StudentT.Builder
self() - Method in class ai.djl.timeseries.translator.BaseTimeSeriesTranslator.BaseBuilder
self() - Method in class ai.djl.timeseries.translator.DeepARTranslator.Builder
self() - Method in class ai.djl.timeseries.translator.TransformerTranslator.Builder
setArgsDim(PairList<String, Integer>) - Method in class ai.djl.timeseries.distribution.output.ArgProj.Builder: Set the arguments dimensions of distribution.
setCardinalities(List<Integer>) - Method in class ai.djl.timeseries.block.FeatureEmbedder.Builder: Set the cardinality for each categorical feature.
setCardinality(List<Integer>) - Method in class ai.djl.timeseries.model.deepar.DeepARNetwork.Builder: Set the cardinality for static categorical feature.
setContextLength(int) - Method in class ai.djl.timeseries.dataset.TimeSeriesDataset.TimeSeriesBuilder: Set the model prediction context length.
setCsvFormat(CSVFormat) - Method in class ai.djl.timeseries.dataset.CsvTimeSeriesDataset.CsvBuilder: Set the CSV file format.
setDim(int) - Method in class ai.djl.timeseries.block.Scaler.ScalerBuilder: Set the dim to scale.
setDistrArgs(NDList) - Method in class ai.djl.timeseries.distribution.Distribution.DistributionBuilder: Set the appropriate arguments for the probability distribution.
setDomainMap(Function<NDList, NDList>) - Method in class ai.djl.timeseries.distribution.output.ArgProj.Builder: Set the domain map function.
setEmbeddingDims(List<Integer>) - Method in class ai.djl.timeseries.block.FeatureEmbedder.Builder: Set the number of dimensions to embed each categorical feature.
setEmbeddingSize(int) - Method in class ai.djl.timeseries.block.FeatureEmbedding.Builder: Sets the size of the embeddings.
setField(FieldName, NDArray) - Method in class ai.djl.timeseries.TimeSeriesData: Replace the existing NDArray of FieldName to the value.
setField(FieldName, NDArray, TimeSeriesData) - Static method in class ai.djl.timeseries.transform.field.Field: Sets a field in the dictionary with the given value.
setField(String, NDArray) - Method in class ai.djl.timeseries.TimeSeriesData: Replaces the existing NDArray of FieldName to the value.
SetField - Class in ai.djl.timeseries.transform.field: Use the preset value for input field names.
SetField(FieldName, NDArray) - Constructor for class ai.djl.timeseries.transform.field.SetField: Constructs a SetField.
setForecastStartTime(LocalDateTime) - Method in class ai.djl.timeseries.TimeSeriesData: Set the time series forecasting time.
setFreq(String) - Method in class ai.djl.timeseries.model.deepar.DeepARNetwork.Builder: Set the prediction frequency.
setNumEmbeddings(int) - Method in class ai.djl.timeseries.block.FeatureEmbedding.Builder: Sets the size of the dictionary of embeddings.
setPredictionLength(int) - Method in class ai.djl.timeseries.model.deepar.DeepARNetwork.Builder: Set the prediction length.
setStartTime(LocalDateTime) - Method in class ai.djl.timeseries.TimeSeriesData: Set the time series start time.
setTransformation(List<TimeSeriesTransform>) - Method in class ai.djl.timeseries.dataset.TimeSeriesDataset.TimeSeriesBuilder: Set the transformation for data preprocess.
Split - Class in ai.djl.timeseries.transform.split: this is a class use to split the time series data of TimeSeriesData.
START - Enum constant in enum class ai.djl.timeseries.dataset.FieldName
startDate - Variable in class ai.djl.timeseries.Forecast
startTimeFeature - Variable in class ai.djl.timeseries.dataset.CsvTimeSeriesDataset
startTimeFeatures - Variable in class ai.djl.timeseries.dataset.CsvTimeSeriesDataset.CsvBuilder
StudentT - Class in ai.djl.timeseries.distribution: Student's t-test distribution.
StudentT.Builder - Class in ai.djl.timeseries.distribution: The builder to construct a NegativeBinomial.
StudentTOutput - Class in ai.djl.timeseries.distribution.output: StudentTOutput is a DistributionOutput for the Student's t-test distribution.
StudentTOutput() - Constructor for class ai.djl.timeseries.distribution.output.StudentTOutput: Construct a negative binomial output with two arguments, mu and sigma.

T

TARGET - Enum constant in enum class ai.djl.timeseries.dataset.FieldName
TARGET_DIM_INDICATOR - Enum constant in enum class ai.djl.timeseries.dataset.FieldName
TimeFeature - Class in ai.djl.timeseries.timefeature: this is a class to generate time feature by frequency.
timeFeaturesFromFreqStr(String) - Static method in class ai.djl.timeseries.timefeature.TimeFeature: Returns a list of time features that will be appropriate for the given frequency string.
TimeFeaturizer - Interface in ai.djl.timeseries.dataset: An interface that convert String to LocalDateTime as the start field of TimeSeriesData.
TimeFeaturizers - Class in ai.djl.timeseries.dataset: A utility class provides helper functions to create TimeFeaturizer.
TimeFeaturizers.ConstantTimeFeaturizer - Class in ai.djl.timeseries.dataset: A featurizer always return a constant date.
TimeFeaturizers.PatternTimeFeaturizer - Class in ai.djl.timeseries.dataset: A featurizer implemented for feature of date type.
TimeOffset - Class in ai.djl.timeseries.timefeature: This is a class use to get multiple and granularity from frequency string.
TimeOffset(String, int) - Constructor for class ai.djl.timeseries.timefeature.TimeOffset: Constructs a new TimeOffset instance.
TimeSeriesBuilder() - Constructor for class ai.djl.timeseries.dataset.TimeSeriesDataset.TimeSeriesBuilder
TimeSeriesData - Class in ai.djl.timeseries: TimeSeriesData is a DataEntry for managing time series data in preprocess.
TimeSeriesData(int) - Constructor for class ai.djl.timeseries.TimeSeriesData: Constructs an empty TimeSeriesData with the specified initial capacity.
TimeSeriesData(List<Pair<String, NDArray>>) - Constructor for class ai.djl.timeseries.TimeSeriesData: Constructs a TimeSeriesData containing the elements of the specified list of Pairs.
TimeSeriesData(List<String>, List<NDArray>) - Constructor for class ai.djl.timeseries.TimeSeriesData: Constructs a TimeSeriesData containing the elements of the specified keys and values.
TimeSeriesData(Map<String, NDArray>) - Constructor for class ai.djl.timeseries.TimeSeriesData: Constructs a TimeSeriesData containing the elements of the specified map.
TimeSeriesDataset - Class in ai.djl.timeseries.dataset: An abstract class for creating time series datasets.
TimeSeriesDataset(TimeSeriesDataset.TimeSeriesBuilder<?>) - Constructor for class ai.djl.timeseries.dataset.TimeSeriesDataset
TimeSeriesDataset.TimeSeriesBuilder<T extends TimeSeriesDataset.TimeSeriesBuilder<T>> - Class in ai.djl.timeseries.dataset: Used to build a TimeSeriesDataset.
TimeSeriesTransform - Interface in ai.djl.timeseries.transform: This interface is used for data transformation on the TimeSeriesData.
TimeSeriesTransform.IdentityTransform - Class in ai.djl.timeseries.transform: An identity transformation.
toFreqStr() - Method in class ai.djl.timeseries.timefeature.TimeOffset: Return the formatted frequency string.
toNDList() - Method in class ai.djl.timeseries.TimeSeriesData: Constructs a NDList containing the remaining NDArray for FieldName.
toOffset(String) - Static method in class ai.djl.timeseries.timefeature.TimeOffset: Return TimeOffset object from frequency string.
transform(NDManager, TimeSeriesData, boolean) - Method in class ai.djl.timeseries.transform.convert.AsArray: Transform process on TimeSeriesData.
transform(NDManager, TimeSeriesData, boolean) - Method in class ai.djl.timeseries.transform.convert.VstackFeatures: Transform process on TimeSeriesData.
transform(NDManager, TimeSeriesData, boolean) - Method in class ai.djl.timeseries.transform.feature.AddAgeFeature: Transform process on TimeSeriesData.
transform(NDManager, TimeSeriesData, boolean) - Method in class ai.djl.timeseries.transform.feature.AddObservedValuesIndicator: Transform process on TimeSeriesData.
transform(NDManager, TimeSeriesData, boolean) - Method in class ai.djl.timeseries.transform.feature.AddTimeFeature: Transform process on TimeSeriesData.
transform(NDManager, TimeSeriesData, boolean) - Method in class ai.djl.timeseries.transform.field.RemoveFields: Transform process on TimeSeriesData.
transform(NDManager, TimeSeriesData, boolean) - Method in class ai.djl.timeseries.transform.field.SelectField: Transform process on TimeSeriesData.
transform(NDManager, TimeSeriesData, boolean) - Method in class ai.djl.timeseries.transform.field.SetField: Transform process on TimeSeriesData.
transform(NDManager, TimeSeriesData, boolean) - Method in class ai.djl.timeseries.transform.split.InstanceSplit: Transform process on TimeSeriesData.
transform(NDManager, TimeSeriesData, boolean) - Method in class ai.djl.timeseries.transform.TimeSeriesTransform.IdentityTransform: Transform process on TimeSeriesData.
transform(NDManager, TimeSeriesData, boolean) - Method in interface ai.djl.timeseries.transform.TimeSeriesTransform: Transform process on TimeSeriesData.
transformation - Variable in class ai.djl.timeseries.dataset.TimeSeriesDataset.TimeSeriesBuilder
transformation - Variable in class ai.djl.timeseries.dataset.TimeSeriesDataset
TransformerTranslator - Class in ai.djl.timeseries.translator: The Translator for Transformer time series forecasting tasks.
TransformerTranslator(TransformerTranslator.Builder) - Constructor for class ai.djl.timeseries.translator.TransformerTranslator: Constructs a TransformerTranslator with TransformerTranslator.Builder.
TransformerTranslator.Builder - Class in ai.djl.timeseries.translator: The builder for Transformer translator.
TransformerTranslatorFactory - Class in ai.djl.timeseries.translator: A TranslatorFactory that creates a DeepARTranslator instance.
TransformerTranslatorFactory() - Constructor for class ai.djl.timeseries.translator.TransformerTranslatorFactory

U

unrollLaggedRnn(ParameterStore, NDList, boolean) - Method in class ai.djl.timeseries.model.deepar.DeepARNetwork: Applies the underlying RNN to the provided target data and covariates.
updateAccumulator(String, NDList, NDList) - Method in class ai.djl.timeseries.evaluator.Rmsse
updateAccumulators(String[], NDList, NDList) - Method in class ai.djl.timeseries.evaluator.Rmsse
useFeatDynamicReal - Variable in class ai.djl.timeseries.model.deepar.DeepARNetwork
useFeatStaticCat - Variable in class ai.djl.timeseries.model.deepar.DeepARNetwork
useFeatStaticReal - Variable in class ai.djl.timeseries.model.deepar.DeepARNetwork

V

validate() - Method in class ai.djl.timeseries.block.Scaler.ScalerBuilder: Validates that the required arguments are set.
validate() - Method in class ai.djl.timeseries.dataset.CsvTimeSeriesDataset.CsvBuilder: Validate the builder to ensure it is correct.
validate() - Method in class ai.djl.timeseries.translator.BaseTimeSeriesTranslator.BaseBuilder
valueOf(String) - Static method in enum class ai.djl.timeseries.dataset.FieldName: Returns the enum constant of this class with the specified name.
values() - Static method in enum class ai.djl.timeseries.dataset.FieldName: Returns an array containing the constants of this enum class, in the order they are declared.
vstackFeatures(FieldName, FieldName[], boolean, boolean, TimeSeriesData) - Static method in class ai.djl.timeseries.transform.convert.Convert: Stacks fields together using NDArrays.concat(NDList).
vstackFeatures(FieldName, FieldName[], TimeSeriesData) - Static method in class ai.djl.timeseries.transform.convert.Convert: Stacks fields together using NDArrays.concat(NDList).
VstackFeatures - Class in ai.djl.timeseries.transform.convert: Use the NDArrays.concat(NDList) to vstack data.
VstackFeatures(FieldName, FieldName[]) - Constructor for class ai.djl.timeseries.transform.convert.VstackFeatures: Constructs a VstackFeatures.
VstackFeatures(FieldName, FieldName[], boolean, boolean) - Constructor for class ai.djl.timeseries.transform.convert.VstackFeatures: Constructs a VstackFeatures.

W

weekOfYear(NDManager, List<LocalDateTime>) - Static method in class ai.djl.timeseries.timefeature.TimeFeature: Computes feature by weeks of the year.

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