feat: add support for more ml models

docs/changelogs/v0.0.25.md

@@ -1,5 +1,6 @@
 ### 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 +1,29 @@
 ### Features
 
-* **Prediction intervals for AutoLGBM, AutoNHITS, and AutoTFT**: These models now support quantile forecasts via the `quantiles` parameter. Pass a list of floats between 0 and 1 to receive additional output columns named `model-q-{percentile}`. Note that `level` is not supported for these models; use `quantiles` instead.
+* **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`.
+
+    ```python
+    import pandas as pd
+    from timecopilot.models.ml import (
+        AutoLinearRegression,
+        AutoXGBoost,
+        AutoRidge,
+        AutoLasso,
+        AutoElasticNet,
+        AutoRandomForest,
+        AutoCatboost,
+    )
+
+    df = pd.read_csv(
+        "https://timecopilot.s3.amazonaws.com/public/data/air_passengers.csv",
+        parse_dates=["ds"],
+    )
+
+    model = AutoRidge()
+    fcst_df = model.forecast(df, h=12, quantiles=[0.1, 0.5, 0.9])
+    ```
+
+* **Quantile forecasts for AutoLGBM, AutoNHITS, and AutoTFT**: These models now support quantile forecasts via the `quantiles` parameter. Pass a list of floats between 0 and 1 to receive additional output columns named `model-q-{percentile}`. Note that `level` is not supported for these models; use `quantiles` instead.
 
     - `AutoLGBM` computes prediction intervals via conformal prediction using cross-validation residuals.
     - `AutoNHITS` and `AutoTFT` are trained with [`MQLoss`](https://nixtla.github.io/neuralforecast/losses.pytorch.html) when quantiles are requested.
@@ -10,8 +33,10 @@
     from timecopilot.models.ml import AutoLGBM
     from timecopilot.models.neural import AutoNHITS, AutoTFT
 
-    df = pd.read_csv("AirPassengers.csv", parse_dates=["ds"])
-    df.insert(0, "unique_id", "AirPassengers")
+    df = pd.read_csv(
+        "https://timecopilot.s3.amazonaws.com/public/data/air_passengers.csv",
+        parse_dates=["ds"],
+    )
 
     model = AutoLGBM()
     fcst_df = model.forecast(df, h=12, quantiles=[0.1, 0.5, 0.9])

pyproject.toml

@@ -61,6 +61,7 @@ dependencies = [
   "accelerate>=1.10.1",
   "arch>=7.2.0",
   "black>=25.9.0",
+  "catboost>=1.2.10",
   "datasets>=4.1.1",
   "fire",
   "fsspec>=2025.9.0",
@@ -103,6 +104,7 @@ dependencies = [
   "tsfeatures>=0.4.5",
   "utilsforecast[plotting]>=0.2.15",
   "wandb==0.22.1",
+  "xgboost>=3.2.0",
 ]
 description = "The GenAI Forecasting Agent · LLMs × Time Series Foundation Models"
 license = "MIT"

tests/models/conftest.py

@@ -9,7 +9,12 @@
 from timecopilot.models.foundation.patchtst_fm import PatchTSTFM
 from timecopilot.models.foundation.timesfm import TimesFM
 from timecopilot.models.foundation.toto import Toto
-from timecopilot.models.ml import AutoLGBM
+from timecopilot.models.ml import (
+    AutoElasticNet,
+    AutoLGBM,
+    AutoLinearRegression,
+    AutoXGBoost,
+)
 from timecopilot.models.neural import AutoNHITS, AutoTFT
 from timecopilot.models.prophet import Prophet
 from timecopilot.models.stats import (
@@ -39,6 +44,9 @@ def disable_mps_session(monkeypatch):
 
 models = [
     AutoLGBM(num_samples=2, cv_n_windows=2),
+    AutoLinearRegression(num_samples=2, cv_n_windows=2),
+    AutoXGBoost(num_samples=2, cv_n_windows=2),
+    AutoElasticNet(num_samples=2, cv_n_windows=2),
     AutoNHITS(
         num_samples=2,
         config=dict(

tests/models/test_models.py

@@ -99,8 +99,18 @@ def test_freq_inferred_correctly(model, freq):
 )
 @pytest.mark.parametrize("h", [1, 12])
 def test_correct_forecast_dates(model, freq, h):
-    if model.alias in ["AutoLGBM", "AutoNHITS", "AutoTFT"]:
-        # AutoLGBM requires a certain minimum length
+    _ml_auto_aliases = {
+        "AutoLGBM",
+        "AutoLinearRegression",
+        "AutoXGBoost",
+        "AutoRidge",
+        "AutoLasso",
+        "AutoElasticNet",
+        "AutoRandomForest",
+        "AutoCatboost",
+    }
+    if model.alias in _ml_auto_aliases | {"AutoNHITS", "AutoTFT"}:
+        # 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"]
         }
@@ -231,11 +241,19 @@ def test_using_quantiles(model):
 def test_using_level(model):
     level = [0, 20, 40, 60, 80]  # corresponds to qs [0.1, 0.2, ..., 0.9]
     df = generate_series(n_series=2, freq="D")
-    if model.alias in [
+    _level_unsupported = {
         "AutoLGBM",
+        "AutoLinearRegression",
+        "AutoXGBoost",
+        "AutoRidge",
+        "AutoLasso",
+        "AutoElasticNet",
+        "AutoRandomForest",
+        "AutoCatboost",
         "AutoNHITS",
         "AutoTFT",
-    ]:
+    }
+    if model.alias in _level_unsupported:
         # These models do not support levels yet
         with pytest.raises(ValueError) as excinfo:
             model.forecast(

timecopilot/models/__init__.py

@@ -1,3 +1,13 @@
+from .ml import (
+    AutoCatboost,
+    AutoElasticNet,
+    AutoLasso,
+    AutoLGBM,
+    AutoLinearRegression,
+    AutoRandomForest,
+    AutoRidge,
+    AutoXGBoost,
+)
 from .stats import (
     ADIDA,
     IMAPA,
@@ -14,10 +24,18 @@
 
 __all__ = [
     "ADIDA",
+    "AutoCatboost",
+    "AutoElasticNet",
     "IMAPA",
     "AutoARIMA",
     "AutoCES",
     "AutoETS",
+    "AutoLasso",
+    "AutoLGBM",
+    "AutoLinearRegression",
+    "AutoRandomForest",
+    "AutoRidge",
+    "AutoXGBoost",
     "CrostonClassic",
     "DynamicOptimizedTheta",
     "HistoricAverage",