Class InformerModel<T>

Namespace

AiDotNet.TimeSeries

Assembly

AiDotNet.dll

Implements the Informer model for efficient long-sequence time series forecasting.

public class InformerModel<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>

InformerModel<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

The Long-Sequence Forecasting Problem: Traditional Transformer models achieve state-of-the-art results in many sequence modeling tasks, but they struggle with long time series because self-attention has O(L^2) time and memory complexity. For a sequence of 1000 time steps, vanilla attention requires 1 million operations per layer. This makes long-horizon forecasting computationally prohibitive.

The Informer Solution: Informer (Zhou et al., AAAI 2021) introduces three key innovations: 1. ProbSparse Self-Attention (O(L log L) complexity) 2. Self-Attention Distilling for sequence compression 3. Generative-Style Decoder for parallel multi-step forecasting

Constructors

InformerModel(InformerOptions<T>?)

Initializes a new instance of the Informer model with the specified options.

public InformerModel(InformerOptions<T>? options = null)

Parameters

options InformerOptions<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

CreateInstance()

Creates a new instance of the Informer model.

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

Returns

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

DeserializeCore(BinaryReader)

Deserializes the model-specific state from a binary reader.

protected override void DeserializeCore(BinaryReader reader)

Parameters

reader BinaryReader

ForecastHorizon(Vector<T>)

Generates multi-step forecasts using the full Informer architecture.

public Vector<T> ForecastHorizon(Vector<T> input)

Parameters

input Vector<T>

Returns

Vector<T>

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>)

Predicts the next single value in the time series.

public override T PredictSingle(Vector<T> input)

Parameters

input Vector<T>

Returns

T

SerializeCore(BinaryWriter)

Serializes the model-specific state to a binary writer.

protected override void SerializeCore(BinaryWriter writer)

Parameters

writer BinaryWriter

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

Trains the model using proper backpropagation through the Informer architecture.

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

Parameters

x Matrix<T>

y Vector<T>