Class DeepANT<T>

Namespace

AiDotNet.TimeSeries.AnomalyDetection

Assembly

AiDotNet.dll

Implements DeepANT (Deep Learning for Anomaly Detection in Time Series).

public class DeepANT<T> : TimeSeriesModelBase<T>, ITimeSeriesModel<T>, IFullModel<T, Matrix<T>, Vector<T>>, IModel<Matrix<T>, Vector<T>, ModelMetadata<T>>, IModelSerializer, ICheckpointableModel, IParameterizable<T, Matrix<T>, Vector<T>>, IFeatureAware, IFeatureImportance<T>, ICloneable<IFullModel<T, Matrix<T>, Vector<T>>>, IGradientComputable<T, Matrix<T>, Vector<T>>, IJitCompilable<T>

Type Parameters

T

The numeric type used for calculations (e.g., float, double).

Inheritance

object

TimeSeriesModelBase<T>

DeepANT<T>

Implements

ITimeSeriesModel<T>

IFullModel<T, Matrix<T>, Vector<T>>

IModel<Matrix<T>, Vector<T>, ModelMetadata<T>>

IModelSerializer

ICheckpointableModel

IParameterizable<T, Matrix<T>, Vector<T>>

IFeatureAware

IFeatureImportance<T>

ICloneable<IFullModel<T, Matrix<T>, Vector<T>>>

IGradientComputable<T, Matrix<T>, Vector<T>>

IJitCompilable<T>

Inherited Members

TimeSeriesModelBase<T>.Options

TimeSeriesModelBase<T>.NumOps

TimeSeriesModelBase<T>.Engine

TimeSeriesModelBase<T>.ModelParameters

TimeSeriesModelBase<T>.IsTrained

TimeSeriesModelBase<T>.LastEvaluationMetrics

TimeSeriesModelBase<T>.ValidateOptions(TimeSeriesRegressionOptions<T>)

TimeSeriesModelBase<T>.Train(Matrix<T>, Vector<T>)

TimeSeriesModelBase<T>.ValidateTrainingInputs(Matrix<T>, Vector<T>)

TimeSeriesModelBase<T>.Predict(Matrix<T>)

TimeSeriesModelBase<T>.ValidatePredictionInput(Matrix<T>)

TimeSeriesModelBase<T>.EvaluateModel(Matrix<T>, Vector<T>)

TimeSeriesModelBase<T>.CalculateErrorMetrics(Vector<T>, Vector<T>)

TimeSeriesModelBase<T>.Serialize()

TimeSeriesModelBase<T>.Deserialize(byte[])

TimeSeriesModelBase<T>.GetParameters()

TimeSeriesModelBase<T>.WithParameters(Vector<T>)

TimeSeriesModelBase<T>.ApplyParameters(Vector<T>)

TimeSeriesModelBase<T>.GetActiveFeatureIndices()

TimeSeriesModelBase<T>.IsFeatureUsed(int)

TimeSeriesModelBase<T>.GetFeatureImportance(int)

TimeSeriesModelBase<T>.SetParameters(Vector<T>)

TimeSeriesModelBase<T>.SetActiveFeatureIndices(IEnumerable<int>)

TimeSeriesModelBase<T>.GetFeatureImportance()

TimeSeriesModelBase<T>.DeepCopy()

TimeSeriesModelBase<T>.Clone()

TimeSeriesModelBase<T>.Reset()

TimeSeriesModelBase<T>.Clip(T, T, T)

TimeSeriesModelBase<T>.Forecast(Vector<T>, int)

TimeSeriesModelBase<T>.PrepareForecastFeatures(List<T>, int)

TimeSeriesModelBase<T>.SaveModel(string)

TimeSeriesModelBase<T>.LoadModel(string)

TimeSeriesModelBase<T>.DefaultLossFunction

TimeSeriesModelBase<T>.ComputeGradients(Matrix<T>, Vector<T>, ILossFunction<T>)

TimeSeriesModelBase<T>.ApplyGradients(Vector<T>, T)

TimeSeriesModelBase<T>.SaveState(Stream)

TimeSeriesModelBase<T>.LoadState(Stream)

TimeSeriesModelBase<T>.SupportsJitCompilation

TimeSeriesModelBase<T>.ExportComputationGraph(List<ComputationNode<T>>)

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Extension Methods

DistributedExtensions.AsDistributedForHighBandwidth<T, TInput, TOutput>(IFullModel<T, TInput, TOutput>, ICommunicationBackend<T>)

DistributedExtensions.AsDistributedForLowBandwidth<T, TInput, TOutput>(IFullModel<T, TInput, TOutput>, ICommunicationBackend<T>)

DistributedExtensions.AsDistributed<T, TInput, TOutput>(IFullModel<T, TInput, TOutput>, ICommunicationBackend<T>)

DistributedExtensions.AsDistributed<T, TInput, TOutput>(IFullModel<T, TInput, TOutput>, IShardingConfiguration<T>)

Remarks

DeepANT is a deep learning-based approach for unsupervised anomaly detection in time series. It uses a convolutional neural network to learn normal patterns and identifies anomalies as data points that deviate significantly from the learned patterns.

Key features: - Time series prediction using CNN - Anomaly detection based on prediction error - Unsupervised learning (no labeled anomalies needed) - Effective for both point anomalies and contextual anomalies

For Beginners: DeepANT learns what "normal" looks like in your time series, then flags anything unusual as an anomaly. It works by: 1. Learning to predict the next value based on past values 2. Comparing actual values to predictions 3. Marking large prediction errors as anomalies

Think of it like a system that learns your daily routine - if you suddenly do something very different, it notices and flags it as unusual.

Constructors

DeepANT(DeepANTOptions<T>?)

Initializes a new instance of the DeepANT class.

public DeepANT(DeepANTOptions<T>? options = null)

Parameters

options DeepANTOptions<T>

Properties

ParameterCount

Gets the number of parameters in the model.

public override int ParameterCount { get; }

Property Value

int

Remarks

This property returns the total count of trainable parameters in the model. It's useful for understanding model complexity and memory requirements.

Methods

ComputeAnomalyScores(Vector<T>)

Computes anomaly scores for a time series.

public Vector<T> ComputeAnomalyScores(Vector<T> data)

Parameters

data Vector<T>

Returns

Vector<T>

CreateInstance()

Creates a new instance of the derived model class.

protected override IFullModel<T, Matrix<T>, Vector<T>> CreateInstance()

Returns

IFullModel<T, Matrix<T>, Vector<T>>

A new instance of the same model type.

Remarks

This abstract factory method must be implemented by derived classes to create a new instance of their specific type. It's used by Clone and DeepCopy to ensure that the correct derived type is instantiated.

For Beginners: This method creates a new, empty instance of the specific model type. It's used during cloning and deep copying to ensure that the copy is of the same specific type as the original.

For example, if the original model is an ARIMA model, this method would create a new ARIMA model. If it's a TBATS model, it would create a new TBATS model.

DeserializeCore(BinaryReader)

Deserializes model-specific data from the binary reader.

protected override void DeserializeCore(BinaryReader reader)

Parameters

reader BinaryReader

The binary reader to read from.

Remarks

This abstract method must be implemented by each specific model type to load its unique parameters and state.

For Beginners: This method is responsible for loading the specific details that make each type of time series model unique. It reads exactly what was written by SerializeCore, in the same order, reconstructing the specialized parts of the model.

It's the counterpart to SerializeCore and should read data in exactly the same order and format that it was written.

This separation allows the base class to handle common deserialization tasks while each model type handles its specialized data.

DetectAnomalies(Vector<T>)

Detects anomalies in a time series.

public bool[] DetectAnomalies(Vector<T> data)

Parameters

data Vector<T>

Time series data.

Returns

bool[]

Boolean array where true indicates an anomaly.

GetModelMetadata()

Gets metadata about the time series model.

public override ModelMetadata<T> GetModelMetadata()

Returns

ModelMetadata<T>

A ModelMetaData object containing information about the model.

Remarks

This method provides comprehensive metadata about the model, including its type, configuration options, training status, evaluation metrics, and information about which features/lags are most important.

For Beginners: This method provides important information about the model that can help you understand its characteristics and performance.

The metadata includes:

• The type of model (e.g., ARIMA, TBATS, Neural Network)
• Configuration details (e.g., lag order, seasonality period)
• Whether the model has been trained
• Performance metrics from the last evaluation
• Information about which features (time periods) are most influential

This information is useful for documentation, model comparison, and debugging. It's like a complete summary of everything important about the model.

PredictSingle(Vector<T>)

Generates a prediction for a single input vector.

public override T PredictSingle(Vector<T> input)

Parameters

input Vector<T>

The input feature vector.

Returns

T

The predicted value.

Remarks

This abstract method must be implemented by derived classes to generate a prediction for a single input vector using the model-specific algorithm.

For Beginners: This method takes a single row of input data (representing one time point) and calculates what the model predicts will happen at that point. Each type of time series model will have its own way of calculating this prediction based on the patterns it learned during training.

SerializeCore(BinaryWriter)

Serializes model-specific data to the binary writer.

protected override void SerializeCore(BinaryWriter writer)

Parameters

writer BinaryWriter

The binary writer to write to.

Remarks

This abstract method must be implemented by each specific model type to save its unique parameters and state.

For Beginners: This method is responsible for saving the specific details that make each type of time series model unique. Different models have different internal structures and parameters that need to be saved separately from the common elements.

For example:

• An ARIMA model would save its AR, I, and MA coefficients
• A TBATS model would save its level, trend, and seasonal components
• A neural network model would save its weights and biases

This separation allows the base class to handle common serialization tasks while each model type handles its specialized data.

TrainCore(Matrix<T>, Vector<T>)

Performs the model-specific training algorithm.

protected override void TrainCore(Matrix<T> x, Vector<T> y)

Parameters

x Matrix<T>

The input features matrix.

y Vector<T>

The target values vector.

Remarks

This abstract method must be implemented by derived classes to perform the actual model training.

For Beginners: This is where the specific math and algorithms for each type of time series model are implemented. Different models (like ARIMA, Exponential Smoothing, etc.) will have their own unique ways of finding patterns in the data.