Comments (1)
Also, a TimeSeriesLoader
class to load the data in a format ready to be used by the TimeSeriesAnomalyDetector
. The class constructor will have the following arguments:
dataset_path
: The path to the dataset. It will be an CSV file without index nor headers.predictions
: Number of steps ahead to predict.samples
: Number of steps composing each training sample.
And will have a single method load(self, target=True)
that will load and format the data and return, for a CSV of size (n_rows, n_features)
the following items:
X
:numpy.ndarray
a 3-d array of shape(training_samples, samples, n_features )
y
:np.ndarray
a 2-d array of shape(training_samples, predictions)
from orion.
Related Issues (20)
- Using Custom Multivariate Time-Series Data with Orion HOT 4
- LNN Pipeline
- ValueError: Unknown primitive: mlstars.custom.timeseries_preprocessing.time_segments_aggregate HOT 3
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- Problem with pip install HOT 2
- Point Anomaly Setting for CSV Analysis HOT 2
- Understand the significance of different errors and their ranges, and few hyperparameters HOT 5
- Different F1 score for same signal for different time_segments_aggregate interval HOT 8
- MacOS compatibility: METAL HOT 5
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- Migrate `setup.py` and `setup.cfg` to `pyproject.toml`
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- Request for UCR Dataset Download in CSV Format HOT 5
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