gluonts.transform.sampler module#
- class gluonts.transform.sampler.BucketInstanceSampler(*, axis: int = - 1, min_past: int = 0, min_future: int = 0, scale_histogram: gluonts.dataset.stat.ScaleHistogram)[source]#
Bases:
gluonts.transform.sampler.InstanceSampler
This sample can be used when working with a set of time series that have a skewed distributions. For instance, if the dataset contains many time series with small values and few with large values.
The probability of sampling from bucket i is the inverse of its number of elements.
- Parameters
scale_histogram (gluonts.dataset.stat.ScaleHistogram) – The histogram of scale for the time series. Here scale is the mean abs value of the time series.
- axis: int#
- min_future: int#
- min_past: int#
- scale_histogram: gluonts.dataset.stat.ScaleHistogram#
- class gluonts.transform.sampler.ContinuousTimePointSampler(*, min_past: float = 0.0, min_future: float = 0.0)[source]#
Bases:
pydantic.v1.main.BaseModel
Abstract class for “continuous time” samplers, which, given a lower bound and upper bound, sample “points” (events) in continuous time from a specified interval.
- min_future: float#
- min_past: float#
- class gluonts.transform.sampler.ContinuousTimePredictionSampler(*, min_past: float = 0.0, min_future: float = 0.0, allow_empty_interval: bool = False)[source]#
Bases:
gluonts.transform.sampler.ContinuousTimePointSampler
- allow_empty_interval: bool#
- min_future: float#
- min_past: float#
- class gluonts.transform.sampler.ContinuousTimeUniformSampler(*, min_past: float = 0.0, min_future: float = 0.0, num_instances: int)[source]#
Bases:
gluonts.transform.sampler.ContinuousTimePointSampler
Implements a simple random sampler to sample points in the continuous interval between
a
andb
.- min_future: float#
- min_past: float#
- num_instances: int#
- class gluonts.transform.sampler.ExpectedNumInstanceSampler(*, axis: int = - 1, min_past: int = 0, min_future: int = 0, num_instances: float, min_instances: int = 0, total_length: int = 0, n: int = 0)[source]#
Bases:
gluonts.transform.sampler.InstanceSampler
Keeps track of the average time series length and adjusts the probability per time point such that on average num_instances training examples are generated per time series.
- Parameters
num_instances (float) – number of time points to sample per time series on average
min_instances (int) – minimum number of time points to sample per time series
- axis: int#
- min_future: int#
- min_instances: int#
- min_past: int#
- n: int#
- num_instances: float#
- total_length: int#
- class gluonts.transform.sampler.InstanceSampler(*, axis: int = - 1, min_past: int = 0, min_future: int = 0)[source]#
Bases:
pydantic.v1.main.BaseModel
An InstanceSampler is called with the time series
ts
, and returns a set of indices at which training instances will be generated.The sampled indices
i
satisfya <= i <= b
, wherea = min_past
andb = ts.shape[axis] - min_future
.- axis: int#
- min_future: int#
- min_past: int#
- class gluonts.transform.sampler.NumInstanceSampler(*, axis: int = - 1, min_past: int = 0, min_future: int = 0, N: int)[source]#
Bases:
gluonts.transform.sampler.InstanceSampler
Samples N time points from each series.
- Parameters
N (int) – number of time points to sample from each time series.
- N: int#
- axis: int#
- min_future: int#
- min_past: int#
- class gluonts.transform.sampler.PredictionSplitSampler(*, axis: int = - 1, min_past: int = 0, min_future: int = 0, allow_empty_interval: bool = False)[source]#
Bases:
gluonts.transform.sampler.InstanceSampler
Sampler used for prediction.
Always selects the last time point for splitting i.e. the forecast point for the time series.
- allow_empty_interval: bool#
- gluonts.transform.sampler.TestSplitSampler(axis: int = - 1, min_past: int = 0) gluonts.transform.sampler.PredictionSplitSampler [source]#
- class gluonts.transform.sampler.UniformSplitSampler(*, axis: int = - 1, min_past: int = 0, min_future: int = 0, p: float)[source]#
Bases:
gluonts.transform.sampler.InstanceSampler
Samples each point with the same fixed probability.
- Parameters
p (float) – Probability of selecting a time point
- axis: int#
- min_future: int#
- min_past: int#
- p: float#
- gluonts.transform.sampler.ValidationSplitSampler(axis: int = - 1, min_past: int = 0, min_future: int = 0) gluonts.transform.sampler.PredictionSplitSampler [source]#