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README.md

@@ -1,3 +1,69 @@
 ---
+language:
+- en
+tags:
+- time-series
+- classification
+- in-context-learning
+- transformer
 license: apache-2.0
 ---
+
+# TIMEE: Time Series Classification via In-Context Learning
+
+TIMEE is a pretrained transformer for time series classification.
+It classifies test series in a **single forward pass** given labeled training examples — no per-dataset training or fine-tuning required.
+
+**Paper:** *TIMEE: End-to-end Time Series Classification via In-Context Learning* (under review)
+
+## Usage
+
+```bash
+pip install timee
+```
+
+```python
+from timee import TimeeClassifier
+import numpy as np
+
+# Downloads weights automatically on first use
+clf = TimeeClassifier.from_pretrained()
+
+# X: (n_samples, n_channels, seq_len) float32
+X_train = np.random.randn(20, 1, 256).astype(np.float32)
+y_train = np.array([0, 1] * 10)
+X_test  = np.random.randn(5,  1, 256).astype(np.float32)
+
+predictions, probabilities = clf.predict(X_train, y_train, X_test)
+```
+
+Labels can be any type (`int`, `str`, etc.).
+Datasets with more than 10 classes are handled automatically via one-vs-rest.
+
+## UCR Benchmark Results
+
+| Dataset            | Classes | Accuracy | ROC AUC |
+|--------------------|---------|----------|---------|
+| ArrowHead          | 3       | 76.6 %   | 0.964   |
+| ECG5000            | 5       | 95.0 %   | 0.952   |
+| GunPoint           | 2       | 98.7 %   | 0.997   |
+| ItalyPowerDemand   | 2       | 96.0 %   | 0.993   |
+| TwoPatterns        | 4       | 99.8 %   | 1.000   |
+
+Results use the default 4-member ensemble (interpolate × {256, 512} × {raw, first-difference}).
+
+## Model Details
+
+- **Parameters:** 4,557,322
+- **Input:** univariate or multivariate time series, any length
+- **Output:** class probabilities over up to 10 classes (OvR for more)
+- **Inference:** single forward pass; no dataset-specific adaptation
+
+## Citation
+
+```bibtex
+@inproceedings{timee2026,
+  title = {TIMEE: End-to-end Time Series Classification via In-Context Learning},
+  year  = {2026},
+}
+```