Review for Deep Learning with Time Series Data

TS2Vec(Towards Universal Representation of Time Series) 論文筆記

1. Datasets for training

   • The data source uses Chung-Hao Lee's pre-screened anomalous events(ADDCAT)
     • ts2vec/datasets/DORA/df_event_all_v1.feather
   • data preparation
     • transform_data_CHPrep.ipynb
     • output at : ts2vec/datasets/DORA/
       • data : dora.npz (n, length, dim)
       • meta : dora_meta.csv
   • Pre-processing process for Finetune
     • ts2vec/datasets/prepare_DORA_KA51AG8742.ipynb
       • X_train.npz, X_test.npz, y_train.npz, y_test.npz

2. Training (in directory ts2vec/)
   We use the self-supervised learning model ts2vec for training and feature extraction.

   • Pretrain commands

     python3 train.py dora  training  --loader DORA --batch-size  16 --repr-dims 320 --epochs 500

   • Get the embedding and dimension reduction

     python3 get_embedding.py  dora  --checkpoint  dora_b16_320d/model_500.pkl  --repr-dims 320  --batch-size 1024  --umap

3. Analysis and auto-labeling

• Representation Analysis
  • ts2vec/explore_feature.ipynb
  • Validate appropriate hyperparameter settings
  • Explore and analyze the representation
• Finetune and get more label
  • ts2vec/verify_label.ipynb
  • Fine-tuning with vehicle_model : "KA51AG8742" datasets (with labels)
    • train the classification model to detect anomalies
  • Use fine-tuning model to infer all data and obtain the obtain labels for abnormal events (automatic labeling)