diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index a05ff0a..3679386 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -145,7 +145,7 @@ jobs:
         run: uv python install
 
       - name: Install dependencies
-        run: uv sync --dev
+        run: uv sync --dev --group ml
 
       - name: Run migrations
         run: uv run alembic upgrade head
diff --git a/.gitignore b/.gitignore
index 13ebac4..63a6cb2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -40,8 +40,13 @@ htmlcov/
 # uv
 .uv/
 
+# Generated data
+backend/data/
 # Ruff
 .ruff_cache/
 
 # Tooling / assistants
 .claude/
+
+# Personal developer journal (private, not for team repo)
+.journal-entries/
diff --git a/backend/pyproject.toml b/backend/pyproject.toml
index 6c665a3..3c6ed78 100644
--- a/backend/pyproject.toml
+++ b/backend/pyproject.toml
@@ -30,6 +30,16 @@ dev = [
     "pytest-asyncio>=0.25",
     "httpx>=0.28",
 ]
+ml = [
+    "matplotlib>=3.11.2",
+    "numpy>=2.5.3",
+    "pandas>=3.0.6",
+    "scikit-learn>=1.9.1",
+    "scipy>=1.18.1",
+    "statsmodels>=0.15.0",
+    "numpy>=2.5.3",
+    "pandas>=3.0.6",
+]
 
 [tool.hatch.build.targets.wheel]
 packages = ["src/foresight"]
@@ -59,3 +69,4 @@ known-first-party = ["foresight"]
 [tool.pytest.ini_options]
 asyncio_mode = "auto"
 testpaths = ["tests"]
+pythonpath = ["src", "scripts"]
diff --git a/backend/scripts/compare_models.py b/backend/scripts/compare_models.py
new file mode 100644
index 0000000..9f3af18
--- /dev/null
+++ b/backend/scripts/compare_models.py
@@ -0,0 +1,87 @@
+"""Compares model performance on predicting room rate.
+
+Run from backend/: uv run python scripts/compare_models.py
+"""
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+from generate_mock_data import write_csv
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
+from services.ml.bayesian import BayesianModel
+from services.ml.neural_net import NNModel
+from services.ml.regression import GBMModel
+from services.ml.timeseries import TimeSeriesModel
+
+# Known only after the night, so never used to predict price.
+POST_STAY_COLUMNS = ["rooms_sold", "occupancy", "room_revenue"]
+MODELS = [BayesianModel, NNModel, GBMModel, TimeSeriesModel]
+METRICS = ["mae", "rmse", "mape", "r2", "coverage"]
+TEST_FRAC = 0.2
+SEEDS = range(5)
+
+
+def load_data() -> tuple[pd.DataFrame, pd.Series]:
+    """Generates the mock data and splits it into features and target.
+
+    Returns:
+        tuple[pd.DataFrame, pd.Series]: Features and rate, sorted by stay_date.
+    """
+    df = pd.read_csv(write_csv(), parse_dates=["stay_date"])
+    df = df.sort_values("stay_date", kind="stable").reset_index(drop=True)
+    x = df.drop(columns=["rate", "stay_date", *POST_STAY_COLUMNS])
+    x = pd.get_dummies(x, columns=["room_type", "condition"], dtype=float)
+    return x, df["rate"]
+
+
+def seed_runs(x_train, y_train, x_test, y_test) -> pd.DataFrame:
+    """Scores each model on the holdout once per seed.
+
+    Args:
+        x_train (pd.DataFrame): Training features.
+        y_train (pd.Series): Training target.
+        x_test (pd.DataFrame): Holdout features.
+        y_test (pd.Series): Holdout target.
+
+    Returns:
+        pd.DataFrame: One row per model per seed, with evaluate metrics.
+    """
+    rows = []
+    for model_cls in MODELS:
+        for seed in SEEDS:
+            model = model_cls(seed=seed).fit(x_train, y_train)
+            rows.append({"model": model.name, **model.evaluate(x_test, y_test)})
+    return pd.DataFrame(rows)
+
+
+def summarize(results: pd.DataFrame) -> pd.DataFrame:
+    """Averages metrics per model.
+
+    Args:
+        results (pd.DataFrame): Output of seed_runs.
+
+    Returns:
+        pd.DataFrame: Mean and std of each metric, one row per model.
+    """
+    return results.groupby("model", sort=False)[METRICS].agg(["mean", "std"]).round(3)
+
+
+def main():
+    """Prints holdout results for each model."""
+    x, y = load_data()
+    cut = int(len(x) * (1 - TEST_FRAC))
+    x_train, x_test, y_train, y_test = x[:cut], x[cut:], y[:cut], y[cut:]
+
+    naive_mae = np.mean(np.abs(y_test - y_train.mean()))
+    print(f"Baseline (always predict the average rate): MAE {naive_mae:.2f}\n")
+
+    print(f"Holdout (last {TEST_FRAC:.0%} of nights, {len(SEEDS)} seeds)")
+    print(summarize(seed_runs(x_train, y_train, x_test, y_test)).to_string())
+
+
+if __name__ == "__main__":
+    main()
diff --git a/backend/scripts/generate_mock_data.py b/backend/scripts/generate_mock_data.py
new file mode 100644
index 0000000..9cb382c
--- /dev/null
+++ b/backend/scripts/generate_mock_data.py
@@ -0,0 +1,166 @@
+"""Generates mock hotel data with a known pricing rule.
+
+Run from backend/: uv run python scripts/generate_mock_data.py
+"""
+
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+CSV_PATH = Path(__file__).resolve().parents[1] / "data" / "mock_hotel_data.csv"
+
+# RoomType.name: (base_rate, room_count)
+ROOM_TYPES = {
+    "standard": (120.0, 30),
+    "deluxe": (180.0, 15),
+    "suite": (300.0, 5),
+}
+
+# CompetitorRate.hotel_name: typical nightly rate
+COMPETITORS = {
+    "harbour_view": 140.0,
+    "the_grand": 230.0,
+    "city_lodge": 160.0,
+    "riverside": 190.0,
+}
+COMPETITOR_REF = np.mean(list(COMPETITORS.values()))
+
+
+def generate(seed: int = 9, start: str = "2024-01-01", days: int = 730) -> pd.DataFrame:
+    """Generates one row per room type per night.
+
+    Args:
+        seed (int): Random seed for reproducible data.
+        start (str): First stay date, as YYYY-MM-DD.
+        days (int): Number of nights to generate.
+
+    Returns:
+        pd.DataFrame: Mock nightly data, sorted by stay_date then room_type.
+    """
+    rng = np.random.default_rng(seed)
+    dates = pd.date_range(start, periods=days, freq="D")
+    doy = dates.dayofyear.to_numpy()
+    is_weekend = dates.dayofweek.isin([4, 5]).astype(int)  # Fri, Sat nights
+    season = 0.15 * np.sin(2 * np.pi * (doy - 100) / 365)
+
+    # Weather: warm summers, rain on ~35% of days.
+    temp_max_c = 12 + 7 * np.sin(2 * np.pi * (doy - 110) / 365) + rng.normal(0, 2, days)
+    temp_min_c = temp_max_c - rng.normal(6, 1.5, days)
+    rainy = rng.random(days) < 0.35
+    precip_mm = np.where(rainy, rng.exponential(6, days), 0.0)
+    wind_kph = rng.gamma(2, 8, days) + 8 * rainy
+    condition = np.where(
+        rainy, "rain", np.where(rng.random(days) < 0.5, "cloudy", "sunny")
+    )
+
+    # Events: total expected attendance of the day's events, ~10% of days.
+    has_event = rng.random(days) < 0.10
+    expected_attendance = np.where(has_event, rng.lognormal(9.5, 0.7, days), 0)
+
+    demand = np.clip(
+        0.55
+        + season
+        + 0.15 * is_weekend
+        + 0.25 * np.minimum(expected_attendance / 40_000, 1)
+        - 0.05 * rainy
+        - 0.03 * (wind_kph > 30)
+        + rng.normal(0, 0.03, days),
+        0.05,
+        1.0,
+    )
+
+    # Flights into the city.
+    flight_count = (
+        400 + 300 * season + 60 * is_weekend + expected_attendance / 200
+    ) + rng.normal(0, 25, days)
+    avg_fare_eur = 110 + 150 * (demand - 0.55) + rng.normal(0, 10, days)
+
+    # Competitors: each lists a rate or sells out; summarized per night.
+    listed = np.column_stack(
+        [
+            typical * (1 + 0.8 * (demand - 0.55)) * rng.normal(1, 0.05, days)
+            for typical in COMPETITORS.values()
+        ]
+    )
+    sold_out = rng.random(listed.shape) < np.clip(2 * (demand - 0.8), 0, 0.5)[:, None]
+    all_sold_out = sold_out.all(axis=1)
+    competitor_rate = np.where(
+        all_sold_out,  # no rate to observe; fall back to what they'd have listed
+        listed.mean(axis=1),
+        np.nanmean(np.where(sold_out, np.nan, listed), axis=1),
+    )
+    competitors_sold_out = sold_out.sum(axis=1)
+
+    frames = []
+    for room_type, (base_rate, room_count) in ROOM_TYPES.items():
+        # Pricing rule the models should learn.
+        fair_rate = base_rate * (0.75 + 0.5 * demand)
+        rate = (
+            fair_rate
+            + 0.3 * base_rate * (competitor_rate / COMPETITOR_REF - 1)
+            + 0.05 * base_rate * is_weekend
+            + rng.normal(0, 0.03 * base_rate, days)
+        )
+        occupancy_prob = np.clip(
+            demand + rng.normal(0, 0.05, days) - 1.5 * (rate / fair_rate - 1), 0, 1
+        )
+        rooms_sold = rng.binomial(room_count, occupancy_prob)
+
+        frames.append(
+            pd.DataFrame(
+                {
+                    # DailyPerformance / RoomType
+                    "stay_date": dates.date,
+                    "room_type": room_type,
+                    "base_rate": base_rate,
+                    "rooms_available": room_count,
+                    "rate": rate.round(2),
+                    "rooms_sold": rooms_sold,
+                    "room_revenue": (rate.round(2) * rooms_sold).round(2),
+                    "occupancy": (rooms_sold / room_count).round(4),
+                    # From stay_date
+                    "day_of_week": dates.dayofweek,
+                    "month": dates.month,
+                    "is_weekend": is_weekend,
+                    # Weather
+                    "temp_max_c": temp_max_c.round(1),
+                    "temp_min_c": temp_min_c.round(1),
+                    "precip_mm": precip_mm.round(1),
+                    "wind_kph": wind_kph.round(1),
+                    "condition": condition,
+                    # Event
+                    "expected_attendance": expected_attendance.round().astype(int),
+                    # CompetitorRate
+                    "competitor_rate": competitor_rate.round(2),
+                    "competitors_sold_out": competitors_sold_out,
+                    # FlightArrival
+                    "flight_count": flight_count.round().astype(int),
+                    "avg_fare_eur": avg_fare_eur.round(2),
+                }
+            )
+        )
+
+    return (
+        pd.concat(frames)
+        .sort_values(["stay_date", "room_type"], kind="stable")
+        .reset_index(drop=True)
+    )
+
+
+def write_csv(path: Path = CSV_PATH) -> Path:
+    """Generates the mock data and writes it to a CSV.
+
+    Args:
+        path (Path): Where to write the CSV.
+
+    Returns:
+        Path: The path written to.
+    """
+    path.parent.mkdir(parents=True, exist_ok=True)
+    generate().to_csv(path, index=False)
+    return path
+
+
+if __name__ == "__main__":
+    print(f"Wrote {write_csv()}")
diff --git a/backend/src/services/ml/__init__.py b/backend/src/services/ml/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/backend/src/services/ml/base.py b/backend/src/services/ml/base.py
new file mode 100644
index 0000000..1be551b
--- /dev/null
+++ b/backend/src/services/ml/base.py
@@ -0,0 +1,119 @@
+from abc import ABC, abstractmethod
+from typing import Self
+
+import numpy as np
+import pandas as pd
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+
+
+class Model(ABC):
+    """Base class for all models. Subclasses implement fit and predict."""
+
+    name: str = "model"
+
+    def __init__(self, seed: int = 9, **params):
+        """Stores the random seed and model-specific settings.
+
+        Args:
+            seed (int): Random seed for reproducible results.
+            **params: Settings passed through to the underlying model.
+        """
+        self.seed = seed
+        self.params = params
+        self._model = None  # set by fit
+
+    def _fitted(self):
+        """Returns the underlying trained model.
+
+        Returns:
+            object: The model built by fit.
+
+        Raises:
+            RuntimeError: If fit has not been called yet.
+        """
+        if self._model is None:
+            raise RuntimeError(f"{self.name} must be fit before predicting")
+        return self._model
+
+    @abstractmethod
+    def fit(self, x: pd.DataFrame, y: pd.Series) -> Self:
+        """Trains the model on the given data.
+
+        Args:
+            x (pd.DataFrame): Training features.
+            y (pd.Series): Training target.
+
+        Returns:
+            Self: The trained model.
+        """
+
+    @abstractmethod
+    def predict(self, x: pd.DataFrame) -> np.ndarray:
+        """Predicts the target for each row.
+
+        Args:
+            x (pd.DataFrame): Features to predict on.
+
+        Returns:
+            np.ndarray: One prediction per row of x.
+
+        Raises:
+            RuntimeError: If fit has not been called yet.
+        """
+
+    def predict_interval(
+        self, x: pd.DataFrame, alpha: float = 0.05
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """Predicts a value and a (1 - alpha) interval for each row.
+
+        Args:
+            x (pd.DataFrame): Features to predict on.
+            alpha (float): Significance level; 0.05 gives a 95% interval.
+
+        Returns:
+            tuple[np.ndarray, np.ndarray, np.ndarray]: Predictions, lower bounds,
+                and upper bounds, one per row of x.
+
+        Raises:
+            NotImplementedError: If the model does not produce intervals.
+        """
+        raise NotImplementedError(f"{self.name} does not produce intervals")
+
+    def evaluate(
+        self, x: pd.DataFrame, y: pd.Series, alpha: float | None = None
+    ) -> dict[str, float | None]:
+        """Scores the model's predictions against the true values.
+
+        Args:
+            x (pd.DataFrame): Test features.
+            y (pd.Series): True target values.
+            alpha (float | None): Significance level for the interval coverage
+                check. None uses the model's own default.
+
+        Returns:
+            dict[str, float | None]: mae, rmse, mape, r2, and coverage
+                (share of y inside the (1 - alpha) interval. None if the model
+                has no intervals).
+        """
+        y_true = np.asarray(y, dtype=float)
+        y_pred = self.predict(x)
+
+        nonzero = y_true != 0  # MAPE is undefined where the true value is 0
+        mape = np.mean(np.abs((y_true - y_pred)[nonzero] / y_true[nonzero]))
+
+        try:
+            if alpha is None:
+                _, lo, hi = self.predict_interval(x)
+            else:
+                _, lo, hi = self.predict_interval(x, alpha)
+            coverage = float(np.mean((y_true >= lo) & (y_true <= hi)))
+        except NotImplementedError:
+            coverage = None
+
+        return {
+            "mae": mean_absolute_error(y_true, y_pred),
+            "rmse": float(np.sqrt(mean_squared_error(y_true, y_pred))),
+            "mape": float(mape),
+            "r2": r2_score(y_true, y_pred),
+            "coverage": coverage,
+        }
diff --git a/backend/src/services/ml/bayesian.py b/backend/src/services/ml/bayesian.py
new file mode 100644
index 0000000..2ec5d96
--- /dev/null
+++ b/backend/src/services/ml/bayesian.py
@@ -0,0 +1,58 @@
+from scipy.stats import norm
+from sklearn.linear_model import BayesianRidge
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
+
+from services.ml.base import Model
+
+
+class BayesianModel(Model):
+    """Bayesian linear regression with standardized features."""
+
+    name = "bayesian_ridge"
+
+    def fit(self, x, y):
+        """Trains the model on the given data.
+
+        Args:
+            x (pd.DataFrame): Training features.
+            y (pd.Series): Training target.
+
+        Returns:
+            BayesianModel: The trained model.
+        """
+        self._model = make_pipeline(StandardScaler(), BayesianRidge(**self.params))
+        self._model.fit(x, y)
+        return self
+
+    def predict(self, x):
+        """Predicts the target for each row.
+
+        Args:
+            x (pd.DataFrame): Features to predict on.
+
+        Returns:
+            np.ndarray: One prediction per row of x.
+
+        Raises:
+            RuntimeError: If fit has not been called yet.
+        """
+        return self._fitted().predict(x)
+
+    def predict_interval(self, x, alpha=0.05):
+        """Predicts a value and a (1 - alpha) interval for each row.
+
+        Args:
+            x (pd.DataFrame): Features to predict on.
+            alpha (float): Significance level; 0.05 gives a 95% interval.
+
+        Returns:
+            tuple[np.ndarray, np.ndarray, np.ndarray]: Predictions, lower bounds,
+                and upper bounds, one per row of x.
+
+        Raises:
+            RuntimeError: If fit has not been called yet.
+        """
+        mean, std = self._fitted().predict(x, return_std=True)
+        z = norm.ppf(1 - alpha / 2)
+        return mean, mean - z * std, mean + z * std
diff --git a/backend/src/services/ml/neural_net.py b/backend/src/services/ml/neural_net.py
new file mode 100644
index 0000000..3b08f3e
--- /dev/null
+++ b/backend/src/services/ml/neural_net.py
@@ -0,0 +1,48 @@
+from sklearn.neural_network import MLPRegressor
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
+
+from services.ml.base import Model
+
+DEFAULTS = {
+    "hidden_layer_sizes": (32, 16),
+    "max_iter": 2000,
+    "early_stopping": True,
+}
+
+
+class NNModel(Model):
+    """Small feed-forward neural network with standardized features."""
+
+    name = "mlp"
+
+    def fit(self, x, y):
+        """Trains the model on the given data.
+
+        Args:
+            x (pd.DataFrame): Training features.
+            y (pd.Series): Training target.
+
+        Returns:
+            NNModel: The trained model.
+        """
+        params = {**DEFAULTS, **self.params}
+        self._model = make_pipeline(
+            StandardScaler(), MLPRegressor(random_state=self.seed, **params)
+        )
+        self._model.fit(x, y)
+        return self
+
+    def predict(self, x):
+        """Predicts the target for each row.
+
+        Args:
+            x (pd.DataFrame): Features to predict on.
+
+        Returns:
+            np.ndarray: One prediction per row of x.
+
+        Raises:
+            RuntimeError: If fit has not been called yet.
+        """
+        return self._fitted().predict(x)
diff --git a/backend/src/services/ml/regression.py b/backend/src/services/ml/regression.py
new file mode 100644
index 0000000..0f379e2
--- /dev/null
+++ b/backend/src/services/ml/regression.py
@@ -0,0 +1,91 @@
+import numpy as np
+from sklearn.ensemble import HistGradientBoostingRegressor
+
+from services.ml.base import Model
+
+
+class GBMModel(Model):
+    "Gradient-boosted trees for per-booking dynamic pricing."
+
+    name = "hist_gradient_boosting"
+
+    def __init__(self, alpha=0.05, **params):
+        super().__init__(**params)
+        self.alpha = alpha
+
+    def fit(self, x, y):
+        """Trains point and quantile models on the given data.
+
+        Args:
+            x (pd.DataFrame): Training features (room_type should be a
+                pandas categorical dtype).
+            y (pd.Series): Training target (price).
+
+        Returns:
+            GBMModel: The trained model.
+        """
+        cat_cols = x.select_dtypes(include="category").columns.tolist()
+        base_kwargs = {k: v for k, v in self.params.items() if k != "random_state"}
+        random_state = self.params.get("random_state", 0)
+
+        def make(loss, **extra):
+            return HistGradientBoostingRegressor(
+                loss=loss,
+                categorical_features=cat_cols or None,
+                random_state=random_state,
+                **base_kwargs,
+                **extra,
+            )
+
+        self._model = {
+            "median": make("squared_error").fit(x, y),
+            "lower": make("quantile", quantile=self.alpha / 2).fit(x, y),
+            "upper": make("quantile", quantile=1 - self.alpha / 2).fit(x, y),
+        }
+        return self
+
+    def predict(self, x):
+        """Predicts the point price for each row.
+
+        Args:
+            x (pd.DataFrame): Features to predict on.
+
+        Returns:
+            np.ndarray: One prediction per row of x.
+        """
+        return self._fitted()["median"].predict(x)
+
+    def predict_interval(self, x, alpha=None):
+        """Predicts a value and interval for each row.
+
+        Args:
+            x (pd.DataFrame): Features to predict on.
+            alpha (float | None): Must match the alpha the model was
+                fit with (its quantile heads are trained for one fixed
+                alpha); pass None to use it implicitly.
+
+        Returns:
+            tuple[np.ndarray, np.ndarray, np.ndarray]: Predictions, lower
+                bounds, and upper bounds, one per row of x.
+
+        Raises:
+            RuntimeError: If fit has not been called yet.
+            ValueError: If a different alpha than the one fit is requested.
+        """
+        if alpha is not None and not np.isclose(alpha, self.alpha):
+            raise ValueError(
+                f"{self.name} was fit with alpha={self.alpha}; its quantile "
+                f"heads can't serve alpha={alpha}. Re-instantiate with "
+                f"GBMModel(alpha={alpha}) and refit."
+            )
+        model = self._fitted()
+        pred = model["median"].predict(x)
+        lower = model["lower"].predict(x)
+        upper = model["upper"].predict(x)
+        # Lower/upper/median are three independently-fit models, so they can
+        # occasionally cross or leave the median outside the band. Repair
+        # that rather than let it silently violate lower <= pred <= upper.
+        lower, upper = np.minimum(lower, upper), np.maximum(lower, upper)
+        lower = np.minimum(lower, pred)
+        upper = np.maximum(upper, pred)
+        return pred, lower, upper
diff --git a/backend/src/services/ml/timeseries.py b/backend/src/services/ml/timeseries.py
new file mode 100644
index 0000000..eb0e24d
--- /dev/null
+++ b/backend/src/services/ml/timeseries.py
@@ -0,0 +1,74 @@
+from statsmodels.tsa.statespace.sarimax import SARIMAX
+
+from services.ml.base import Model
+
+
+class TimeSeriesModel(Model):
+    """SARIMAX forecaster for the daily average price series.
+
+    Captures trend/weekly seasonality in price itself (order + seasonal_order)
+    while also using same-day demand drivers (occupancy, competitor price,
+    weekend/holiday/event flags, average lead time) as exogenous regressors,
+    since price isn't just autocorrelated noise -- it responds to demand.
+    """
+
+    name = "sarimax"
+
+    def __init__(self, order=(1, 1, 1), seasonal_order=(1, 1, 1, 7), **params):
+        super().__init__(**params)
+        self.order = order
+        self.seasonal_order = seasonal_order
+
+    def fit(self, x, y):
+        """Fits the SARIMAX model on a contiguous daily series.
+
+        Args:
+            x (pd.DataFrame): Exogenous regressors, daily frequency,
+                same index as y.
+            y (pd.Series): Daily average price, daily frequency.
+
+        Returns:
+            TimeSeriesModel: The trained model.
+        """
+        self._model = SARIMAX(
+            y,
+            exog=x,
+            order=self.order,
+            seasonal_order=self.seasonal_order,
+            enforce_stationarity=False,
+            enforce_invertibility=False,
+        ).fit(disp=False, **self.params)
+        return self
+
+    def predict(self, x):
+        """Forecasts the point price for each step in x.
+
+        Args:
+            x (pd.DataFrame): Exogenous regressors for the forecast
+                horizon; must immediately follow the training period.
+
+        Returns:
+            np.ndarray: One forecast per row of x.
+        """
+        forecast = self._fitted().get_forecast(steps=len(x), exog=x)
+        return forecast.predicted_mean.to_numpy()
+
+    def predict_interval(self, x, alpha=0.05):
+        """Forecasts a value and a (1 - alpha) interval for each step in x.
+
+        Args:
+            x (pd.DataFrame): Exogenous regressors for the forecast horizon.
+            alpha (float): Significance level; 0.05 gives a 95% interval.
+
+        Returns:
+            tuple[np.ndarray, np.ndarray, np.ndarray]: Forecasts, lower
+                bounds, and upper bounds, one per row of x.
+
+        Raises:
+            RuntimeError: If fit has not been called yet.
+        """
+        forecast = self._fitted().get_forecast(steps=len(x), exog=x)
+        pred = forecast.predicted_mean.to_numpy()
+        ci = forecast.conf_int(alpha=alpha)
+        lower, upper = ci.iloc[:, 0].to_numpy(), ci.iloc[:, 1].to_numpy()
+        return pred, lower, upper
diff --git a/backend/tests/conftest.py b/backend/tests/conftest.py
index 9102435..a341be7 100644
--- a/backend/tests/conftest.py
+++ b/backend/tests/conftest.py
@@ -1,6 +1,59 @@
-"""Shared pytest fixtures.
+"""Shared fixtures: mock hotel data and train/test splits for the ML models."""
 
-The `foresight` package is installed via `uv sync`, so no sys.path
-manipulation is needed here — tests can import from `foresight.*` directly.
-This file is kept as the conventional location for future shared fixtures.
-"""
+import pandas as pd
+import pytest
+
+from generate_mock_data import write_csv
+
+# Known only after the night, so never used to predict price.
+POST_STAY_COLUMNS = ["rooms_sold", "occupancy", "room_revenue"]
+TEST_FRAC = 0.2
+
+
+@pytest.fixture(scope="session")
+def df():
+    """Mock nightly data, freshly generated to data/, sorted by stay_date."""
+    df = pd.read_csv(write_csv(), parse_dates=["stay_date"])
+    return df.sort_values("stay_date", kind="stable").reset_index(drop=True)
+
+
+@pytest.fixture(scope="session")
+def price_split(df):
+    """x_train, x_test, y_train, y_test for predicting rate.
+
+    The test set is the most recent TEST_FRAC of nights.
+    """
+    x = df.drop(columns=["rate", "stay_date", *POST_STAY_COLUMNS])
+    x = pd.get_dummies(x, columns=["room_type", "condition"], dtype=float)
+    y = df["rate"]
+
+    cut = int(len(df) * (1 - TEST_FRAC))
+    return x.iloc[:cut], x.iloc[cut:], y.iloc[:cut], y.iloc[cut:]
+
+
+# SARIMAX inputs besides past prices; same for every room type on a night.
+TS_EXOG_COLUMNS = [
+    "is_weekend",
+    "temp_max_c",
+    "precip_mm",
+    "expected_attendance",
+    "competitor_rate",
+    "competitors_sold_out",
+    "flight_count",
+    "avg_fare_eur",
+]
+
+
+@pytest.fixture(scope="session")
+def ts_split(df):
+    """x_train, x_test, y_train, y_test for forecasting the nightly mean rate.
+
+    One row per night at daily frequency; the test set is the most recent
+    TEST_FRAC of nights.
+    """
+    # SARIMAX needs one row per day, so average the room types for each night.
+    daily = df.groupby("stay_date")[["rate", *TS_EXOG_COLUMNS]].mean().asfreq("D")
+    x, y = daily[TS_EXOG_COLUMNS], daily["rate"]
+
+    cut = int(len(daily) * (1 - TEST_FRAC))
+    return x.iloc[:cut], x.iloc[cut:], y.iloc[:cut], y.iloc[cut:]
diff --git a/backend/tests/test_ml_models.py b/backend/tests/test_ml_models.py
new file mode 100644
index 0000000..f6bca88
--- /dev/null
+++ b/backend/tests/test_ml_models.py
@@ -0,0 +1,117 @@
+"""Each model predicting room rate on held-out, later nights."""
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from services.ml.base import Model
+from services.ml.bayesian import BayesianModel
+from services.ml.neural_net import NNModel
+from services.ml.regression import GBMModel
+from services.ml.timeseries import TimeSeriesModel
+
+MODELS = [BayesianModel, NNModel, GBMModel, TimeSeriesModel]
+
+
+@pytest.fixture(scope="module", params=MODELS, ids=lambda m: m.name)
+def fitted(request, price_split):
+    """Each model, fit on the price training split."""
+    x_train, _, y_train, _ = price_split
+    return request.param().fit(x_train, y_train)
+
+
+def test_predicts_one_price_per_row(fitted, price_split):
+    """predict returns one positive price per test row."""
+    _, x_test, _, _ = price_split
+    pred = fitted.predict(x_test)
+    assert pred.shape == (len(x_test),)
+    assert (pred > 0).all()
+
+
+@pytest.mark.parametrize("model_cls", MODELS, ids=lambda m: m.name)
+def test_same_seed_gives_same_predictions(model_cls, price_split):
+    """Two fits with the same seed give identical predictions."""
+    x_train, x_test, y_train, _ = price_split
+    first = model_cls(seed=3).fit(x_train, y_train).predict(x_test)
+    second = model_cls(seed=3).fit(x_train, y_train).predict(x_test)
+    np.testing.assert_array_equal(first, second)
+
+
+@pytest.mark.parametrize("model_cls", MODELS, ids=lambda m: m.name)
+def test_predict_before_fit_raises(model_cls, price_split):
+    """predict raises RuntimeError on an unfit model."""
+    _, x_test, _, _ = price_split
+    with pytest.raises(RuntimeError):
+        model_cls().predict(x_test)
+
+
+def test_bayesian_interval(price_split):
+    """Bayesian intervals run, match predict, and contain the prediction."""
+    x_train, x_test, y_train, y_test = price_split
+    model = BayesianModel().fit(x_train, y_train)
+
+    pred, lower, upper = model.predict_interval(x_test)
+    np.testing.assert_array_equal(pred, model.predict(x_test))
+    assert (lower < pred).all() and (pred < upper).all()
+    assert 0.0 <= model.evaluate(x_test, y_test)["coverage"] <= 1.0
+
+
+def test_nn_has_no_interval(price_split):
+    """NN raises NotImplementedError for intervals; coverage is None."""
+    x_train, x_test, y_train, y_test = price_split
+    model = NNModel().fit(x_train, y_train)
+
+    with pytest.raises(NotImplementedError):
+        model.predict_interval(x_test)
+    assert model.evaluate(x_test, y_test)["coverage"] is None
+
+class FixedModel(Model):
+    """Returns preset predictions and a +/-5 band, recording the alpha asked for."""
+
+    name = "fixed"
+
+    def __init__(self, pred):
+        super().__init__()
+        self.pred = np.asarray(pred, dtype=float)
+        self.alpha_seen = "not called"
+
+    def fit(self, x, y):
+        """Does nothing; predictions are preset."""
+        return self
+
+    def predict(self, x):
+        """Returns the preset predictions."""
+        return self.pred
+
+    def predict_interval(self, x, alpha="default"):
+        """Returns the preset predictions with a +/-5 band; records alpha."""
+        self.alpha_seen = alpha
+        return self.pred, self.pred - 5, self.pred + 5
+
+
+def test_evaluate_metrics():
+    """evaluate computes each metric correctly on known values."""
+    # Errors are -10, 10, -10, 0; the 0 true value is left out of MAPE.
+    y = pd.Series([100.0, 200.0, 0.0, 400.0])
+    model = FixedModel([110.0, 190.0, 10.0, 400.0])
+
+    scores = model.evaluate(pd.DataFrame(index=y.index), y)
+
+    assert scores["mae"] == pytest.approx(7.5)
+    assert scores["rmse"] == pytest.approx(np.sqrt(75))
+    assert scores["mape"] == pytest.approx((0.1 + 0.05 + 0.0) / 3)
+    assert scores["r2"] == pytest.approx(1 - 300 / 87500)
+    assert scores["coverage"] == pytest.approx(0.25)  # only the last row is within +/-5
+
+
+def test_evaluate_passes_alpha_only_when_given():
+    """evaluate passes alpha to predict_interval only when one is given."""
+    y = pd.Series([1.0, 2.0])
+    x = pd.DataFrame(index=y.index)
+    model = FixedModel([1.0, 2.0])
+
+    model.evaluate(x, y)
+    assert model.alpha_seen == "default"  # model keeps its own alpha
+
+    model.evaluate(x, y, alpha=0.2)
+    assert model.alpha_seen == 0.2
diff --git a/backend/uv.lock b/backend/uv.lock
index 0f6a8d2..3a7670b 100644
--- a/backend/uv.lock
+++ b/backend/uv.lock
@@ -1,6 +1,17 @@
 version = 1
 revision = 3
 requires-python = ">=3.12"
+resolution-markers = [
+    "python_full_version >= '3.14' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.13.*' and sys_platform == 'win32'",
+    "python_full_version < '3.13' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and sys_platform == 'emscripten'",
+    "python_full_version < '3.13' and sys_platform == 'emscripten'",
+    "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
 
 [[package]]
 name = "alembic"
@@ -114,6 +125,15 @@ wheels = [
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 ]
 
+[[package]]
+name = "cloudpickle"
+version = "3.1.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/27/fb/576f067976d320f5f0114a8d9fa1215425441bb35627b1993e5afd8111e5/cloudpickle-3.1.2.tar.gz", hash = "sha256:7fda9eb655c9c230dab534f1983763de5835249750e85fbcef43aaa30a9a2414", size = 22330, upload-time = "2025-11-03T09:25:26.604Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/88/39/799be3f2f0f38cc727ee3b4f1445fe6d5e4133064ec2e4115069418a5bb6/cloudpickle-3.1.2-py3-none-any.whl", hash = "sha256:9acb47f6afd73f60dc1df93bb801b472f05ff42fa6c84167d25cb206be1fbf4a", size = 22228, upload-time = "2025-11-03T09:25:25.534Z" },
+]
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -123,6 +143,95 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
 ]
 
+[[package]]
+name = "contourpy"
+version = "1.4.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
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