---

name: "pypots" description: "Routes PyPOTS workflows for imputation, forecasting, classification, anomaly detection, clustering, representation learning, and the command-line and data-management surfaces around them." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

PyPOTS

PyPOTS is a toolbox for machine learning on partially-observed time series. Use this skill when the request is about missing-value handling, time-series prediction, task-specific training, model inspection, data preparation, or the pypots-cli interface.

Start Here

• Install from PyPI: pip install pypots
• Install from this checkout: pip install -e .
• If you will use YAML configs or the CLI helpers, also install pyyaml.
• Optional backend packages:
  • torch-geometric, torch-scatter, torch-sparse for Raindrop.
  • LLM-oriented workflows may need extra tokenizer/model packages and model downloads.

Run the bundled scripts/check_install.py for a safe first check:

python scripts/check_install.py

That helper prints the package version, model counts, CLI command surface, and optional backend status without training anything.

Route Map

Imputation

Use for filling missing values with Mean, Median, LOCF, Lerp, SAITS, BRITS, USGAN, TEFN, TimeLLM, and the other imputation families. Read this route for impute, predict()["imputation"], lazy-loading HDF5 inputs, checkpoint save/load, and evaluation with missingness masks.

Forecasting

Use for future-value prediction with BTTF, TEFN, TimeMixer, TimeLLM, TimesNet, FITS, GPT4TS, MOMENT, and related forecasting models. Read this route for forecast, predict()["forecasting"], X_pred inputs, and model selection across short- and long-horizon use cases.

Classification

Use for labeled time-series classification with Raindrop, TS2Vec, TimesNet, BRITS, CSAI, GRUD, SAITS, iTransformer, TEFN, PatchTST, and Autoformer. Read this route for classify, predict_proba, class labels, and binary metric interpretation.

Anomaly detection

Use for anomaly scoring and binary anomaly labels with TimesNet, TEFN, TimeMixer, Transformer, FiLM, SegRNN, and the other detector families. Read this route for detect, predict()["anomaly_detection"], anomaly-rate inputs, and score-to-label evaluation.

Clustering

Use for cluster assignment workflows with CRLI and VaDER. Read this route for cluster, predict()["clustering"], latent-variable outputs, and external/internal cluster validation metrics.

Representation

Use for time-series embeddings and vectorization with TS2Vec. Read this route for represent, predict()["representation"], and downstream use of learned embeddings.

CLI and data management

Use for pypots-cli workflows: info, model, train, predict, evaluate, tune, recommend, benchmark, and data. Read this route for config files, HDF5/CSV conversion, dataset profiles, benchmark comparisons, and model inspection/config generation.

Shared References

• references/model-overview.md — model families, representative choices, and which task route owns each family.
• references/api-reference.md — verified task bases, helper methods, constructor patterns, return keys, and optimizer wrappers.
• references/data-formats.md — HDF5/CSV schemas, BaseDataset sample order, and the programmatic data-utility surface.
• references/cli-reference.md — command groups, flags, config shapes, and CLI-specific gotchas.
• references/troubleshooting.md — install/import problems, backend gaps, shape/key mismatches, and CLI/data failures.
• references/repo-provenance.md — source commit and refresh baseline.

Usage Notes

• Most models accept in-memory dicts or HDF5 file paths.
• All task routes rely on the shared pypots.data and pypots.nn.functional contracts described in the references above.
• Use CPU by default; switch to CUDA only when you want acceleration and the environment has a compatible GPU.
• If a request names TimeSeriesAI, treat it as a separate service-oriented surface unless the user explicitly wants the local PyPOTS package.

Before You Refresh

Check references/repo-provenance.md against the current checkout before using this skill on a newer PyPOTS revision. If the commit, dirty state, or package version changed, refresh the skill instead of assuming these routes are current.