gluonts.dataset.artificial package#
- class gluonts.dataset.artificial.ArtificialDataset(freq)[source]#
Bases:
object
Parent class of a dataset that can be generated from code.
- generate() TrainDatasets [source]#
- abstract property test: List[Dict[str, Any]]#
- abstract property train: List[Dict[str, Any]]#
- class gluonts.dataset.artificial.ComplexSeasonalTimeSeries(num_series: int = 100, prediction_length: int = 20, freq_str: str = 'D', length_low: int = 30, length_high: int = 200, min_val: float = -10000, max_val: float = 10000, is_integer: bool = False, proportion_missing_values: float = 0, is_noise: bool = True, is_scale: bool = True, percentage_unique_timestamps: float = 0.07, is_out_of_bounds_date: bool = False, seasonality: Optional[int] = None, clip_values: bool = False)[source]#
Bases:
ArtificialDataset
Generate sinus time series that ramp up and reach a certain amplitude, and level and have additional spikes on each sunday.
TODO: This could be converted to a RecipeDataset to avoid code duplication.
- property test: List[Dict[str, Any]]#
- property train: List[Dict[str, Any]]#
- class gluonts.dataset.artificial.ConstantDataset(num_timeseries: int = 10, num_steps: int = 30, freq: str = '1h', start: str = '2000-01-01 00:00:00', is_nan: bool = False, is_random_constant: bool = False, is_different_scales: bool = False, is_piecewise: bool = False, is_noise: bool = False, is_long: bool = False, is_short: bool = False, is_trend: bool = False, num_missing_middle: int = 0, is_promotions: bool = False, holidays: Optional[List[Timestamp]] = None)[source]#
Bases:
ArtificialDataset
- compute_data_from_recipe(num_steps: int, constant: Optional[float] = None, one_to_zero: float = 0.1, zero_to_one: float = 0.1, scale_features: float = 200) TrainDatasets [source]#
- determine_constant(index: int, constant: Optional[float] = None, seed: int = 1) Optional[float] [source]#
- get_num_steps(index: int, num_steps_max: int = 10000, long_freq: int = 4, num_steps_min: int = 2, short_freq: int = 4) int [source]#
- property test: List[Dict[str, Any]]#
- property train: List[Dict[str, Any]]#
- class gluonts.dataset.artificial.RecipeDataset(recipe: ~typing.Union[~typing.Callable, ~typing.Dict[str, ~typing.Callable], ~typing.List[~typing.Tuple[str, ~typing.Callable]]], metadata: ~gluonts.dataset.common.MetaData, max_train_length: int, prediction_length: int, num_timeseries: int, trim_length_fun=<function RecipeDataset.<lambda>>, data_start=Timestamp('2014-01-01 00:00:00'))[source]#
Bases:
ArtificialDataset
Synthetic data set generated by providing a recipe.
A recipe is either a (non-deterministic) function
f(length: int, global_state: dict) -> dict
or list of (field, function) tuples of the form
(field: str, f(data: dict, length: int, global_state: dict) -> dict)
which is processed sequentially, with data initially set to {}, and each entry updating data[field] to the output of the function call.
- generate() TrainDatasets [source]#
- property test#
- property train#
Submodules#
- gluonts.dataset.artificial.ar_p module
- gluonts.dataset.artificial.recipe module
ARp
Add
BinaryHolidays
BinaryMarkovChain
Choose
Concatenate
Constant
ConstantVec
Convolve
Debug
Dilated
Eval
EvalRecipe
ForEachCat
Lag
Length
Lifted
LinearTrend
Mul
NanWhere
NormalizeMax
NumpyFunc
OneMinus
OnesLike
RandomBinary
RandomCat
RandomChangepoints
RandomGaussian
RandomInteger
RandomSymmetricDirichlet
RandomUniform
Ref
Repeated
SmoothSeasonality
Stack
StackPrefix
evaluate()
expand_shape()
generate()
lift()
make_func()
normalized_ar1()
resolve()
take_as_list()