---

name: imputation-anomaly-classification description: "Configure and debug Time-Series-Library imputation, reconstruction anomaly detection, and UEA time-series classification workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

TSLib Imputation, Anomaly Detection, and Classification

Use this sub-skill when the task uses --task_name imputation, anomaly_detection, or classification, or when the user needs help with mask_rate, anomaly_ratio, PSM/MSL/SMAP/SMD/SWAT layouts, UEA .ts files, classification accuracy, or reconstruction-style anomaly metrics.

Route Here

• Build or debug masked imputation commands over ETT/custom CSV datasets.
• Configure reconstruction anomaly detection with PSM, MSL, SMAP, SMD, or SWAT folders.
• Configure UEA time-series classification with <DatasetName>_TRAIN.ts and <DatasetName>_TEST.ts.
• Interpret imputation MAE/MSE, anomaly threshold/accuracy/precision/recall/F-score, and classification accuracy outputs.
• Decide when augmentation flags are relevant to classification or detection experiments.

Reroute

• Generic local file validation, run.py parser flags, output folders, or GPU/CPU preflight: use ../data-and-cli/SKILL.md.
• Forecasting, TimeXer, M4, or zero-shot forecast tasks: use ../forecasting/SKILL.md.
• Optional model dependency installation, custom models, and augmentation implementation details: use ../foundation-models-and-customization/SKILL.md.

Quick Patterns

Imputation

python -u run.py \
  --task_name imputation --is_training 1 \
  --root_path ./dataset/ETT-small/ --data_path ETTh1.csv \
  --model_id ETTh1_mask_0.125 --mask_rate 0.125 \
  --model TimesNet --data ETTh1 --features M \
  --seq_len 96 --label_len 0 --pred_len 0 \
  --enc_in 7 --dec_in 7 --c_out 7 \
  --train_epochs 1 --num_workers 0 --no_use_gpu

Anomaly detection

python -u run.py \
  --task_name anomaly_detection --is_training 1 \
  --root_path ./dataset/PSM --model_id PSM \
  --model TimesNet --data PSM --features M \
  --seq_len 100 --pred_len 0 --enc_in 25 --c_out 25 \
  --anomaly_ratio 1.0 --train_epochs 1 --num_workers 0

UEA classification

python -u run.py \
  --task_name classification --is_training 1 \
  --root_path ./dataset/Heartbeat/ --model_id Heartbeat \
  --model TimesNet --data UEA \
  --e_layers 2 --d_model 64 --d_ff 128 --top_k 3 \
  --batch_size 16 --train_epochs 1 --num_workers 0 --no_use_gpu

Important Task Differences

• Imputation randomly masks entries in batch_x according to --mask_rate and computes MAE/MSE only on masked positions.
• Anomaly detection trains a reconstruction model, computes reconstruction energy on train and test windows, thresholds by 100 - anomaly_ratio percentile, then applies event-level adjustment.
• Classification builds model dimensions from the TRAIN and TEST .ts files; seq_len, enc_in, and num_class are inferred before model construction.

References

• references/task-workflows.md gives complete task recipes and data requirements.
• references/metrics-and-results.md explains task metrics and output files.
• references/troubleshooting.md covers task-specific data, metric, and shape failures.
• ../data-and-cli/scripts/validate_tslib_data.py validates local anomaly/UEA/CSV files without running training.

Avoid

• Do not use anomaly or classification benchmark scripts as smoke tests without rewriting GPU, epoch, data path, and worker settings.
• Do not compare tiny synthetic anomaly/classification metrics to paper results.
• Do not force forecasting channel-count assumptions onto UEA classification; classification derives data dimensions from .ts files.