---

name: universal-tasks description: "Use and debug TimeMixer imputation, anomaly detection, and classification workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

TimeMixer Universal Tasks

Use this sub-skill for TimeMixer's non-forecast branches: imputation, anomaly detection, and classification.

Route here for

• Building or checking run.py commands for imputation, anomaly detection, or classification, using the explicit helper path /path/to/timemixer-skill/sub-skills/universal-tasks/scripts/build_universal_task_command.py.
• Understanding task-specific args such as mask_rate, anomaly_ratio, UEA padding, and channel_independence.
• Locating result files, saved metrics, and checkpoint paths for the non-forecast branches.
• Debugging dataset-name, mask, threshold, or classification shape issues that are specific to these tasks. The helper prints commands only; run generated run.py commands from /path/to/TimeMixer-checkout, not from the skill directory.

Safe defaults

1. Use the command builder to assemble a command before any training run. Its source-compatible downsampling defaults are --down-sampling-layers 1 --down-sampling-window 2; overrides must keep layers >= 1, window >= 2, and the smallest scale non-empty.
2. M4 imputation is rejected by the builder because this source M4 loader cannot safely use the universal branch's pred_len=0; use the forecasting M4 presets instead.
3. Keep c_out aligned with the input channel count for imputation and anomaly detection.
4. For multi-feature UEA classification, prefer channel_independence=0.
5. Treat anomaly_ratio as a percent value in the source threshold calculation.
6. Do not launch benchmark-scale training or dataset downloads from this sub-skill.

Route elsewhere

• Forecasting benchmark scripts, custom forecast presets, or M4/PEMS/ECL/ETT/Solar recipes: forecasting-experiments.
• Dataset file layout, CSV/NPY/TS placement, or validation of raw inputs: data-preparation.
• TimeMixer tensor internals, PDM/FMM shapes, or decomposition/downsampling behavior: model-architecture.

Task map

Task	Source branch	Typical data key	Main metric	Main outputs
Imputation	Exp_Imputation	ETTh1, ETTh2, ETTm1, ETTm2, custom, m4, PEMS, Solar	Masked-value MSELoss during training; MAE/MSE/RMSE/MAPE/MSPE on test	result_imputation.txt, results/<setting>/metrics.npy, pred.npy, true.npy
Anomaly detection	Exp_Anomaly_Detection	PSM, MSL, SMAP, SMD, SWAT	Accuracy, precision, recall, F-score after thresholding and adjustment	result_anomaly_detection.txt
Classification	Exp_Classification	UEA	Cross-entropy loss; accuracy for validation/test	results/<setting>/result_classification.txt

Bundled assets

• references/task-workflows.md
• references/troubleshooting.md
• scripts/build_universal_task_command.py

Safe defaults

1. Use the command builder to assemble a command before any training run.
2. Keep c_out aligned with the input channel count for imputation and anomaly detection.
3. For multi-feature UEA classification, prefer channel_independence=0.
4. Treat anomaly_ratio as a percent value in the source threshold calculation.
5. Do not launch benchmark-scale training or dataset downloads from this sub-skill.