feat: add support for more neural models

docs/api/models/foundation/models.md

@@ -16,6 +16,11 @@
         members:
             - Moirai
 
+::: timecopilot.models.foundation.patchtst_fm
+    options:
+        members:
+            - PatchTSTFM
+
 ::: timecopilot.models.foundation.sundial
     options:
         members:

docs/api/models/ml.md

@@ -4,4 +4,11 @@
 ::: timecopilot.models.ml
     options:
         members:
-            - AutoLGBM
+            - AutoCatboost
+            - AutoElasticNet
+            - AutoLasso
+            - AutoLGBM
+            - AutoLinearRegression
+            - AutoRandomForest
+            - AutoRidge
+            - AutoXGBoost

docs/api/models/neural.md

@@ -4,5 +4,8 @@
 ::: timecopilot.models.neural
     options:
         members:
+            - AutoDeepAR
+            - AutoNBEATS
             - AutoNHITS
+            - AutoPatchTST
             - AutoTFT

docs/changelogs/v0.0.25.md

@@ -1,6 +1,5 @@
 ### Features
 
-
 * **TimeGPT finetuning**: Finetuning is now supported for TimeGPT. You can adapt the pre-trained model to your data before forecasting via `TimeGPTFinetuningConfig`, with options for loss function and finetuning depth. See [#332](https://github.com/TimeCopilot/timecopilot/pull/332) and the [Finetuning Foundation Models](https://timecopilot.dev/examples/finetuning/) example for a full walkthrough.
 
     ```python

docs/changelogs/v0.0.26.md

@@ -1,6 +1,21 @@
 ### Features
 
-* **New ML models**: Added 7 new auto ML models powered by `mlforecast`'s hyperparameter optimization: `AutoLinearRegression`, `AutoXGBoost`, `AutoRidge`, `AutoLasso`, `AutoElasticNet`, `AutoRandomForest`, and `AutoCatboost`. All models support `quantiles` for probabilistic forecasts via conformal prediction and follow the same interface as the existing `AutoLGBM`.
+* **New neural models**: Added 3 new auto neural models: `AutoNBEATS`, `AutoDeepAR`, and `AutoPatchTST`. All support `quantiles` for probabilistic forecasts trained with `MQLoss` and follow the same interface as the existing `AutoNHITS` and `AutoTFT`.
+
+    ```python
+    import pandas as pd
+    from timecopilot.models.neural import AutoDeepAR, AutoNBEATS, AutoPatchTST
+
+    df = pd.read_csv(
+        "https://timecopilot.s3.amazonaws.com/public/data/air_passengers.csv",
+        parse_dates=["ds"],
+    )
+
+    model = AutoNBEATS()
+    fcst_df = model.forecast(df, h=12, quantiles=[0.1, 0.5, 0.9])
+    ```
+
+* **New ML models**: Added 7 new auto ML models: `AutoLinearRegression`, `AutoXGBoost`, `AutoRidge`, `AutoLasso`, `AutoElasticNet`, `AutoRandomForest`, and `AutoCatboost`. All models support `quantiles` for probabilistic forecasts via conformal prediction and follow the same interface as the existing `AutoLGBM`.
 
     ```python
     import pandas as pd

docs/model-hub.md

@@ -42,6 +42,7 @@ TimeCopilot provides a unified interface to state-of-the-art foundation models f
 - [Chronos](api/models/foundation/models.md#timecopilot.models.foundation.chronos) ([arXiv:2403.07815](https://arxiv.org/abs/2403.07815))
 - [FlowState](api/models/foundation/models.md#timecopilot.models.foundation.flowstate) ([arXiv:2508.05287](https://arxiv.org/abs/2508.05287)) 
 - [Moirai](api/models/foundation/models.md#timecopilot.models.foundation.moirai) ([arXiv:2402.02592](https://arxiv.org/abs/2402.02592))
+- [PatchTST-FM](api/models/foundation/models.md#timecopilot.models.foundation.patchtst_fm) ([arXiv:2602.06909](https://arxiv.org/abs/2602.06909))
 - [Sundial](api/models/foundation/models.md#timecopilot.models.foundation.sundial) ([arXiv:2502.00816](https://arxiv.org/pdf/2502.00816))
 - [TabPFN](api/models/foundation/models.md#timecopilot.models.foundation.tabpfn) ([arXiv:2501.02945](https://arxiv.org/abs/2501.02945))
 - [TiRex](api/models/foundation/models.md#timecopilot.models.foundation.tirex) ([arXiv:2505.23719](https://arxiv.org/abs/2505.23719))
@@ -79,11 +80,21 @@ TimeCopilot integrates the popular Prophet model for time series forecasting, de
 
 TimeCopilot provides access to automated machine learning models for time series forecasting. These models leverage gradient boosting and other ML techniques to automatically select features and optimize hyperparameters for your specific time series data.
 
+- [AutoCatboost](api/models/ml.md#timecopilot.models.ml.AutoCatboost)
+- [AutoElasticNet](api/models/ml.md#timecopilot.models.ml.AutoElasticNet)
+- [AutoLasso](api/models/ml.md#timecopilot.models.ml.AutoLasso)
 - [AutoLGBM](api/models/ml.md#timecopilot.models.ml.AutoLGBM)
+- [AutoLinearRegression](api/models/ml.md#timecopilot.models.ml.AutoLinearRegression)
+- [AutoRandomForest](api/models/ml.md#timecopilot.models.ml.AutoRandomForest)
+- [AutoRidge](api/models/ml.md#timecopilot.models.ml.AutoRidge)
+- [AutoXGBoost](api/models/ml.md#timecopilot.models.ml.AutoXGBoost)
 
 ## Neural Network Models
 
 TimeCopilot integrates state-of-the-art neural network models for time series forecasting. These models leverage deep learning architectures specifically designed for temporal data, offering powerful capabilities for complex pattern recognition and long-range dependency modeling.
 
+- [AutoDeepAR](api/models/neural.md#timecopilot.models.neural.AutoDeepAR)
+- [AutoNBEATS](api/models/neural.md#timecopilot.models.neural.AutoNBEATS)
 - [AutoNHITS](api/models/neural.md#timecopilot.models.neural.AutoNHITS)
+- [AutoPatchTST](api/models/neural.md#timecopilot.models.neural.AutoPatchTST)
 - [AutoTFT](api/models/neural.md#timecopilot.models.neural.AutoTFT)

tests/models/conftest.py

@@ -15,7 +15,12 @@
     AutoLinearRegression,
     AutoXGBoost,
 )
-from timecopilot.models.neural import AutoNHITS, AutoTFT
+from timecopilot.models.neural import (
+    AutoNBEATS,
+    AutoNHITS,
+    AutoPatchTST,
+    AutoTFT,
+)
 from timecopilot.models.prophet import Prophet
 from timecopilot.models.stats import (
     ADIDA,
@@ -65,6 +70,27 @@ def disable_mps_session(monkeypatch):
             hidden_size=8,
         ),
     ),
+    AutoNBEATS(
+        num_samples=2,
+        config=dict(
+            max_steps=1,
+            val_check_steps=1,
+            input_size=12,
+            n_harmonics=1,
+            n_polynomials=1,
+            stack_types=["identity"],
+        ),
+    ),
+    AutoPatchTST(
+        num_samples=2,
+        config=dict(
+            max_steps=1,
+            val_check_steps=1,
+            input_size=12,
+            hidden_size=8,
+            n_heads=2,
+        ),
+    ),
     AutoARIMA(),
     SeasonalNaive(),
     ZeroModel(),

tests/models/test_models.py

@@ -109,7 +109,14 @@ def test_correct_forecast_dates(model, freq, h):
         "AutoRandomForest",
         "AutoCatboost",
     }
-    if model.alias in _ml_auto_aliases | {"AutoNHITS", "AutoTFT"}:
+    _neural_auto_aliases = {
+        "AutoNHITS",
+        "AutoTFT",
+        "AutoNBEATS",
+        "AutoDeepAR",
+        "AutoPatchTST",
+    }
+    if model.alias in _ml_auto_aliases | _neural_auto_aliases:
         # These auto ML and neural models require a longer minimum series length
         sizes_per_freq = {
             freq: 1_000 for freq in ["10S", "10T", "15T", "5T", "H", "Q-DEC"]
@@ -230,7 +237,13 @@ def test_using_quantiles(model):
         elif "moe" in model.alias.lower():
             # MoE is a bit more lenient with the monotonicity condition
             assert fcst_df[c1].le(fcst_df[c2]).mean() >= 0.5
-        elif model.alias in ["AutoNHITS", "AutoTFT"]:
+        elif model.alias in [
+            "AutoNHITS",
+            "AutoTFT",
+            "AutoNBEATS",
+            "AutoDeepAR",
+            "AutoPatchTST",
+        ]:
             # test config uses max_steps=1, so quantile ordering is not guaranteed
             continue
         else:
@@ -252,6 +265,9 @@ def test_using_level(model):
         "AutoCatboost",
         "AutoNHITS",
         "AutoTFT",
+        "AutoNBEATS",
+        "AutoDeepAR",
+        "AutoPatchTST",
     }
     if model.alias in _level_unsupported:
         # These models do not support levels yet

timecopilot/models/__init__.py

@@ -8,6 +8,7 @@
     AutoRidge,
     AutoXGBoost,
 )
+from .neural import AutoDeepAR, AutoNBEATS, AutoPatchTST
 from .stats import (
     ADIDA,
     IMAPA,
@@ -25,8 +26,11 @@
 __all__ = [
     "ADIDA",
     "AutoCatboost",
+    "AutoDeepAR",
     "AutoElasticNet",
     "IMAPA",
+    "AutoNBEATS",
+    "AutoPatchTST",
     "AutoARIMA",
     "AutoCES",
     "AutoETS",