Table Of Contents
Table Of Contents

gluonts.dataset.artificial.recipe module

class gluonts.dataset.artificial.recipe.Add(inputs)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.Binary(dates: List[pandas._libs.tslibs.timestamps.Timestamp], holidays: List[Any])[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.BinaryMarkovChain(one_to_zero: float, zero_to_one: float)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.Constant(constant)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.ConstantVec(constant)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.Debug(print_global=False)[source]

Bases: object

class gluonts.dataset.artificial.recipe.Expr(expr: str)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.ForEachCat(fun, cat_field='cat', cat_idx=0)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.Lag(field_name: str, lag: int = 0)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.Lifted[source]

Bases: object

class gluonts.dataset.artificial.recipe.LiftedAdd(left, right)[source]

Bases: gluonts.dataset.artificial.recipe.LiftedBinaryOp

class gluonts.dataset.artificial.recipe.LiftedBinaryOp(left, right, op)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.LiftedMul(left, right)[source]

Bases: gluonts.dataset.artificial.recipe.LiftedBinaryOp

class gluonts.dataset.artificial.recipe.LiftedTruediv(left, right)[source]

Bases: gluonts.dataset.artificial.recipe.LiftedBinaryOp

class gluonts.dataset.artificial.recipe.LinearTrend(slope_fun: Callable = gluonts.dataset.artificial.recipe.Constant(constant=1.0))[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.Mul(inputs)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.NanWhere(source_name, nan_indicator_name)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.NanWhereNot(source_name, nan_indicator_name)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.RandomBinary(prob: float = 0.1)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.RandomCat(cardinalities: List[int], prob_fun: gluonts.dataset.artificial.recipe.Lifted = gluonts.dataset.artificial.recipe.RandomSymmetricDirichlet(alpha=1.0, length=None))[source]

Bases: object

class gluonts.dataset.artificial.recipe.RandomGaussian(stddev: float = 1.0, length: Optional[int] = None)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.RandomSymmetricDirichlet(alpha: float = 1.0, length: Optional[int] = None)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.SmoothSeasonality(period_fun: Callable, phase_fun: Callable)[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

class gluonts.dataset.artificial.recipe.Stack(inputs: List[str])[source]

Bases: gluonts.dataset.artificial.recipe.Lifted

gluonts.dataset.artificial.recipe.evaluate_recipe(funcs: List[Tuple[str, Callable]], length: int, global_state: dict = None) → dict[source]
gluonts.dataset.artificial.recipe.generate(length: int, recipe: Union[Callable, List[Tuple[str, Callable]]], start: pandas._libs.tslibs.timestamps.Timestamp, global_state: Optional[dict] = None, seed: int = 0) → Iterator[Dict[str, Any]][source]
gluonts.dataset.artificial.recipe.make_func(length: int, funcs: List[Tuple[str, Callable]], global_state=None) → Callable[[int, dict], Dict[str, Any]][source]
gluonts.dataset.artificial.recipe.take_as_list(iterator, num)[source]