Detector

MomentFMAnomalyDetector

Interface for anomaly detection with the deep learning time series model momentfm.

Quickstart

python

from sktime.detection.momentfm import MomentFMAnomalyDetector

estimator = MomentFMAnomalyDetector(pretrained_model_name_or_path='AutonLab/MOMENT-1-large', freeze_encoder=True, freeze_embedder=True, freeze_head=False, dropout=0.1, head_dropout=0.1, batch_size=32, eval_batch_size=32, epochs=1, max_lr=0.0001, device='auto', pct_start=0.3, max_norm=5.0, train_val_split=0.2, mask_ratio=0.3, transformer_backbone='google/flan-t5-large', criterion=None, anomaly_criterion='mse', anomaly_percentile=95.0, config=None, return_model_to_cpu=False)

Examples

>>> from sktime.detection.momentfm import MomentFMAnomalyDetector
>>> import pandas as pd
>>> import numpy as np
>>> # Create sample time series data
>>> X = pd. DataFrame (np. random. randn (100, 1))
>>> detector = MomentFMAnomalyDetector ()
>>> detector. fit (X)
>>> # Use.predict to get indices of detected anomalies
>>> y_pred = detector. predict (X)
>>> # Use.predict_scores to get anomaly scores
>>> y_pred = detector. predict_scores (X)
>>> # Use.transform_scores to get scores in a DataFrame format
>>> y_pred = detector. transform_scores (X)

References

Paper: https://arxiv.org/abs/2402.03885 Github: https://github.com/moment-timeseries-foundation-model/moment/tree/main