---

name: "classification" description: "Guides PyPOTS classification workflows for partially-observed time series, including labels, probability outputs, model selection, metrics, and optional Raindrop dependencies." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

PyPOTS Classification

Use this sub-skill when the user needs class labels or class probabilities for partially-observed time-series samples.

Natural Triggers

• "classify time series with PyPOTS"
• "use Raindrop / TS2Vec / TimesNet / BRITS / CSAI"
• "get classification_proba"
• "use predict_proba() or classify()"
• "why does Raindrop need torch-geometric?"
• "evaluate ROC-AUC, PR-AUC, F1, precision, or recall"

First References

• Read ../../references/data-formats.md#classification for X and y.
• Read ../../references/api-reference.md for result keys and classifier helpers.
• Read ../../references/model-overview.md#classification for model-family choice.
• Read ../../references/troubleshooting.md for optional GNN dependencies, missing labels, and constructor drift.
• Use ../cli/ for config-driven pypots-cli train, predict, evaluate, recommend, or tune workflows.

Scope

This route covers:

• Classifier models: Raindrop, TS2Vec, TimesNet, BRITS, CSAI, GRUD, SAITS, iTransformer, TEFN, PatchTST, Autoformer, and SeFT.
• Training with train_set = {"X": X, "y": y}.
• Probability and class-label extraction.
• Binary classification metrics exposed through pypots.nn.functional.
• HDF5 lazy-loading for train/validation/test sets.

Route elsewhere:

• Missing-value filling -> ../imputation/.
• Future prediction -> ../forecasting/.
• Anomaly labels over a series -> ../anomaly-detection/.
• Embedding-only workflows -> ../representation/.

Core Workflow

1. Prepare X with shape [n_samples, n_steps, n_features] and y with one label per sample.
2. Select a classifier. Use a non-Raindrop model if the environment lacks the torch-geometric stack.
3. Instantiate the model with n_steps, n_features, n_classes, and the model-specific architecture parameters.
4. Train with model.fit(train_set, val_set).
5. Predict with model.predict(test_set) and read:
   • results["classification_proba"] for probabilities.
   • results["classification"] for labels.
6. Or call predict_proba(test_set) and classify(test_set) directly.
7. Evaluate with calc_binary_classification_metrics() for binary workflows.

Minimal Example Shape

from pypots.classification import TimesNet

model = TimesNet(
    n_steps=n_steps,
    n_features=n_features,
    n_classes=n_classes,
    n_layers=1,
    top_k=1,
    d_model=16,
    d_ffn=16,
    n_kernels=3,
    epochs=1,
    device="cpu",
)
model.fit({"X": train_X, "y": train_y}, {"X": val_X, "y": val_y})
proba = model.predict_proba({"X": test_X})
labels = model.classify({"X": test_X})

For TS2Vec, predict() can be called with classifier choices such as classifier_type="svm", "knn", or "lr" in the native tests.

Common Decision Points

• Use pypots-cli model describe --name MODEL --task classification before generating a config; many shared model names differ by task wrapper.
• Start on CPU and a small fixture before enabling CUDA.
• Use classification_proba for ranking metrics such as ROC-AUC and PR-AUC.
• Use classification only when discrete class labels are needed.
• If Raindrop fails on missing PyG packages, install the compatible PyG stack or choose a non-GNN classifier.

Validation Signals

A successful classification workflow returns probabilities and labels for the same number of samples as the test input. Binary native tests commonly assert a ROC-AUC threshold and log PR-AUC, F1, precision, and recall.