Class InformerOptions<T>

Namespace

AiDotNet.Models.Options

Assembly

AiDotNet.dll

Configuration options for the Informer model (Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting).

public class InformerOptions<T> : TimeSeriesRegressionOptions<T>

Type Parameters

T

The numeric type used for calculations (typically double or float).

Inheritance

object

ModelOptions

RegressionOptions<T>

TimeSeriesRegressionOptions<T>

InformerOptions<T>

Inherited Members

TimeSeriesRegressionOptions<T>.LagOrder

TimeSeriesRegressionOptions<T>.IncludeTrend

TimeSeriesRegressionOptions<T>.SeasonalPeriod

TimeSeriesRegressionOptions<T>.AutocorrelationCorrection

TimeSeriesRegressionOptions<T>.ModelType

TimeSeriesRegressionOptions<T>.LossFunction

RegressionOptions<T>.DecompositionMethod

RegressionOptions<T>.UseIntercept

ModelOptions.Seed

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Remarks

Informer addresses the computational complexity challenges of vanilla Transformers for long-sequence forecasting. Key innovations include: - ProbSparse self-attention mechanism (O(L log L) complexity instead of O(L²)) - Self-attention distilling for efficient stacking - Generative style decoder for one-forward prediction

For Beginners: Informer is an efficient version of the Transformer architecture designed specifically for long time series. Traditional transformers become very slow with long sequences, but Informer uses smart tricks to be much faster while maintaining accuracy. It's particularly good for forecasting that requires looking far back in history (like predicting next month based on the past year).

Constructors

InformerOptions()

public InformerOptions()

InformerOptions(InformerOptions<T>)

public InformerOptions(InformerOptions<T> other)

Parameters

other InformerOptions<T>

Properties

BatchSize

Gets or sets the batch size.

public int BatchSize { get; set; }

Property Value

int

DistillingFactor

Gets or sets the distilling factor for self-attention distilling.

public int DistillingFactor { get; set; }

Property Value

int

Remarks

For Beginners: This controls how much the model compresses information between layers. A factor of 2 means each layer has half the sequence length of the previous one.

DropoutRate

Gets or sets the dropout rate.

public double DropoutRate { get; set; }

Property Value

double

EmbeddingDim

Gets or sets the embedding dimension.

public int EmbeddingDim { get; set; }

Property Value

int

Epochs

Gets or sets the number of training epochs.

public int Epochs { get; set; }

Property Value

int

ForecastHorizon

Gets or sets the forecast horizon (decoder output length).

public int ForecastHorizon { get; set; }

Property Value

int

LearningRate

Gets or sets the learning rate.

public double LearningRate { get; set; }

Property Value

double

LookbackWindow

Gets or sets the lookback window (encoder input length).

public int LookbackWindow { get; set; }

Property Value

int

NumAttentionHeads

Gets or sets the number of attention heads.

public int NumAttentionHeads { get; set; }

Property Value

int

NumDecoderLayers

Gets or sets the number of decoder layers.

public int NumDecoderLayers { get; set; }

Property Value

int

NumEncoderLayers

Gets or sets the number of encoder layers.

public int NumEncoderLayers { get; set; }

Property Value

int