diff --git a/README.rst b/README.rst
index 0be4e10c..e6cdfa30 100644
--- a/README.rst
+++ b/README.rst
@@ -244,6 +244,23 @@ This can be used to retrieve the schedule, using:
 The client will re-try until the schedule is available or the ``MAX_POLLING_STEPS`` of ``10`` is reached.
 
 
+Forecasting
+===========
+
+Trigger a forecast for a sensor and wait for the result:
+
+.. code-block:: python
+
+    forecast = await client.trigger_and_get_forecast(
+        sensor_id=<sensor_id>,  # int
+        duration="PT24H",  # ISO duration – how far ahead to forecast
+    )
+    # Returns e.g. {"values": [1.2, 1.5, ...], "start": "...", "duration": "PT24H", "unit": "kW"}
+
+The client polls until the forecasting job is complete.  For more advanced options
+(training window, regressors, forecast frequency, etc.) see :doc:`forecasting`.
+
+
 Development
 ==============
 
diff --git a/docs/forecasting.rst b/docs/forecasting.rst
new file mode 100644
index 00000000..5bc3dacd
--- /dev/null
+++ b/docs/forecasting.rst
@@ -0,0 +1,147 @@
+.. _forecasting:
+
+Forecasting
+===========
+
+The FlexMeasures Client supports the forecasting API endpoints introduced in
+FlexMeasures v0.31.0:
+
+- ``POST /sensors/<id>/forecasts/trigger`` — queue a forecasting job
+- ``GET  /sensors/<id>/forecasts/<uuid>``  — poll for results
+
+These are exposed through three client methods:
+
+- :meth:`trigger_forecast` — trigger and return the job UUID
+- :meth:`get_forecast`     — poll until results are ready
+- :meth:`trigger_and_get_forecast` — convenience wrapper for both
+
+.. note::
+
+    These endpoints require a FlexMeasures server of version **0.31.0** or above.
+
+
+Basic example
+-------------
+
+Forecast the next 24 hours for a sensor, using server-side defaults for the
+training window:
+
+.. code-block:: python
+
+    import asyncio
+    from flexmeasures_client import FlexMeasuresClient
+
+    async def main():
+        client = FlexMeasuresClient(
+            host="localhost:5000",
+            ssl=False,
+            email="user@example.com",
+            password="password",
+        )
+
+        forecast = await client.trigger_and_get_forecast(
+            sensor_id=1,
+            duration="PT24H",
+        )
+        print(forecast)
+        # e.g. {"values": [1.2, 1.5, 1.8, ...], "start": "...", "duration": "PT24H", "unit": "kW"}
+
+        await client.close()
+
+    asyncio.run(main())
+
+
+Specifying a forecast window
+-----------------------------
+
+Use ``start`` and ``end`` (or ``start`` and ``duration``) to define the exact
+period to forecast:
+
+.. code-block:: python
+
+    forecast = await client.trigger_and_get_forecast(
+        sensor_id=1,
+        start="2025-01-15T00:00:00+01:00",
+        end="2025-01-17T00:00:00+01:00",
+    )
+
+
+Controlling the training window
+---------------------------------
+
+Pass training parameters inside a nested structure via the ``train_start``,
+``train_period``, and ``retrain_frequency`` keyword arguments:
+
+.. code-block:: python
+
+    forecast = await client.trigger_and_get_forecast(
+        sensor_id=1,
+        start="2025-01-15T00:00:00+01:00",
+        duration="PT48H",
+        # Training configuration
+        train_start="2025-01-01T00:00:00+01:00",  # historical data start
+        train_period="P14D",                        # use 14 days of history
+        retrain_frequency="PT24H",                  # retrain every 24 h
+    )
+
+
+Using regressors
+----------------
+
+You can improve forecast accuracy by supplying regressor sensor IDs:
+
+.. code-block:: python
+
+    forecast = await client.trigger_and_get_forecast(
+        sensor_id=1,
+        duration="PT24H",
+        # Sensors whose *forecasts* matter (e.g. weather forecasts)
+        future_regressors=[10, 11],
+        # Sensors whose *measurements* matter (e.g. price history)
+        past_regressors=[20],
+    )
+
+
+Step-by-step usage
+-------------------
+
+Trigger and retrieve separately to handle the job UUID yourself:
+
+.. code-block:: python
+
+    # Step 1 – enqueue the forecasting job
+    forecast_id = await client.trigger_forecast(
+        sensor_id=1,
+        start="2025-01-15T00:00:00+01:00",
+        end="2025-01-17T00:00:00+01:00",
+    )
+    print(f"Job queued: {forecast_id}")
+
+    # Step 2 – poll until the job finishes
+    forecast = await client.get_forecast(
+        sensor_id=1,
+        forecast_id=forecast_id,
+    )
+    print(forecast)
+
+
+Polling behaviour
+-----------------
+
+``get_forecast`` polls the server with a ``GET`` request and returns when the
+server responds with HTTP 200.  The polling respects the same client-level
+settings as scheduling:
+
+- ``polling_interval`` (default 10 s) — time between retries
+- ``polling_timeout`` (default 200 s) — maximum total wait time
+- ``max_polling_steps`` (default 10)  — maximum number of poll attempts
+
+Override them at client construction time:
+
+.. code-block:: python
+
+    client = FlexMeasuresClient(
+        ...,
+        polling_interval=5.0,   # check every 5 seconds
+        polling_timeout=300.0,  # wait up to 5 minutes
+    )
diff --git a/docs/index.rst b/docs/index.rst
index 980910dd..05174429 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -30,6 +30,7 @@ Contents
    :maxdepth: 2
 
    Overview <readme>
+   Forecasting <forecasting>
    Contributions & Help <contributing>
    License <license>
    Authors <authors>
diff --git a/src/flexmeasures_client/client.py b/src/flexmeasures_client/client.py
index c28ca854..adb0282b 100644
--- a/src/flexmeasures_client/client.py
+++ b/src/flexmeasures_client/client.py
@@ -1265,6 +1265,210 @@ async def trigger_schedule(
         self.logger.info(f"Schedule triggered successfully. Schedule ID: {schedule_id}")
         return schedule_id
 
+    async def trigger_forecast(
+        self,
+        sensor_id: int,
+        start: str | datetime | None = None,
+        end: str | datetime | None = None,
+        duration: str | timedelta | None = None,
+        train_start: str | datetime | None = None,
+        train_period: str | timedelta | None = None,
+        max_training_period: str | timedelta | None = None,
+        retrain_frequency: str | timedelta | None = None,
+        future_regressors: list[int] | None = None,
+        past_regressors: list[int] | None = None,
+        regressors: list[int] | None = None,
+        max_forecast_horizon: str | timedelta | None = None,
+        forecast_frequency: str | timedelta | None = None,
+        probabilistic: bool | None = None,
+    ) -> str:
+        """Trigger a forecasting job for the given sensor.
+
+        :param sensor_id: ID of the sensor to forecast.
+        :param start: Start date for predictions (ISO 8601 datetime string or datetime).
+        :param end: End date of the forecast period (ISO 8601 datetime string or datetime).
+        :param duration: Duration of the forecast period (ISO 8601 duration or timedelta).
+                         Provide at most two of start, end, and duration.
+        :param train_start: Start date of historical training data.
+        :param train_period: Duration of the training period (min 2 days).
+        :param max_training_period: Maximum duration of the training period. Defaults to 1 year.
+        :param retrain_frequency: How often to retrain (ISO 8601 duration or timedelta).
+        :param future_regressors: List of sensor IDs used only as future regressors.
+        :param past_regressors: List of sensor IDs used only as past regressors.
+        :param regressors: List of sensor IDs used as both past and future regressors.
+        :param max_forecast_horizon: Maximum forecast horizon (ISO 8601 duration or timedelta).
+        :param forecast_frequency: How often to recompute forecasts (ISO 8601 duration or timedelta).
+        :param probabilistic: If True, enable probabilistic forecasting.
+
+        :returns: Forecast job UUID (string).
+
+        This function raises a ValueError when an unhandled status code is returned.
+        """
+        json_payload: dict[str, Any] = {}
+
+        if start is not None:
+            json_payload["start"] = pd.Timestamp(start).isoformat()
+        if end is not None:
+            json_payload["end"] = pd.Timestamp(end).isoformat()
+        if duration is not None:
+            json_payload["duration"] = pd.Timedelta(duration).isoformat()
+        if max_forecast_horizon is not None:
+            json_payload["max-forecast-horizon"] = pd.Timedelta(
+                max_forecast_horizon
+            ).isoformat()
+        if forecast_frequency is not None:
+            json_payload["forecast-frequency"] = pd.Timedelta(
+                forecast_frequency
+            ).isoformat()
+        if probabilistic is not None:
+            json_payload["probabilistic"] = probabilistic
+
+        # Build config sub-dict for training and regressor parameters
+        config: dict[str, Any] = {}
+        if train_start is not None:
+            config["train-start"] = pd.Timestamp(train_start).isoformat()
+        if train_period is not None:
+            config["train-period"] = pd.Timedelta(train_period).isoformat()
+        if max_training_period is not None:
+            config["max-training-period"] = pd.Timedelta(
+                max_training_period
+            ).isoformat()
+        if retrain_frequency is not None:
+            config["retrain-frequency"] = pd.Timedelta(retrain_frequency).isoformat()
+        if future_regressors is not None:
+            config["future-regressors"] = future_regressors
+        if past_regressors is not None:
+            config["past-regressors"] = past_regressors
+        if regressors is not None:
+            config["regressors"] = regressors
+        if config:
+            json_payload["config"] = config
+
+        response, status = await self.request(
+            uri=f"sensors/{sensor_id}/forecasts/trigger",
+            json_payload=json_payload,
+            method="POST",
+            minimum_server_version="0.31.0",
+        )
+        check_for_status(status, 200)
+
+        if not isinstance(response, dict):
+            raise ContentTypeError(
+                f"Expected a dictionary, but got {type(response)}",
+            )
+
+        if not isinstance(response.get("forecast"), str):
+            raise ContentTypeError(
+                f"Expected a forecast ID, but got {type(response.get('forecast'))}",
+            )
+        forecast_id = response["forecast"]
+        self.logger.info(f"Forecast triggered successfully. Forecast ID: {forecast_id}")
+        return forecast_id
+
+    async def get_forecast(
+        self,
+        sensor_id: int,
+        forecast_id: str,
+    ) -> dict:
+        """Get forecast with given ID.
+
+        Polls the server until the forecasting job is complete and returns the result.
+
+        :returns: forecast as dictionary, for example:
+                {
+                    'values': [1.2, 1.5, 1.4, 0.8],
+                    'start': '2025-10-15T00:00:00+01:00',
+                    'duration': 'PT4H',
+                    'unit': 'kW'
+                }
+
+        This function raises a ValueError when an unhandled status code is returned.
+        """
+        polling_step = 0
+        try:
+            async with async_timeout.timeout(self.polling_timeout):
+                while polling_step < self.max_polling_steps:
+                    forecast, status = await self.request(
+                        uri=f"sensors/{sensor_id}/forecasts/{forecast_id}",
+                        method="GET",
+                        minimum_server_version="0.31.0",
+                    )
+                    if status == 200:
+                        if not isinstance(forecast, dict):
+                            raise ContentTypeError(
+                                f"Expected a forecast dictionary, but got {type(forecast)}",
+                            )
+                        return forecast
+                    elif status == 202:
+                        job_status = (
+                            forecast.get("status", "unknown")
+                            if isinstance(forecast, dict)
+                            else "unknown"
+                        )
+                        message = f"Forecast job status: {job_status}. Polling step: {polling_step}. Retrying in {self.polling_interval} seconds..."
+                        self.logger.debug(message)
+                        polling_step += 1
+                        await asyncio.sleep(self.polling_interval)
+                    else:
+                        check_for_status(status, 200)
+        except asyncio.TimeoutError as exception:
+            raise ConnectionError(
+                "Client polling timeout while waiting for forecast job to complete."
+            ) from exception
+        raise ConnectionError(
+            "Max polling steps reached while waiting for forecast job to complete."
+        )
+
+    async def trigger_and_get_forecast(
+        self,
+        sensor_id: int,
+        start: str | datetime | None = None,
+        end: str | datetime | None = None,
+        duration: str | timedelta | None = None,
+        train_start: str | datetime | None = None,
+        train_period: str | timedelta | None = None,
+        max_training_period: str | timedelta | None = None,
+        retrain_frequency: str | timedelta | None = None,
+        future_regressors: list[int] | None = None,
+        past_regressors: list[int] | None = None,
+        regressors: list[int] | None = None,
+        max_forecast_horizon: str | timedelta | None = None,
+        forecast_frequency: str | timedelta | None = None,
+        probabilistic: bool | None = None,
+    ) -> dict:
+        """Trigger a forecasting job and then fetch the result.
+
+        :returns: forecast as dictionary, for example:
+                {
+                    'values': [1.2, 1.5, 1.4, 0.8],
+                    'start': '2025-10-15T00:00:00+01:00',
+                    'duration': 'PT4H',
+                    'unit': 'kW'
+                }
+
+        This function raises a ValueError when an unhandled status code is returned.
+        """
+        forecast_id = await self.trigger_forecast(
+            sensor_id=sensor_id,
+            start=start,
+            end=end,
+            duration=duration,
+            train_start=train_start,
+            train_period=train_period,
+            max_training_period=max_training_period,
+            retrain_frequency=retrain_frequency,
+            future_regressors=future_regressors,
+            past_regressors=past_regressors,
+            regressors=regressors,
+            max_forecast_horizon=max_forecast_horizon,
+            forecast_frequency=forecast_frequency,
+            probabilistic=probabilistic,
+        )
+        return await self.get_forecast(
+            sensor_id=sensor_id,
+            forecast_id=forecast_id,
+        )
+
     @staticmethod
     def create_storage_flex_model(
         soc_unit: str,
diff --git a/tests/test_client.py b/tests/test_client.py
index adcea5e8..8fdf292f 100644
--- a/tests/test_client.py
+++ b/tests/test_client.py
@@ -1016,3 +1016,192 @@ async def test_trigger_schedule_with_custom_scheduler() -> None:
         )
 
         await flexmeasures_client.close()
+
+
+@pytest.mark.asyncio
+async def test_trigger_forecast() -> None:
+    """Test triggering a forecast with basic parameters."""
+    with aioresponses() as m:
+        flexmeasures_client = FlexMeasuresClient(
+            email="test@test.test", password="test"
+        )
+        flexmeasures_client.access_token = "test-token"
+
+        sensor_id = 1
+        m.post(
+            f"http://localhost:5000/api/v3_0/sensors/{sensor_id}/forecasts/trigger",
+            status=200,
+            payload={"forecast": "test-forecast-uuid"},
+        )
+
+        forecast_id = await flexmeasures_client.trigger_forecast(
+            sensor_id=sensor_id,
+            start="2025-01-05T00:00:00+00:00",
+            end="2025-01-07T00:00:00+00:00",
+        )
+
+        assert forecast_id == "test-forecast-uuid"
+
+        m.assert_called_once_with(
+            f"http://localhost:5000/api/v3_0/sensors/{sensor_id}/forecasts/trigger",
+            method="POST",
+            headers={"Content-Type": "application/json", "Authorization": "test-token"},
+            json={
+                "start": "2025-01-05T00:00:00+00:00",
+                "end": "2025-01-07T00:00:00+00:00",
+            },
+            params=None,
+            ssl=False,
+            allow_redirects=False,
+        )
+
+        await flexmeasures_client.close()
+
+
+@pytest.mark.asyncio
+async def test_trigger_forecast_with_config() -> None:
+    """Test triggering a forecast with training config parameters."""
+    with aioresponses() as m:
+        flexmeasures_client = FlexMeasuresClient(
+            email="test@test.test", password="test"
+        )
+        flexmeasures_client.access_token = "test-token"
+
+        sensor_id = 1
+        m.post(
+            f"http://localhost:5000/api/v3_0/sensors/{sensor_id}/forecasts/trigger",
+            status=200,
+            payload={"forecast": "test-forecast-uuid"},
+        )
+
+        forecast_id = await flexmeasures_client.trigger_forecast(
+            sensor_id=sensor_id,
+            start="2025-01-05T00:00:00+00:00",
+            end="2025-01-07T00:00:00+00:00",
+            train_start="2025-01-01T00:00:00+00:00",
+            retrain_frequency="PT24H",
+            future_regressors=[2, 3],
+        )
+
+        assert forecast_id == "test-forecast-uuid"
+
+        m.assert_called_once_with(
+            f"http://localhost:5000/api/v3_0/sensors/{sensor_id}/forecasts/trigger",
+            method="POST",
+            headers={"Content-Type": "application/json", "Authorization": "test-token"},
+            json={
+                "start": "2025-01-05T00:00:00+00:00",
+                "end": "2025-01-07T00:00:00+00:00",
+                "config": {
+                    "train-start": "2025-01-01T00:00:00+00:00",
+                    "retrain-frequency": "P1DT0H0M0S",
+                    "future-regressors": [2, 3],
+                },
+            },
+            params=None,
+            ssl=False,
+            allow_redirects=False,
+        )
+
+        await flexmeasures_client.close()
+
+
+@pytest.mark.asyncio
+async def test_get_forecast_polling() -> None:
+    """Test getting a forecast with polling (202 -> 202 -> 200)."""
+    sensor_id = 1
+    forecast_id = "test-uuid"
+    url = f"http://localhost:5000/api/v3_0/sensors/{sensor_id}/forecasts/{forecast_id}"
+
+    with aioresponses() as m:
+        # First call returns 202 (QUEUED)
+        m.get(
+            url=url,
+            status=202,
+            payload={"status": "QUEUED"},
+        )
+        # Second call returns 202 (STARTED)
+        m.get(
+            url=url,
+            status=202,
+            payload={"status": "STARTED"},
+        )
+        # Third call returns 200 (completed)
+        m.get(
+            url=url,
+            status=200,
+            payload={
+                "values": [1.2, 1.5],
+                "start": "2025-01-05T00:00:00+00:00",
+                "duration": "PT2H",
+                "unit": "kW",
+            },
+        )
+
+        flexmeasures_client = FlexMeasuresClient(
+            email="test@test.test",
+            password="test",
+            request_timeout=2,
+            polling_interval=0.2,
+            access_token="skip-auth",
+        )
+
+        forecast = await flexmeasures_client.get_forecast(
+            sensor_id=sensor_id, forecast_id=forecast_id
+        )
+
+        assert forecast["values"] == [1.2, 1.5]
+        assert forecast["start"] == "2025-01-05T00:00:00+00:00"
+        assert forecast["duration"] == "PT2H"
+        assert forecast["unit"] == "kW"
+
+        await flexmeasures_client.close()
+
+
+@pytest.mark.asyncio
+async def test_trigger_and_get_forecast() -> None:
+    """Test triggering and getting a forecast in one call."""
+    with aioresponses() as m:
+        flexmeasures_client = FlexMeasuresClient(
+            email="test@test.test",
+            password="test",
+            request_timeout=2,
+            polling_interval=0.2,
+        )
+        flexmeasures_client.access_token = "test-token"
+
+        sensor_id = 1
+        forecast_uuid = "test-forecast-uuid"
+
+        # Mock the trigger request
+        m.post(
+            f"http://localhost:5000/api/v3_0/sensors/{sensor_id}/forecasts/trigger",
+            status=200,
+            payload={"forecast": forecast_uuid},
+        )
+
+        # Mock the get forecast request (with immediate 200 response)
+        m.get(
+            f"http://localhost:5000/api/v3_0/sensors/{sensor_id}/forecasts/{forecast_uuid}",
+            status=200,
+            payload={
+                "values": [1.2, 1.5, 1.8],
+                "start": "2025-01-05T00:00:00+00:00",
+                "duration": "PT3H",
+                "unit": "kW",
+            },
+        )
+
+        forecast = await flexmeasures_client.trigger_and_get_forecast(
+            sensor_id=sensor_id,
+            start="2025-01-05T00:00:00+00:00",
+            end="2025-01-07T00:00:00+00:00",
+        )
+
+        assert "values" in forecast
+        assert forecast["values"] == [1.2, 1.5, 1.8]
+        assert forecast["start"] == "2025-01-05T00:00:00+00:00"
+        assert forecast["duration"] == "PT3H"
+        assert forecast["unit"] == "kW"
+
+        await flexmeasures_client.close()