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- Global:
- model: TimesNet_ad
- mode: check_dataset # check_dataset/train/evaluate/predict
- dataset_dir: "/paddle/dataset/paddlex/ts_ad/ts_anomaly_examples/"
- device: gpu:0
- output: "output"
- CheckDataset:
- convert:
- enable: False
- src_dataset_type: nul
- split:
- enable: False
- train_percent: null
- val_percent: null
- Train:
- time_col: timestamp
- feature_cols: feature_0,feature_1
- label_col: label
- freq: 1
- input_len: 96
- epochs_iters: 20
- batch_size: 16
- learning_rate: 0.0005
- Evaluate:
- weight_path: "output/best_accuracy.pdparams.tar"
- Predict:
- model_dir: "output/best_model/model.pdparams"
- input_path: "/paddle/dataset/paddlex/ts_ad/ts_anomaly_examples/test.csv"
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