gluonts.transform.sampler module¶
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class
gluonts.transform.sampler.
BucketInstanceSampler
[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 – The histogram of scale for the time series. Here scale is the mean abs value of the time series.
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lookup
: np.ndarray = None¶
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scale_histogram
: ScaleHistogram = None¶
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class
gluonts.transform.sampler.
ContinuousTimePointSampler
[source]¶ Bases:
pydantic.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.
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min_future
: float = None¶
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min_past
: float = None¶
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-
class
gluonts.transform.sampler.
ContinuousTimePredictionSampler
[source]¶ Bases:
gluonts.transform.sampler.ContinuousTimePointSampler
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allow_empty_interval
: bool = None¶
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-
class
gluonts.transform.sampler.
ContinuousTimeUniformSampler
[source]¶ Bases:
gluonts.transform.sampler.ContinuousTimePointSampler
Implements a simple random sampler to sample points in the continuous interval between
a
andb
.-
num_instances
: int = None¶
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-
class
gluonts.transform.sampler.
ExpectedNumInstanceSampler
[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 – number of training examples generated per time series on average
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n
: int = None¶
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num_instances
: float = None¶
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total_length
: int = None¶
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class
gluonts.transform.sampler.
InstanceSampler
[source]¶ Bases:
pydantic.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 = None¶
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min_future
: int = None¶
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min_past
: int = None¶
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-
class
gluonts.transform.sampler.
PredictionSplitSampler
[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.
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allow_empty_interval
: bool = None¶
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-
gluonts.transform.sampler.
TestSplitSampler
(axis: int = -1, min_past: int = 0) → gluonts.transform.sampler.PredictionSplitSampler[source]¶
-
class
gluonts.transform.sampler.
UniformSplitSampler
[source]¶ Bases:
gluonts.transform.sampler.InstanceSampler
Samples each point with the same fixed probability.
- Parameters
p – Probability of selecting a time point
-
p
: float = None¶