diff --git a/tests/test_detectors.py b/tests/test_detectors.py
index 55dbc1b..a411abf 100644
--- a/tests/test_detectors.py
+++ b/tests/test_detectors.py
@@ -2,8 +2,9 @@
 import pytest
 import numpy as np
 import pandas as pd
+import os
 
-from tsod.custom_exceptions import WrongInputDataType
+from tsod.custom_exceptions import WrongInputDataTypeError
 from tsod.detectors import (
     RangeDetector,
     DiffDetector,
@@ -89,7 +90,7 @@ def test_base_detector_exceptions(range_data, range_data_series):
     data_series, _, _ = range_data_series
 
     detector = RangeDetector()
-    pytest.raises(WrongInputDataType, detector.fit, data)
+    pytest.raises(WrongInputDataTypeError, detector.fit, data)
 
 
 def test_range_detector(range_data_series):
@@ -175,7 +176,9 @@ def test_diff_detector_autoset(range_data_series):
 
 
 def test_combined_detector():
-    df = pd.read_csv("tests/data/example.csv", parse_dates=True, index_col=0)
+    path_to_tests_super_folder = os.path.abspath(__file__).split('tests')[0]
+    df = pd.read_csv(os.path.join(path_to_tests_super_folder, 'tests', 'data', 'example.csv'),
+                     parse_dates=True, index_col=0)
     combined = CombinedDetector(
         [
             ConstantValueDetector(),
@@ -229,7 +232,7 @@ def test_hampel_detector(data_series):
     assert all(i in expected_anomalies_indices for i in anomalies_indices)
 
 
-def test_autoencoder_detector(data_series):
+def test_auto_encoder_detector(data_series):
     data_with_anomalies, expected_anomalies_indices, normal_data = data_series
     detector = AutoEncoder(
         hidden_neurons=[1, 1, 1, 1], epochs=1
@@ -239,15 +242,16 @@ def test_autoencoder_detector(data_series):
     anomalies_indices = np.array(np.where(anomalies)).flatten()
     # Validate if the found anomalies are also in the expected anomaly set
     # NB Not necessarily all of them
-    # assert all(i in expected_anomalies_indices for i in anomalies_indices)
+    assert np.mean(np.array([i in expected_anomalies_indices for i in anomalies_indices])) > 0.4
 
 
-def test_autoencoderlstm_detector(data_series):
+def test_auto_encoder_lstm_detector(data_series):
     data_with_anomalies, expected_anomalies_indices, normal_data = data_series
     detector = AutoEncoderLSTM()
     detector.fit(data_with_anomalies)
     anomalies = detector.detect(data_with_anomalies)
     anomalies_indices = np.array(np.where(anomalies)).flatten()
+    assert np.mean(np.array([i in expected_anomalies_indices for i in anomalies_indices])) > 0.01
 
 
 def test_constant_value_detector(constant_data_series):
@@ -371,7 +375,7 @@ def test_create_dataset(data_series):
     data_with_anomalies.name = "y"
     data = data_with_anomalies.to_frame()
     time_steps = 2
-    X, y = create_dataset(data[["y"]], data.y, time_steps)
+    predictors, y = create_dataset(data[["y"]], data.y, time_steps)
     assert len(y) == len(data) - time_steps
-    assert X.shape[0] == len(data) - time_steps
-    assert X.shape[1] == time_steps
+    assert predictors.shape[0] == len(data) - time_steps
+    assert predictors.shape[1] == time_steps
diff --git a/tests/test_mvdetectors.py b/tests/test_mvdetectors.py
new file mode 100644
index 0000000..1104e1f
--- /dev/null
+++ b/tests/test_mvdetectors.py
@@ -0,0 +1,161 @@
+import pytest
+import pandas as pd
+import numpy as np
+
+from tsod.custom_exceptions import InvalidArgumentError
+from tsod.mvdetectors import MVRangeDetector
+
+
+@pytest.fixture
+def range_data():
+    n_obs = 15
+    normal_data = pd.DataFrame(np.random.uniform(size=[3, n_obs]))
+    normal_data.iloc[2, [2, 8]] = np.nan
+    normal_data.iloc[:, 13] = 1
+    normal_data.iloc[:, 14] = 0
+    abnormal_data = pd.DataFrame(np.random.uniform(size=[3, n_obs]))
+    abnormal_data.iloc[0, [2, 3, 7]] = 5
+    abnormal_data.iloc[1, [2, 12]] = -2
+    abnormal_data.iloc[0, [8]] = np.nan
+    abnormal_data.iloc[2, [8, 9]] = np.nan
+    return normal_data, abnormal_data
+
+
+@pytest.fixture
+def range_data_time_series_specific_ranges():
+    n_obs = 15
+    ts_mins = [-1, -0.5, 0]
+    ts_maxs = [2, 3, 4]
+    normal_data = pd.DataFrame(np.random.uniform(low=ts_mins, high=ts_maxs, size=(n_obs, len(ts_mins))).T)
+    normal_data.iloc[2, [2, 8]] = np.nan
+    normal_data.iloc[:, 13] = ts_mins
+    normal_data.iloc[:, 14] = ts_maxs
+    abnormal_data = pd.DataFrame(np.random.uniform(low=ts_mins, high=ts_maxs, size=(n_obs, len(ts_mins))).T)
+    abnormal_data.iloc[0, [2, 3, 7]] = 5
+    abnormal_data.iloc[1, [2, 12]] = -2
+    abnormal_data.iloc[0, [8]] = np.nan
+    abnormal_data.iloc[2, [8, 9]] = np.nan
+    return normal_data, abnormal_data
+
+
+@pytest.mark.parametrize("detector, expected_anomalies_list", [
+    (MVRangeDetector(min_value=0.0, max_value=1.0),
+     [[False, False, True, True, False, False, False, True, False, False, False, False, False, False, False],
+     [False, False, True, False, False, False, False, False, False, False, False, False, True, False, False],
+     [False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]]),
+    (MVRangeDetector(max_value=1.0),
+     [[False, False, True, True, False, False, False, True, False, False, False, False, False, False, False],
+     [False, False, False, False, False, False, False, False, False, False, False, False, False, False, False],
+     [False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]]),
+    (MVRangeDetector(min_value=0.0),
+     [[False, False, False, False, False, False, False, False, False, False, False, False, False, False, False],
+     [False, False, True, False, False, False, False, False, False, False, False, False, True, False, False],
+     [False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]])
+])
+def test_single_range_detector_detection(range_data, detector, expected_anomalies_list):
+    normal_data, abnormal_data = range_data
+    detected_anomalies = detector.detect(abnormal_data)
+    expected_anomalies = pd.DataFrame(expected_anomalies_list, columns=abnormal_data.columns, index=abnormal_data.index)
+    pd.testing.assert_frame_equal(expected_anomalies, detected_anomalies)
+
+    detected_anomalies = detector.detect(normal_data)
+    assert not detected_anomalies.to_numpy().any()
+
+
+def test_single_range_detector_fitting(range_data):
+    normal_data, abnormal_data = range_data
+    detector = MVRangeDetector()
+    detector.fit(normal_data)
+    detected_anomalies = detector.detect(abnormal_data)
+    expected_anomalies = pd.DataFrame(
+        [[False, False, True, True, False, False, False, True, False, False, False, False, False, False, False],
+         [False, False, True, False, False, False, False, False, False, False, False, False, True, False, False],
+         [False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]],
+        columns=abnormal_data.columns, index=abnormal_data.index)
+    pd.testing.assert_frame_equal(expected_anomalies, detected_anomalies)
+
+    detected_anomalies = detector.detect(normal_data)
+    assert not detected_anomalies.to_numpy().any()
+
+
+@pytest.mark.parametrize("detector, expected_anomalies_list", [
+    (MVRangeDetector(min_value=[0.0, 0.0, 0.0], max_value=1.0),
+     [[False, False, True, True, False, False, False, True, False, False, False, False, False, False, False],
+     [False, False, True, False, False, False, False, False, False, False, False, False, True, False, False],
+     [False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]]),
+    (MVRangeDetector(min_value=0.0, max_value=[1.0, 1.0, 1.0]),
+     [[False, False, True, True, False, False, False, True, False, False, False, False, False, False, False],
+     [False, False, True, False, False, False, False, False, False, False, False, False, True, False, False],
+     [False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]]),
+    (MVRangeDetector(min_value=[0.0, 0.0, 0.0], max_value=[1.0, 1.0, 1.0]),
+     [[False, False, True, True, False, False, False, True, False, False, False, False, False, False, False],
+     [False, False, True, False, False, False, False, False, False, False, False, False, True, False, False],
+     [False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]])
+])
+def test_multi_range_detector_detection(range_data, detector, expected_anomalies_list):
+    normal_data, abnormal_data = range_data
+    detected_anomalies = detector.detect(abnormal_data)
+    expected_anomalies = pd.DataFrame(expected_anomalies_list, columns=abnormal_data.columns, index=abnormal_data.index)
+    pd.testing.assert_frame_equal(expected_anomalies, detected_anomalies)
+
+    detected_anomalies = detector.detect(normal_data)
+
+    assert not detected_anomalies.to_numpy().any()
+
+
+@pytest.mark.parametrize("detector, expected_anomalies_list", [
+    (MVRangeDetector(min_value=[-1, -0.5, 0], max_value=[2, 3, 4]),
+     [[False, False, True, True, False, False, False, True, False, False, False, False, False, False, False],
+     [False, False, True, False, False, False, False, False, False, False, False, False, True, False, False],
+     [False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]]),
+    (MVRangeDetector(max_value=[2, 3, 4]),
+     [[False, False, True, True, False, False, False, True, False, False, False, False, False, False, False],
+     [False, False, False, False, False, False, False, False, False, False, False, False, False, False, False],
+     [False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]]),
+    (MVRangeDetector(min_value=[-1, -0.5, 0]),
+     [[False, False, False, False, False, False, False, False, False, False, False, False, False, False, False],
+     [False, False, True, False, False, False, False, False, False, False, False, False, True, False, False],
+     [False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]])
+])
+def test_multiple_ranges_detector_detection(range_data_time_series_specific_ranges, detector, expected_anomalies_list):
+    normal_data, abnormal_data = range_data_time_series_specific_ranges
+    detected_anomalies = detector.detect(abnormal_data)
+    expected_anomalies = pd.DataFrame(expected_anomalies_list, columns=abnormal_data.columns, index=abnormal_data.index)
+    pd.testing.assert_frame_equal(expected_anomalies, detected_anomalies)
+
+    detected_anomalies = detector.detect(normal_data)
+    assert not detected_anomalies.to_numpy().any()
+
+
+def test_multiple_ranges_detector_fitting(range_data_time_series_specific_ranges):
+    normal_data, abnormal_data = range_data_time_series_specific_ranges
+    detector = MVRangeDetector()
+    detector.fit(normal_data)
+    detected_anomalies = detector.detect(abnormal_data)
+    expected_anomalies = pd.DataFrame(
+        [[False, False, True, True, False, False, False, True, False, False, False, False, False, False, False],
+         [False, False, True, False, False, False, False, False, False, False, False, False, True, False, False],
+         [False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]],
+        columns=abnormal_data.columns, index=abnormal_data.index)
+    pd.testing.assert_frame_equal(expected_anomalies, detected_anomalies)
+
+    detected_anomalies = detector.detect(normal_data)
+    assert not detected_anomalies.to_numpy().any()
+
+
+@pytest.mark.parametrize("min_value, max_value",
+                         [
+                             (3, 2), ([0, 0, 3], 2), ([[0], [0], [0]], 1), (-1, [[0], [0], [0]])
+                         ])
+def test_invalid_argument_raised_min_max(min_value, max_value):
+    with pytest.raises(InvalidArgumentError):
+        MVRangeDetector(min_value=min_value, max_value=max_value)
+
+
+@pytest.mark.parametrize("quantile_prob_cut_offs",
+                         [
+                             ([0.5, 1.1]), ([-0.5, 1.1]), ([-0.5, 0.9])
+                         ])
+def test_invalid_argument_raised_quantiles(quantile_prob_cut_offs):
+    with pytest.raises(InvalidArgumentError):
+        MVRangeDetector(quantiles=quantile_prob_cut_offs)
diff --git a/tests/test_persistence.py b/tests/test_persistence.py
index 83e75cc..d27e189 100644
--- a/tests/test_persistence.py
+++ b/tests/test_persistence.py
@@ -21,8 +21,8 @@ def test_save_and_load(tmp_path):
 
 
 def test_load():
-
-    filename = os.path.join("tests", "data", "combined.joblib")
+    path_to_tests_super_folder = os.path.abspath(__file__).split('tests')[0]
+    filename = os.path.join(path_to_tests_super_folder, "tests", "data", "combined.joblib")
 
     loaded = tsod.load(filename)
 
@@ -43,4 +43,4 @@ def test_save_and_load_filename(tmpdir):
 
     loaded = tsod.load(filename)
 
-    assert isinstance(loaded, CombinedDetector)
\ No newline at end of file
+    assert isinstance(loaded, CombinedDetector)
diff --git a/tsod/base.py b/tsod/base.py
index 98c45e3..f706bd1 100644
--- a/tsod/base.py
+++ b/tsod/base.py
@@ -5,10 +5,9 @@
 import joblib
 
 import pandas as pd
-import numpy as np
 
 
-from .custom_exceptions import WrongInputDataType
+from .custom_exceptions import WrongInputDataTypeError
 
 
 def load(path: Union[str, Path]):
@@ -29,7 +28,7 @@ class Detector(ABC):
     def __init__(self):
         pass
 
-    def fit(self, data: pd.Series):
+    def fit(self, data: Union[pd.Series, pd.DataFrame]):
         """Set detector parameters based on data.
 
         Parameters
@@ -41,11 +40,11 @@ def fit(self, data: pd.Series):
         self._fit(data)
         return self
 
-    def _fit(self, data: pd.Series):
+    def _fit(self, data: Union[pd.Series, pd.DataFrame]):
         # Default implementation is a NoOp
         return self
 
-    def detect(self, data: pd.Series) -> pd.Series:
+    def detect(self, data: Union[pd.Series, pd.DataFrame]) -> Union[pd.Series, pd.DataFrame]:
         """Detect anomalies
 
         Parameters
@@ -63,25 +62,25 @@ def detect(self, data: pd.Series) -> pd.Series:
         pred = self._detect(data)
         return self._postprocess(pred)
 
-    def _postprocess(self, pred: pd.Series) -> pd.Series:
+    def _postprocess(self, pred: Union[pd.Series, pd.DataFrame]) -> pd.Series:
         # TODO implement
         return pred
 
     @abstractmethod
-    def _detect(self, data: pd.Series) -> pd.Series:
-        "Detect anomalies"
-        NotImplementedError()
+    def _detect(self, data: Union[pd.Series, pd.DataFrame]) -> pd.Series:
+        """Detect anomalies"""
+        pass
 
-    def validate(self, data: pd.Series) -> pd.Series:
+    def validate(self, data: Union[pd.Series, pd.DataFrame]) -> Union[pd.Series, pd.DataFrame]:
         """Check that input data is in correct format and possibly adjust"""
-        if not isinstance(data, pd.Series):
-            raise WrongInputDataType()
+        if not (isinstance(data, pd.Series) or isinstance(data, pd.DataFrame)):
+            raise WrongInputDataTypeError()
         return data
 
-    def _gradient(self, data: pd.Series, periods: int = 1) -> pd.Series:
+    def _gradient(self, data: Union[pd.Series, pd.DataFrame], periods: int = 1) -> pd.Series:
         dt = data.index.to_series().diff().dt.total_seconds()
         if dt.min() < 1e-15:
-            raise ValueError("Input must be monotonic increasing")
+            raise ValueError("Index must be monotonically increasing")
 
         gradient = data.diff(periods=periods) / dt
         return gradient
diff --git a/tsod/custom_exceptions.py b/tsod/custom_exceptions.py
index 61b3d08..3cfb4bd 100644
--- a/tsod/custom_exceptions.py
+++ b/tsod/custom_exceptions.py
@@ -1,4 +1,4 @@
-class WrongInputDataType(Exception):
+class WrongInputDataTypeError(Exception):
     def __init__(self, message="Input data must be a pandas.Series."):
         self.message = message
         super().__init__(self.message)
@@ -15,18 +15,22 @@ def __init__(self, message="Or specify min/max range when instantiating detector
         super().__init__(message)
 
 
-class InvalidArgument(Exception):
+class InvalidArgumentError(Exception):
     def __init__(self, argument_name, requirement):
         self.message = f"{argument_name} must be {requirement}."
         super().__init__(self.message)
 
 
-class NotInteger(InvalidArgument):
+class NotIntegerError(InvalidArgumentError):
     def __init__(self, argument_name):
         super().__init__(argument_name, "an integer")
 
 
-class NonUniqueTimeStamps(Exception):
+class NonUniqueTimeStampsError(Exception):
     def __init__(self, message="Found multiple values at the same time stamp."):
         self.message = message
         super().__init__(self.message)
+
+
+class WrongInputSizeError(ValueError):
+    pass
diff --git a/tsod/detectors.py b/tsod/detectors.py
index 564b398..750bcd3 100644
--- a/tsod/detectors.py
+++ b/tsod/detectors.py
@@ -14,6 +14,10 @@ class CombinedDetector(Detector, Sequence):
 
     Examples
     --------
+    >>> normal_data = pd.Series(np.random.normal(size=100))
+    >>> abnormal_data = pd.Series(np.random.normal(size=100))
+    >>> abnormal_data[[2, 6, 15, 57, 60, 73]] = 5
+
     >>> anomaly_detector = CombinedDetector([RangeDetector(), DiffDetector()])
     >>> anomaly_detector.fit(normal_data)
     >>> detected_anomalies = anomaly_detector.detect(abnormal_data)
@@ -66,16 +70,22 @@ class RangeDetector(Detector):
 
     Examples
     ---------
+    >>> normal_data = pd.Series(np.random.normal(size=100))
+    >>> abnormal_data = pd.Series(np.random.normal(size=100))
+    >>> abnormal_data[[2, 6, 15, 57, 60, 73]] = 5
+    >>> normal_data_with_some_outliers = pd.Series(np.random.normal(size=100))
+    >>> normal_data_with_some_outliers[[12, 13, 20, 90]] = 7
+
     >>> detector = RangeDetector(min_value=0.0, max_value=2.0)
-    >>> anomalies = detector.detect(data)
+    >>> anomalies = detector.detect(abnormal_data)
 
     >>> detector = RangeDetector()
     >>> detector.fit(normal_data) # min, max inferred from normal data
-    >>> anomalies = detector.detect(data)
+    >>> anomalies = detector.detect(abnormal_data)
 
     >>> detector = RangeDetector(quantiles=[0.001,0.999])
     >>> detector.fit(normal_data_with_some_outliers)
-    >>> anomalies = detector.detect(data)"""
+    >>> anomalies = detector.detect(abnormal_data)"""
 
     def __init__(self, min_value=-np.inf, max_value=np.inf, quantiles=None):
         super().__init__()
@@ -101,7 +111,7 @@ def _fit(self, data):
         """
         super().validate(data)
 
-        quantiles = np.quantile(data.dropna(), self._quantiles)
+        quantiles = np.nanquantile(data, self._quantiles)
         self._min = quantiles.min()
         self._max = quantiles.max()
 
@@ -109,7 +119,7 @@ def _fit(self, data):
         return self
 
     def _detect(self, data: pd.Series) -> pd.Series:
-        "Detect anomalies outside range"
+        """Detect anomalies outside range"""
 
         if self._max is None:
             return data < self._min
diff --git a/tsod/hampel.py b/tsod/hampel.py
index 2a0272b..1b6abed 100644
--- a/tsod/hampel.py
+++ b/tsod/hampel.py
@@ -2,7 +2,7 @@
 import numpy as np
 from numba import jit
 
-from tsod.custom_exceptions import NotInteger, InvalidArgument
+from tsod.custom_exceptions import NotIntegerError, InvalidArgumentError
 from tsod.detectors import Detector
 
 
@@ -14,13 +14,13 @@
 
 def _validate_arguments(window_size, threshold):
     if not isinstance(window_size, int):
-        raise NotInteger("window_size")
+        raise NotIntegerError("window_size")
     else:
         if window_size <= 0:
-            raise InvalidArgument("window_size", "nonnegative")
+            raise InvalidArgumentError("window_size", "nonnegative")
 
     if threshold < 0:
-        raise InvalidArgument("threshold", "positive")
+        raise InvalidArgumentError("threshold", "positive")
 
 
 @jit(nopython=True)
diff --git a/tsod/mvdetectors.py b/tsod/mvdetectors.py
new file mode 100644
index 0000000..65235ec
--- /dev/null
+++ b/tsod/mvdetectors.py
@@ -0,0 +1,127 @@
+import pandas as pd
+import numpy as np
+import typing
+
+from .base import Detector
+from .custom_exceptions import NoRangeDefinedError, WrongInputSizeError, InvalidArgumentError
+
+
+def make_vector_broadcastable(function_input, n_data_rows):
+    if function_input is not None:
+        if len(function_input.shape) > 0:
+            if len(function_input) != n_data_rows:
+                raise WrongInputSizeError(
+                    "The number of rows in the input data must match the number of "
+                    "values specified for min and max if more than one value is given for min/max.")
+    min_comparison = function_input
+    if len(function_input.shape) == 1:
+        min_comparison = function_input[..., np.newaxis]
+    return min_comparison
+
+
+class MVRangeDetector(Detector):
+    """
+    Detect values outside range.
+
+    NaN values are not marked as anomalies.
+
+    Parameters
+    ----------
+    min_value : float, List, np.array
+        Minimum value threshold.
+    max_value : float, List, np.array
+        Maximum value threshold.
+    quantiles : list[2]
+                Default quantiles [0, 1]. Same as min and max value.
+
+    Examples
+    ---------
+    >>> n_obs = 100
+    >>> normal_data = pd.DataFrame(np.random.normal(size=[3, n_obs]))
+    >>> abnormal_data = pd.DataFrame(np.random.normal(size=[3, n_obs]))
+    >>> abnormal_data.iloc[0, [2, 6, 15, 57, 60, 73]] = 5
+    >>> normal_data_with_some_outliers = pd.DataFrame(np.random.normal(size=[3, n_obs]))
+    >>> normal_data_with_some_outliers.iloc[0, [12, 13, 20, 90]] = 7
+
+    >>> detector = MVRangeDetector(min_value=0.0, max_value=2.0)
+    >>> anomalies = detector.detect(abnormal_data)
+
+    >>> detector = MVRangeDetector()
+    >>> detector.fit(normal_data) # min, max inferred from normal data
+    >>> anomalies = detector.detect(abnormal_data)
+
+    >>> detector = MVRangeDetector(quantiles=[0.001,0.999])
+    >>> detector.fit(normal_data_with_some_outliers)
+    >>> anomalies = detector.detect(normal_data_with_some_outliers)"""
+
+    def __init__(self, min_value=-np.inf, max_value=np.inf, quantiles=None):
+        super().__init__()
+
+        min_value = np.array(min_value)
+        if len(min_value.shape) > 1:
+            raise InvalidArgumentError('min_value ', ' a float or 1D array_like.')
+
+        max_value = np.array(max_value)
+        if len(max_value.shape) > 1:
+            raise InvalidArgumentError('max_value ', ' a float or 1D array_like.')
+
+        if np.array([min_value > max_value]).any():
+            raise InvalidArgumentError('For all values in min_value and max_value ', ' the min must be less than max.')
+
+        self._min = min_value
+
+        self._max = max_value
+
+        if quantiles is None:
+            self.quantiles = [0.0, 1.0]
+        else:
+            if not (0.0 <= quantiles[0] <= 1.0):
+                raise InvalidArgumentError('Values in quantile_prob_cut_offs', ' between 0 and 1, both inclusive.')
+            if not (0.0 <= quantiles[1] <= 1.0):
+                raise InvalidArgumentError('Values in quantile_prob_cut_offs', ' between 0 and 1, both inclusive.')
+            self.quantiles = [np.min(quantiles), np.max(quantiles)]
+
+    def _fit(self, data):
+        """Set min and max based on data.
+
+        Parameters
+        ----------
+        data :  pd.DataFrame
+                Time series data with time over columns.
+        """
+        super().validate(data)
+
+        values_at_quantiles = np.nanquantile(data, self.quantiles, axis=1)
+        self._min = values_at_quantiles[0]
+        self._max = values_at_quantiles[1]
+
+        return self
+
+    def _detect(self, data: typing.Union[pd.Series, pd.DataFrame]) -> pd.DataFrame:
+        """Detect anomalies outside range"""
+
+        if (self._min is None) and (self._max is None):
+            raise NoRangeDefinedError("Both min and max are None. At least one of them must be set.")
+
+        if len(data.shape) == 1:
+            n_data_rows = 1
+        else:
+            n_data_rows = data.shape[0]
+
+        min_comparison = make_vector_broadcastable(self._min, n_data_rows)
+        max_comparison = make_vector_broadcastable(self._max, n_data_rows)
+
+        if self._max is None:
+            return data < min_comparison
+
+        if self._min is None:
+            return data > max_comparison
+
+        return (data < min_comparison) | (data > max_comparison)
+
+    def __str__(self):
+
+        return f"{super.__str__(self)}{self._min}, {self._max})"
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}(min: {self._min:.1e}, max: {self._max:.1e})"