feat: add support for more neural models

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: `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

tests/models/conftest.py

@@ -15,7 +15,13 @@
     AutoLinearRegression,
     AutoXGBoost,
 )
-from timecopilot.models.neural import AutoNHITS, AutoTFT
+from timecopilot.models.neural import (
+    AutoDeepAR,
+    AutoNBEATS,
+    AutoNHITS,
+    AutoPatchTST,
+    AutoTFT,
+)
 from timecopilot.models.prophet import Prophet
 from timecopilot.models.stats import (
     ADIDA,
@@ -65,6 +71,36 @@ 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,
+        ),
+    ),
+    AutoDeepAR(
+        num_samples=2,
+        config=dict(
+            max_steps=1,
+            val_check_steps=1,
+            input_size=12,
+            lstm_n_layers=1,
+            lstm_hidden_size=8,
+        ),
+    ),
+    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",