timeseries

Documentation

Overview

Package timeseries implements time-series model builders.

Package timeseries implements time-series model builders.

Package timeseries implements GGUF metadata loading for time-series models.

Index

• func BuildPatchTST[T tensor.Numeric](cfg PatchTSTConfig, engine compute.Engine[T]) (*graph.Graph[T], error)
• func BuildTFT[T tensor.Numeric](cfg TFTConfig, engine compute.Engine[T]) (*graph.Graph[T], error)
• type PatchTSTBuilder
• type PatchTSTConfig
• type RegimeConfig
• type RegimeDetector
  • func BuildRegimeDetector[T tensor.Numeric](cfg RegimeConfig, engine compute.Engine[T], ops numeric.Arithmetic[T]) (*RegimeDetector[T], error)
  • func (rd *RegimeDetector[T]) Attributes() map[string]interface{}
  • func (rd *RegimeDetector[T]) Backward(_ context.Context, _ types.BackwardMode, _ *tensor.TensorNumeric[T], ...) ([]*tensor.TensorNumeric[T], error)
  • func (rd *RegimeDetector[T]) Forward(ctx context.Context, inputs ...*tensor.TensorNumeric[T]) (*tensor.TensorNumeric[T], error)
  • func (rd *RegimeDetector[T]) OpType() string
  • func (rd *RegimeDetector[T]) OutputShape() []int
  • func (rd *RegimeDetector[T]) Parameters() []*graph.Parameter[T]
• type TFTConfig
• type TimeSeriesSignalConfig
  • func LoadPatchTSTFromGGUF[T tensor.Numeric](path string, engine compute.Engine[T], build PatchTSTBuilder[T]) (*graph.Graph[T], TimeSeriesSignalConfig, error)
  • func LoadTimeSeriesSignalConfig(meta map[string]interface{}) (TimeSeriesSignalConfig, error)

Functions

func BuildPatchTST

func BuildPatchTST[T tensor.Numeric](cfg PatchTSTConfig, engine compute.Engine[T]) (*graph.Graph[T], error)

BuildPatchTST constructs a PatchTST computation graph. Input: [batch, seq_len, num_vars] time series. Output: [batch, horizon, num_vars] predictions.

func BuildTFT

func BuildTFT[T tensor.Numeric](cfg TFTConfig, engine compute.Engine[T]) (*graph.Graph[T], error)

BuildTFT constructs a TFT computation graph. Input 0: [batch, seq_len, num_temporal_features] temporal features. Input 1: [batch, num_static_features] static covariates. Output: [batch, H, len(quantiles)] quantile predictions.

Types

type PatchTSTBuilder

type PatchTSTBuilder[T tensor.Numeric] func(cfg TimeSeriesSignalConfig, engine compute.Engine[T]) (*graph.Graph[T], error)

PatchTSTBuilder constructs a PatchTST computation graph from a signal config and compute engine. This allows callers to supply their own graph builder without creating a circular import.

type PatchTSTConfig

type PatchTSTConfig struct {
	PatchLen  int // patch size (e.g., 16)
	Stride    int // patch stride (e.g., 8)
	NumLayers int // transformer encoder depth (e.g., 3)
	NumHeads  int // attention heads (e.g., 8)
	DModel    int // model dim (e.g., 128)
	Horizon   int // prediction horizon H
	NumVars   int // number of input features D
}

PatchTSTConfig holds configuration for the PatchTST model.

type RegimeConfig

type RegimeConfig struct {
	InputDim   int // number of input features per timestep
	HiddenDim  int // GRU hidden size (e.g., 128)
	NumLayers  int // GRU depth (e.g., 2)
	SeqLen     int // input sequence length (e.g., 60 days)
	NumClasses int // number of regime classes (default: 4)
}

RegimeConfig holds configuration for the regime detection model.

type RegimeDetector

type RegimeDetector[T tensor.Numeric] struct {
	// contains filtered or unexported fields
}

RegimeDetector is a GRU-based regime classification model. It processes sequential input through stacked GRU layers and classifies the final hidden state into one of NumClasses regimes (bull/bear/sideways/volatile).

func BuildRegimeDetector

func BuildRegimeDetector[T tensor.Numeric](
	cfg RegimeConfig,
	engine compute.Engine[T],
	ops numeric.Arithmetic[T],
) (*RegimeDetector[T], error)

BuildRegimeDetector constructs a regime detection model. The model consists of stacked GRU layers followed by a linear classifier with softmax output producing 4-class probabilities (bull/bear/sideways/volatile).

func (*RegimeDetector[T]) Attributes

func (rd *RegimeDetector[T]) Attributes() map[string]interface{}

Attributes returns the model configuration.

func (*RegimeDetector[T]) Backward

func (rd *RegimeDetector[T]) Backward(_ context.Context, _ types.BackwardMode, _ *tensor.TensorNumeric[T], _ ...*tensor.TensorNumeric[T]) ([]*tensor.TensorNumeric[T], error)

Backward is not implemented for inference-only use.

func (*RegimeDetector[T]) Forward

func (rd *RegimeDetector[T]) Forward(ctx context.Context, inputs ...*tensor.TensorNumeric[T]) (*tensor.TensorNumeric[T], error)

Forward runs the regime detection model. Input shape: [batch, seqLen, inputDim] Output shape: [batch, numClasses] with softmax probabilities.

func (*RegimeDetector[T]) OpType

func (rd *RegimeDetector[T]) OpType() string

OpType returns the operation type.

func (*RegimeDetector[T]) OutputShape

func (rd *RegimeDetector[T]) OutputShape() []int

OutputShape returns [batch, numClasses].

func (*RegimeDetector[T]) Parameters

func (rd *RegimeDetector[T]) Parameters() []*graph.Parameter[T]

Parameters returns all trainable parameters.

type TFTConfig

type TFTConfig struct {
	NumStaticFeatures   int       // static covariates (e.g. asset metadata)
	NumTemporalFeatures int       // time-varying inputs
	HiddenDim           int       // d_model
	NumHeads            int       // attention heads
	NumLSTMLayers       int       // LSTM encoder layers
	HorizonLen          int       // forecast steps H
	Quantiles           []float32 // e.g. [0.1, 0.5, 0.9]
}

TFTConfig holds configuration for the Temporal Fusion Transformer.

type TimeSeriesSignalConfig

type TimeSeriesSignalConfig struct {
	PatchLen      int // ts.signal.patch_len
	Stride        int // ts.signal.stride (defaults to PatchLen if absent)
	InputFeatures int // ts.signal.input_features
	HiddenDim     int // ts.signal.hidden_dim (default 128)
	NumHeads      int // ts.signal.num_heads (default 8)
	NumLayers     int // ts.signal.num_layers (default 6)
	HorizonLen    int // ts.signal.horizon_len (default 1)
}

TimeSeriesSignalConfig holds signal processing parameters loaded from GGUF metadata for time-series models.

func LoadPatchTSTFromGGUF

func LoadPatchTSTFromGGUF[T tensor.Numeric](path string, engine compute.Engine[T], build PatchTSTBuilder[T]) (*graph.Graph[T], TimeSeriesSignalConfig, error)

LoadPatchTSTFromGGUF loads a PatchTST model from a GGUF file. It parses the file header, extracts ts.signal.* metadata, and delegates graph construction to the provided builder function.

func LoadTimeSeriesSignalConfig

func LoadTimeSeriesSignalConfig(meta map[string]interface{}) (TimeSeriesSignalConfig, error)

LoadTimeSeriesSignalConfig extracts TimeSeriesSignalConfig from a GGUF metadata map. Required keys are ts.signal.patch_len and ts.signal.input_features; all others have defaults.