diff --git a/poetry.lock b/poetry.lock
index 69d64312..531c0d6b 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -68,25 +68,26 @@ resolved_reference = "a6c5184c21470d1c734eeb332696146ee0f1452f"
 
 [[package]]
 name = "anyio"
-version = "3.6.2"
+version = "3.7.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 category = "dev"
 optional = false
-python-versions = ">=3.6.2"
+python-versions = ">=3.7"
 files = [
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+    {file = "anyio-3.7.0-py3-none-any.whl", hash = "sha256:eddca883c4175f14df8aedce21054bfca3adb70ffe76a9f607aef9d7fa2ea7f0"},
+    {file = "anyio-3.7.0.tar.gz", hash = "sha256:275d9973793619a5374e1c89a4f4ad3f4b0a5510a2b5b939444bee8f4c4d37ce"},
 ]
 
 [package.dependencies]
+exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
 idna = ">=2.8"
 sniffio = ">=1.1"
 typing-extensions = {version = "*", markers = "python_version < \"3.8\""}
 
 [package.extras]
-doc = ["packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["contextlib2", "coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "mock (>=4)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (<0.15)", "uvloop (>=0.15)"]
-trio = ["trio (>=0.16,<0.22)"]
+doc = ["Sphinx (>=6.1.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme", "sphinxcontrib-jquery"]
+test = ["anyio[trio]", "coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "mock (>=4)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (<0.22)"]
 
 [[package]]
 name = "appnope"
@@ -324,26 +325,26 @@ files = [
 
 [[package]]
 name = "cachetools"
-version = "5.3.0"
+version = "5.3.1"
 description = "Extensible memoizing collections and decorators"
 category = "dev"
 optional = false
-python-versions = "~=3.7"
+python-versions = ">=3.7"
 files = [
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 ]
 
 [[package]]
 name = "certifi"
-version = "2022.12.7"
+version = "2023.5.7"
 description = "Python package for providing Mozilla's CA Bundle."
 category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
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 ]
 
 [[package]]
@@ -550,63 +551,72 @@ files = [
 
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+version = "7.2.7"
 description = "Code coverage measurement for Python"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
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 ]
 
+[[package]]
+name = "exceptiongroup"
+version = "1.1.1"
+description = "Backport of PEP 654 (exception groups)"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+files = [
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+
+[package.extras]
+test = ["pytest (>=6)"]
+
 [[package]]
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-version = "2.16.3"
+version = "2.17.1"
 description = "Fastest Python implementation of JSON schema"
 category = "dev"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.extras]
@@ -842,21 +867,22 @@ files = [
 
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+version = "2.19.1"
 description = "Google Authentication Library"
 category = "dev"
 optional = false
-python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*"
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-rsa = {version = ">=3.1.4,<5", markers = "python_version >= \"3.6\""}
+rsa = ">=3.1.4,<5"
 six = ">=1.9.0"
+urllib3 = "<2.0"
 
 [package.extras]
 aiohttp = ["aiohttp (>=3.6.2,<4.0.0dev)", "requests (>=2.20.0,<3.0.0dev)"]
@@ -886,14 +912,14 @@ tool = ["click (>=6.0.0)"]
 
 [[package]]
 name = "gspread"
-version = "5.8.0"
+version = "5.9.0"
 description = "Google Spreadsheets Python API"
 category = "dev"
 optional = false
 python-versions = ">=3.6, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
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 ]
 
 [package.dependencies]
@@ -1148,14 +1174,14 @@ files = [
 
 [[package]]
 name = "json5"
-version = "0.9.11"
+version = "0.9.14"
 description = "A Python implementation of the JSON5 data format."
 category = "dev"
 optional = false
 python-versions = "*"
 files = [
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 [package.extras]
@@ -1369,14 +1395,14 @@ test = ["pre-commit", "pytest", "pytest-asyncio", "websockets (>=10.0)", "ypy-we
 
 [[package]]
 name = "jupyterlab"
-version = "3.6.3"
+version = "3.6.4"
 description = "JupyterLab computational environment"
 category = "dev"
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 python-versions = ">=3.7"
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 [package.dependencies]
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 jupyter-server = ">=1.16.0,<3"
 jupyter-server-ydoc = ">=0.8.0,<0.9.0"
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+jupyter-ydoc = ">=0.2.4,<0.3.0"
 jupyterlab-server = ">=2.19,<3.0"
 nbclassic = "*"
 notebook = "<7"
@@ -1560,62 +1586,62 @@ testing = ["coverage", "pyyaml"]
 
 [[package]]
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 optional = false
 python-versions = ">=3.7"
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 [[package]]
@@ -1832,14 +1858,14 @@ test = ["flaky", "ipykernel", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "p
 
 [[package]]
 name = "nbconvert"
-version = "7.3.1"
+version = "7.4.0"
 description = "Converting Jupyter Notebooks"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -2400,21 +2426,21 @@ files = [
 
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 python-versions = ">=3.7"
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 [package.extras]
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 [[package]]
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 [[package]]
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 files = [
-    {file = "typing_extensions-4.5.0-py3-none-any.whl", hash = "sha256:fb33085c39dd998ac16d1431ebc293a8b3eedd00fd4a32de0ff79002c19511b4"},
-    {file = "typing_extensions-4.5.0.tar.gz", hash = "sha256:5cb5f4a79139d699607b3ef622a1dedafa84e115ab0024e0d9c044a9479ca7cb"},
+    {file = "typing_extensions-4.6.3-py3-none-any.whl", hash = "sha256:88a4153d8505aabbb4e13aacb7c486c2b4a33ca3b3f807914a9b4c844c471c26"},
+    {file = "typing_extensions-4.6.3.tar.gz", hash = "sha256:d91d5919357fe7f681a9f2b5b4cb2a5f1ef0a1e9f59c4d8ff0d3491e05c0ffd5"},
 ]
 
 [[package]]
@@ -3806,14 +3861,14 @@ dev = ["flake8 (<4.0.0)", "flake8-annotations", "flake8-bugbear", "flake8-commas
 
 [[package]]
 name = "urllib3"
-version = "2.0.2"
+version = "1.26.16"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 category = "dev"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
 files = [
-    {file = "urllib3-2.0.2-py3-none-any.whl", hash = "sha256:d055c2f9d38dc53c808f6fdc8eab7360b6fdbbde02340ed25cfbcd817c62469e"},
-    {file = "urllib3-2.0.2.tar.gz", hash = "sha256:61717a1095d7e155cdb737ac7bb2f4324a858a1e2e6466f6d03ff630ca68d3cc"},
+    {file = "urllib3-1.26.16-py2.py3-none-any.whl", hash = "sha256:8d36afa7616d8ab714608411b4a3b13e58f463aee519024578e062e141dce20f"},
+    {file = "urllib3-1.26.16.tar.gz", hash = "sha256:8f135f6502756bde6b2a9b28989df5fbe87c9970cecaa69041edcce7f0589b14"},
 ]
 
 [package.extras]
@@ -3864,14 +3919,14 @@ files = [
 
 [[package]]
 name = "websocket-client"
-version = "1.5.1"
+version = "1.5.2"
 description = "WebSocket client for Python with low level API options"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "websocket-client-1.5.1.tar.gz", hash = "sha256:3f09e6d8230892547132177f575a4e3e73cfdf06526e20cc02aa1c3b47184d40"},
-    {file = "websocket_client-1.5.1-py3-none-any.whl", hash = "sha256:cdf5877568b7e83aa7cf2244ab56a3213de587bbe0ce9d8b9600fc77b455d89e"},
+    {file = "websocket-client-1.5.2.tar.gz", hash = "sha256:c7d67c13b928645f259d9b847ab5b57fd2d127213ca41ebd880de1f553b7c23b"},
+    {file = "websocket_client-1.5.2-py3-none-any.whl", hash = "sha256:f8c64e28cd700e7ba1f04350d66422b6833b82a796b525a51e740b8cc8dab4b1"},
 ]
 
 [package.extras]
@@ -4009,4 +4064,4 @@ testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.7.1,<3.12"  # When deploying set this to 3.7
-content-hash = "494abacce212d8ad465388a7be9c9a0a798fecfd13cb5245ddf108c7ea157efc"
+content-hash = "8c79c9148f612667a3e2d74d0ca5ee45db9d6b80dc8aaa743a39aa77d348deb8"
diff --git a/pyproject.toml b/pyproject.toml
index 025639b6..a6a89526 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -45,6 +45,10 @@ pycatch22 = "^0.4.2"
 # antropy = "^0.1.5"
 antropy = { git = "https://github.com/raphaelvallat/antropy.git", branch = "master" }  # replace with pip when > 0.1.5 release available
 nolds = "^0.5.2"
+pyentrp = [
+    { version = "^0.7.1", python = "<3.8" },
+    { version = "^0.8.2", python = ">=3.8" }
+]
 # Linting
 ruff = "^0.0.264"
 black = "^22.12.0"
diff --git a/tests/test_features_feature_collection.py b/tests/test_features_feature_collection.py
index e04ce198..900adc9b 100644
--- a/tests/test_features_feature_collection.py
+++ b/tests/test_features_feature_collection.py
@@ -1730,7 +1730,7 @@ def sum_mean(x, axis):
     s = "EDA__"
     p = "__w=1000"
     assert np.all(res[s + "sum" + p].values == res[s + "sum_vect" + p].values)
-    assert np.all(res[s + "mean" + p].values == res[s + "mean_vect" + p].values)
+    assert np.allclose(res[s + "mean" + p].values, res[s + "mean_vect" + p].values)
 
 
 def test_multiple_inputs_vectorized_features(dummy_data):
@@ -1759,6 +1759,137 @@ def windowed_diff(x1, x2):
     assert np.all(res["EDA|TMP__windowed_diff" + p].values == manual_diff)
 
 
+### Test parallel features
+
+
+def test_basic_parallel_features_different_fc(dummy_data):
+    fs = 4  # The sample frequency in Hz
+    fc1 = FeatureCollection(
+        feature_descriptors=[
+            FeatureDescriptor(np.max, "EDA", 250 * fs, 75 * fs),
+        ]
+    )
+    fc2 = FeatureCollection(
+        feature_descriptors=[
+            FeatureDescriptor(
+                FuncWrapper(np.max, output_names="max_", parallel=True),
+                "EDA",
+                250 * fs,
+                75 * fs,
+            ),
+        ]
+    )
+    res1 = fc1.calculate(dummy_data)
+    res2 = fc2.calculate(dummy_data)
+
+    assert len(res1) == 1
+    assert len(res2) == 1
+    res1 = res1[0]
+    res2 = res2[0]
+    assert (len(res1) > 1) and (len(res2) > 1)
+    assert np.all(res1.index == res2.index)
+    assert np.all(res1.values == res2.values)
+
+
+def test_basic_parallel_features_same_fc(dummy_data):
+    fs = 4  # The sample frequency in Hz
+    fc = FeatureCollection(
+        feature_descriptors=[
+            FeatureDescriptor(np.max, "EDA", 250 * fs, 75 * fs),
+            FeatureDescriptor(
+                FuncWrapper(np.max, output_names="max_", parallel=True),
+                "EDA",
+                250 * fs,
+                75 * fs,
+            ),
+        ]
+    )
+    res = fc.calculate(dummy_data)
+
+    assert len(res) == 2
+    assert (len(res[0]) > 1) and (len(res[1]) > 1)
+    assert np.all(res[0].index == res[1].index)
+    assert np.all(res[0].values == res[1].values)
+
+
+def test_time_based_parallel_features(dummy_data):
+    fc = FeatureCollection(
+        feature_descriptors=[
+            FeatureDescriptor(np.max, "EDA", "5min", "3min"),
+            FeatureDescriptor(
+                FuncWrapper(np.max, output_names="max_", parallel=True),
+                "EDA",
+                "5min",
+                "3min",
+            ),
+        ]
+    )
+    res = fc.calculate(dummy_data)
+
+    assert len(res) == 2
+    assert (len(res[0]) > 1) and (len(res[1]) > 1)
+    assert np.all(res[0].index == res[1].index)
+    assert np.all(res[0].values == res[1].values)
+
+
+def test_multiple_outputs_parallel_features(dummy_data):
+    def sum_mean(x):
+        s = np.sum(x)
+        return s, s / x.shape[0]
+
+    fs = 4  # The sample frequency in Hz
+    fc = FeatureCollection(
+        feature_descriptors=[
+            FeatureDescriptor(np.sum, "EDA", 250 * fs, 75 * fs),
+            FeatureDescriptor(np.mean, "EDA", 250 * fs, 75 * fs),
+            FeatureDescriptor(
+                FuncWrapper(
+                    sum_mean,
+                    output_names=["sum_par", "mean_par"],
+                    parallel=True,
+                ),
+                "EDA",
+                250 * fs,
+                75 * fs,
+            ),
+        ]
+    )
+
+    res = fc.calculate(dummy_data, return_df=True)
+
+    assert res.shape[1] == 4
+    s = "EDA__"
+    p = "__w=1000"
+    assert np.all(res[s + "sum" + p].values == res[s + "sum_par" + p].values)
+    assert np.allclose(res[s + "mean" + p].values, res[s + "mean_par" + p].values)
+
+
+def test_multiple_inputs_parallel_features(dummy_data):
+    def windowed_diff(x1, x2):
+        return np.sum(x1) - np.sum(x2)
+
+    fc = FeatureCollection(
+        feature_descriptors=[
+            FeatureDescriptor(np.sum, "EDA", "5min", "2.5min"),
+            FeatureDescriptor(np.sum, "TMP", "5min", "2.5min"),
+            FeatureDescriptor(
+                FuncWrapper(windowed_diff, parallel=True),
+                ("EDA", "TMP"),
+                "5min",
+                "2.5min",
+            ),
+        ]
+    )
+
+    res = fc.calculate(dummy_data, return_df=True)
+
+    assert res.shape[1] == 3
+    assert res.shape[0] > 1
+    p = "__w=5m"
+    manual_diff = res["EDA__sum" + p].values - res["TMP__sum" + p].values
+    assert np.all(res["EDA|TMP__windowed_diff" + p].values == manual_diff)
+
+
 ### Test feature extraction length
 
 
diff --git a/tsflex/features/feature_collection.py b/tsflex/features/feature_collection.py
index b4ed870a..73739447 100644
--- a/tsflex/features/feature_collection.py
+++ b/tsflex/features/feature_collection.py
@@ -104,6 +104,31 @@ def get_required_series(self) -> List[str]:
             set(flatten([fr_key[0] for fr_key in self._feature_desc_dict.keys()]))
         )
 
+    def _get_feature_desc_dict(
+        self, parallel: bool
+    ) -> Dict[
+        Tuple[Tuple[str, ...], Union[float, pd.Timedelta]], List[FeatureDescriptor]
+    ]:
+        """Return the feature descriptor dictionary.
+
+        Parameters
+        ----------
+        parallel : bool
+            Whether to return the feature descriptors for the parallel functions or not.
+
+        Returns
+        -------
+        Dict[Tuple[Tuple[str, ...], Union[float, pd.Timedelta]], List[FeatureDescriptor]]
+            The feature descriptor dictionary.
+
+        """
+        res = {}
+        for k, fd_list in self._feature_desc_dict.items():
+            fd_list = [fd for fd in fd_list if fd.function.parallel == parallel]
+            if fd_list:
+                res[k] = fd_list
+        return res
+
     def get_nb_output_features(self) -> int:
         """Return the number of output features in this feature collection.
 
@@ -260,11 +285,23 @@ def add(
         self._check_feature_descriptors(skip_none=True)
 
     @staticmethod
-    def _executor(idx: int):
-        # global get_stroll_func
-        stroll, function = get_stroll_func(idx)
+    def _non_parallel_func_executor(idx: int):
+        """Execute functions that are not parallelized (over the strided window).
+
+        This executor can be called in a multiprocessing context.
+        """
+        stroll, function = get_stroll_non_parallel_func(idx)
         return stroll.apply_func(function)
 
+    @staticmethod
+    def _parallel_func_executor(idx: int, n_jobs: int):
+        """Execute functions that are parallelized (over the strided window).
+
+        This executor canNOT be called in a multiprocessing context.
+        """
+        stroll, function = get_stroll_parallel_func(idx)
+        return stroll.apply_func(function, n_jobs)
+
     # def _get_stroll(self, kwargs):
     #     return StridedRollingFactory.get_segmenter(**kwargs)
 
@@ -279,20 +316,21 @@ def _stroll_feat_generator(
         window_idx: str,
         include_final_window: bool,
         approve_sparsity: bool,
+        parallel: bool,
     ) -> Callable[[int], Tuple[StridedRolling, FuncWrapper]]:
         # --- Future work ---
         # We could also make the StridedRolling creation multithreaded
         # Very low priority because the STROLL __init__ is rather efficient!
-        keys_wins_strides = list(self._feature_desc_dict.keys())
-        lengths = np.cumsum(
-            [len(self._feature_desc_dict[k]) for k in keys_wins_strides]
-        )
+        feature_desc_dict = self._get_feature_desc_dict(parallel)
+
+        keys_wins_strides = list(feature_desc_dict)
+        lengths = np.cumsum([len(feature_desc_dict[k]) for k in keys_wins_strides])
 
         def get_stroll_function(idx) -> Tuple[StridedRolling, FuncWrapper]:
             key_idx = np.searchsorted(lengths, idx, "right")  # right bc idx starts at 0
             key, win = keys_wins_strides[key_idx]
 
-            feature = self._feature_desc_dict[keys_wins_strides[key_idx]][
+            feature = feature_desc_dict[keys_wins_strides[key_idx]][
                 idx - lengths[key_idx]
             ]
             stride = feature.stride if calc_stride is None else calc_stride
@@ -316,10 +354,9 @@ def get_stroll_function(idx) -> Tuple[StridedRolling, FuncWrapper]:
 
         return get_stroll_function
 
-    def _get_stroll_feat_length(self) -> int:
-        return sum(
-            len(self._feature_desc_dict[k]) for k in self._feature_desc_dict.keys()
-        )
+    def _get_stroll_feat_length(self, parallel: bool) -> int:
+        feat_desc_dict = self._get_feature_desc_dict(parallel)
+        return sum(len(feat_desc_dict[k]) for k in feat_desc_dict.keys())
 
     def _check_no_multiple_windows(self):
         assert (
@@ -599,11 +636,18 @@ def calculate(
             for n, s, in series_dict.items()
         }
 
+        if (
+            os.name == "nt"
+        ):  # On Windows no multiprocessing is supported, see https://github.com/predict-idlab/tsflex/issues/51
+            n_jobs = 1
+        elif n_jobs is None:
+            n_jobs = os.cpu_count()
+
         # Note: this variable has a global scope so this is shared in multiprocessing
         # TODO: try to make this more efficient (but is not really the bottleneck)
-        global get_stroll_func
-        get_stroll_func = self._stroll_feat_generator(
-            series_dict,
+        global get_stroll_non_parallel_func  # is not applied in parallel on the stroll
+        global get_stroll_parallel_func  # is applied in parallel on the stroll
+        kwargs = dict(
             calc_stride=stride,
             segment_start_idxs=segment_start_idxs,
             segment_end_idxs=segment_end_idxs,
@@ -613,30 +657,42 @@ def calculate(
             include_final_window=include_final_window,
             approve_sparsity=approve_sparsity,
         )
-        nb_stroll_funcs = self._get_stroll_feat_length()
-
-        if (
-            os.name == "nt"
-        ):  # On Windows no multiprocessing is supported, see https://github.com/predict-idlab/tsflex/issues/51
-            n_jobs = 1
-        elif n_jobs is None:
-            n_jobs = os.cpu_count()
-        n_jobs = min(n_jobs, nb_stroll_funcs)
+        get_stroll_non_parallel_func = self._stroll_feat_generator(
+            series_dict,
+            **kwargs,
+            parallel=False,
+        )
+        nb_stroll_non_parallel_func = self._get_stroll_feat_length(parallel=False)
+        n_jobs_non_parallel_func = min(n_jobs, nb_stroll_non_parallel_func)
+        get_stroll_parallel_func = self._stroll_feat_generator(
+            series_dict,
+            **kwargs,
+            parallel=True,
+        )
+        nb_stroll_parallel_func = self._get_stroll_feat_length(parallel=True)
 
         calculated_feature_list = None
-        if n_jobs in [0, 1]:
-            idxs = range(nb_stroll_funcs)
+
+        # 1. Calculate the features for the non-parallel functions
+        # -> these functions are calculated (possibly in parallel) over the functions
+        if n_jobs_non_parallel_func in [0, 1]:
+            idxs = range(nb_stroll_non_parallel_func)
             if show_progress:
                 idxs = tqdm(idxs)
             try:
-                calculated_feature_list = [self._executor(idx) for idx in idxs]
+                # 1. Calculate the features for the non-parallel functions
+                calculated_feature_list = [
+                    self._non_parallel_func_executor(i) for i in idxs
+                ]
             except Exception:
                 traceback.print_exc()
         else:
-            with Pool(processes=n_jobs) as pool:
-                results = pool.imap_unordered(self._executor, range(nb_stroll_funcs))
+            with Pool(processes=n_jobs_non_parallel_func) as pool:
+                results = pool.imap_unordered(
+                    self._non_parallel_func_executor, range(nb_stroll_non_parallel_func)
+                )
                 if show_progress:
-                    results = tqdm(results, total=nb_stroll_funcs)
+                    results = tqdm(results, total=nb_stroll_non_parallel_func)
                 try:
                     calculated_feature_list = [f for f in results]
                 except Exception:
@@ -647,6 +703,21 @@ def calculate(
                     pool.close()
                     pool.join()
 
+        # 2. Calculate the features for the parallel functions
+        # -> these functions are calculated (possibly in parallel) within the functions
+        idxs = range(nb_stroll_parallel_func)
+        if show_progress:
+            idxs = tqdm(idxs)
+        try:
+            # We collect one by one the results of the parallel functions
+            # (because the parallelization is done within the function , i.e. over the
+            # strided windows)
+            calculated_feature_list += [
+                self._parallel_func_executor(i, n_jobs) for i in idxs
+            ]
+        except Exception:
+            traceback.print_exc()
+
         # Close the file handler (this avoids PermissionError: [WinError 32])
         if logging_file_path:
             f_handler.close()
diff --git a/tsflex/features/function_wrapper.py b/tsflex/features/function_wrapper.py
index f400a5cb..dd027c41 100644
--- a/tsflex/features/function_wrapper.py
+++ b/tsflex/features/function_wrapper.py
@@ -66,6 +66,7 @@ def __init__(
         output_names: Optional[Union[List[str], str]] = None,
         input_type: Optional[Union[np.array, pd.Series]] = np.array,
         vectorized: bool = False,
+        parallel: bool = False,
         **kwargs,
     ):
         """Create FuncWrapper instance."""
@@ -83,10 +84,14 @@ def __init__(
 
         assert input_type in SUPPORTED_STROLL_TYPES, "Invalid input_type!"
         assert not (
-            vectorized & (input_type is not np.array)
+            vectorized and (input_type is not np.array)
         ), "The input_type must be np.array if vectorized is True!"
+        assert not (
+            vectorized and parallel
+        ), "vectorized and parallel cannot be both True!"
         self.input_type = input_type
         self.vectorized = vectorized
+        self.parallel = parallel
 
         self._freeze()
 
diff --git a/tsflex/features/segmenter/strided_rolling.py b/tsflex/features/segmenter/strided_rolling.py
index 3f55d8f6..3c4b93d6 100644
--- a/tsflex/features/segmenter/strided_rolling.py
+++ b/tsflex/features/segmenter/strided_rolling.py
@@ -14,6 +14,7 @@
 __author__ = "Jonas Van Der Donckt, Jeroen Van Der Donckt"
 
 import time
+import traceback
 import warnings
 from abc import ABC, abstractmethod
 from collections import namedtuple
@@ -21,6 +22,7 @@
 
 import numpy as np
 import pandas as pd
+from multiprocess import Pool
 
 from ...utils.attribute_parsing import AttributeParser, DataType
 from ...utils.data import SUPPORTED_STROLL_TYPES, to_list, to_series_list, to_tuple
@@ -325,13 +327,18 @@ def _construct_series_containers(
             )
         return series_containers
 
-    def apply_func(self, func: FuncWrapper) -> pd.DataFrame:
+    def apply_func(
+        self, func: FuncWrapper, n_jobs: Optional[int] = None
+    ) -> pd.DataFrame:
         """Apply a function to the segmented series.
 
         Parameters
         ----------
         func : FuncWrapper
             The Callable wrapped function which will be applied.
+        n_jobs : Optional[int], optional
+            The number of processes to use for parallel FuncWrapper execution, by
+            default None.
 
         Returns
         -------
@@ -444,6 +451,36 @@ def apply_func(self, func: FuncWrapper) -> pd.DataFrame:
             # when combining into an array
             out = out.T if out_type is tuple else out
 
+        elif func.parallel and n_jobs > 1:
+            # Parallel function execution
+            with Pool(processes=n_jobs) as pool:
+                try:
+                    out = np.array(
+                        list(
+                            pool.starmap(
+                                func,
+                                [
+                                    [
+                                        sc.values[
+                                            sc.start_indexes[idx] : sc.end_indexes[idx]
+                                        ]
+                                        for sc in self.series_containers
+                                    ]
+                                    for idx in range(len(self.index))
+                                ],
+                                # TODO -> make this configurable
+                                # chunksize=100,
+                            )
+                        )
+                    )
+                except Exception:
+                    traceback.print_exc()
+                    pool.terminate()
+                finally:
+                    # Close & join because: https://github.com/uqfoundation/pathos/issues/131
+                    pool.close()
+                    pool.join()
+
         else:
             # Sequential function execution (default)
             out = np.array(
@@ -682,9 +719,11 @@ def __init__(
         # we want to assure that the window-stride arguments are integers (samples)
         assert all(isinstance(p, int) for p in [self.window] + self.strides)
 
-    def apply_func(self, func: FuncWrapper) -> pd.DataFrame:
+    def apply_func(
+        self, func: FuncWrapper, n_jobs: Optional[int] = None
+    ) -> pd.DataFrame:
         # Apply the function and stitch back the time-index
-        df = super().apply_func(func)
+        df = super().apply_func(func, n_jobs)
         df.index = self._series_index[df.index]
         return df
 
diff --git a/tsflex/features/utils.py b/tsflex/features/utils.py
index 769c8f8e..43ca3201 100644
--- a/tsflex/features/utils.py
+++ b/tsflex/features/utils.py
@@ -121,6 +121,7 @@ def _get_funcwrapper_func_and_kwargs(func: FuncWrapper) -> Tuple[Callable, dict]
     func_wrapper_kwargs["output_names"] = func.output_names
     func_wrapper_kwargs["input_type"] = func.input_type
     func_wrapper_kwargs["vectorized"] = func.vectorized
+    func_wrapper_kwargs["parallel"] = func.parallel
     func_wrapper_kwargs.update(func.kwargs)
 
     return function, func_wrapper_kwargs