diff --git a/src/gluonts/core/serde/_base.py b/src/gluonts/core/serde/_base.py
index 9efee3aab8..97de04d65d 100644
--- a/src/gluonts/core/serde/_base.py
+++ b/src/gluonts/core/serde/_base.py
@@ -287,6 +287,15 @@ def encode_partial(v: partial) -> Any:
     }
 
 
+decode_disallow = [
+    eval,
+    exec,
+    compile,
+    open,
+    input,
+]
+
+
 def decode(r: Any) -> Any:
     """
     Decodes a value from an intermediate representation `r`.
@@ -312,7 +321,10 @@ def decode(r: Any) -> Any:
         kind = r["__kind__"]
         cls = cast(Any, locate(r["class"]))
 
-        assert cls is not None, f"Can not locate {r['class']}."
+        if cls is None:
+            raise ValueError(f"Cannot locate {r['class']}.")
+        if cls in decode_disallow:
+            raise ValueError(f"{r['class']} cannot be run.")
 
         if kind == Kind.Type:
             return cls
diff --git a/src/gluonts/dataset/artificial/_base.py b/src/gluonts/dataset/artificial/_base.py
index ca7e6532a9..847e740d11 100644
--- a/src/gluonts/dataset/artificial/_base.py
+++ b/src/gluonts/dataset/artificial/_base.py
@@ -112,7 +112,7 @@ def __init__(
         self,
         num_timeseries: int = 10,
         num_steps: int = 30,
-        freq: str = "1H",
+        freq: str = "1h",
         start: str = "2000-01-01 00:00:00",
         # Generates constant dataset of 0s with explicit NaN missing values
         is_nan: bool = False,
diff --git a/src/gluonts/mx/model/deepstate/_estimator.py b/src/gluonts/mx/model/deepstate/_estimator.py
index 525f2d3cf1..1ba7c95d5a 100644
--- a/src/gluonts/mx/model/deepstate/_estimator.py
+++ b/src/gluonts/mx/model/deepstate/_estimator.py
@@ -69,11 +69,14 @@
 # series in the dataset.
 FREQ_LONGEST_PERIOD_DICT = {
     "M": 12,  # yearly seasonality
+    "ME": 12,  # yearly seasonality
     "W": 52,  # yearly seasonality
     "D": 31,  # monthly seasonality
     "B": 22,  # monthly seasonality
     "H": 168,  # weekly seasonality
+    "h": 168,  # weekly seasonality
     "T": 1440,  # daily seasonality
+    "min": 1440,  # daily seasonality
 }
 
 
diff --git a/src/gluonts/mx/model/deepstate/issm.py b/src/gluonts/mx/model/deepstate/issm.py
index 1078959978..28ae8edd5c 100644
--- a/src/gluonts/mx/model/deepstate/issm.py
+++ b/src/gluonts/mx/model/deepstate/issm.py
@@ -305,7 +305,7 @@ def get_from_freq(cls, freq: str, add_trend: bool = DEFAULT_ADD_TREND):
 
         seasonal_issms: List[SeasonalityISSM] = []
 
-        if offset.name == "M":
+        if offset.name in ["M", "ME"]:
             seasonal_issms = [MonthOfYearSeasonalISSM()]
         elif norm_freq_str(offset.name) == "W":
             seasonal_issms = [WeekOfYearSeasonalISSM()]
@@ -313,12 +313,12 @@ def get_from_freq(cls, freq: str, add_trend: bool = DEFAULT_ADD_TREND):
             seasonal_issms = [DayOfWeekSeasonalISSM()]
         elif offset.name == "B":  # TODO: check this case
             seasonal_issms = [DayOfWeekSeasonalISSM()]
-        elif offset.name == "H":
+        elif offset.name in ["H", "h"]:
             seasonal_issms = [
                 HourOfDaySeasonalISSM(),
                 DayOfWeekSeasonalISSM(),
             ]
-        elif offset.name == "T":
+        elif offset.name in ["T", "min"]:
             seasonal_issms = [
                 MinuteOfHourSeasonalISSM(),
                 HourOfDaySeasonalISSM(),
diff --git a/src/gluonts/mx/model/deepvar/_estimator.py b/src/gluonts/mx/model/deepvar/_estimator.py
index 0a1828369d..ceea7b588c 100644
--- a/src/gluonts/mx/model/deepvar/_estimator.py
+++ b/src/gluonts/mx/model/deepvar/_estimator.py
@@ -91,10 +91,12 @@ def __call__(self, index: pd.PeriodIndex) -> np.ndarray:
 def time_features_from_frequency_str(freq_str: str) -> List[TimeFeature]:
     features = {
         "M": ["weekofyear"],
+        "ME": ["weekofyear"],
         "W": ["daysinmonth", "weekofyear"],
         "D": ["dayofweek"],
         "B": ["dayofweek", "dayofyear"],
         "H": ["hour", "dayofweek"],
+        "h": ["hour", "dayofweek"],
         "min": ["minute", "hour", "dayofweek"],
         "T": ["minute", "hour", "dayofweek"],
     }
@@ -114,13 +116,13 @@ def get_lags_for_frequency(
 ) -> List[int]:
     offset = to_offset(freq_str)
 
-    if offset.name == "M":
+    if offset.name in ["M", "ME"]:
         lags = [[1, 12]]
     elif offset.name == "D":
         lags = [[1, 7, 14]]
     elif offset.name == "B":
         lags = [[1, 2]]
-    elif offset.name == "H":
+    elif offset.name in ["H", "h"]:
         lags = [[1, 24, 168]]
     elif offset.name in ("min", "T"):
         lags = [[1, 4, 12, 24, 48]]
diff --git a/src/gluonts/mx/model/wavenet/_estimator.py b/src/gluonts/mx/model/wavenet/_estimator.py
index 28fca9d2cc..a34d18007a 100644
--- a/src/gluonts/mx/model/wavenet/_estimator.py
+++ b/src/gluonts/mx/model/wavenet/_estimator.py
@@ -226,9 +226,11 @@ def __init__(
                 self.freq,
                 {
                     "H": 7 * 24,
+                    "h": 7 * 24,
                     "D": 7,
                     "W": 52,
                     "M": 12,
+                    "ME": 12,
                     "B": 7 * 5,
                     "min": 24 * 60,
                 },
diff --git a/src/gluonts/nursery/robust-mts-attack/pts/feature/fourier_date_feature.py b/src/gluonts/nursery/robust-mts-attack/pts/feature/fourier_date_feature.py
index 1d7556b04a..562151da83 100644
--- a/src/gluonts/nursery/robust-mts-attack/pts/feature/fourier_date_feature.py
+++ b/src/gluonts/nursery/robust-mts-attack/pts/feature/fourier_date_feature.py
@@ -54,10 +54,12 @@ def fourier_time_features_from_frequency(freq_str: str) -> List[TimeFeature]:
 
     features = {
         "M": ["weekofyear"],
+        "ME": ["weekofyear"],
         "W": ["daysinmonth", "weekofyear"],
         "D": ["dayofweek"],
         "B": ["dayofweek", "dayofyear"],
         "H": ["hour", "dayofweek"],
+        "h": ["hour", "dayofweek"],
         "min": ["minute", "hour", "dayofweek"],
         "T": ["minute", "hour", "dayofweek"],
     }
diff --git a/src/gluonts/nursery/robust-mts-attack/pts/feature/lags.py b/src/gluonts/nursery/robust-mts-attack/pts/feature/lags.py
index 8fe434df6d..cb203d2718 100644
--- a/src/gluonts/nursery/robust-mts-attack/pts/feature/lags.py
+++ b/src/gluonts/nursery/robust-mts-attack/pts/feature/lags.py
@@ -22,13 +22,13 @@ def lags_for_fourier_time_features_from_frequency(
     offset = to_offset(freq_str)
     multiple, granularity = offset.n, offset.name
 
-    if granularity == "M":
+    if granularity in ("M", "ME"):
         lags = [[1, 12]]
     elif granularity == "D":
         lags = [[1, 7, 14]]
     elif granularity == "B":
         lags = [[1, 2]]
-    elif granularity == "H":
+    elif granularity in ("H", "h"):
         lags = [[1, 24, 168]]
     elif granularity in ("T", "min"):
         lags = [[1, 4, 12, 24, 48]]
diff --git a/src/gluonts/time_feature/_base.py b/src/gluonts/time_feature/_base.py
index ab6ab20935..2be82cb26c 100644
--- a/src/gluonts/time_feature/_base.py
+++ b/src/gluonts/time_feature/_base.py
@@ -11,6 +11,7 @@
 # express or implied. See the License for the specific language governing
 # permissions and limitations under the License.
 
+from packaging.version import Version
 from typing import Any, Callable, Dict, List
 
 import numpy as np
@@ -194,7 +195,10 @@ def norm_freq_str(freq_str: str) -> str:
     # Note: Secondly ("S") frequency exists, where we don't want to remove the
     # "S"!
     if len(base_freq) >= 2 and base_freq.endswith("S"):
-        return base_freq[:-1]
+        base_freq = base_freq[:-1]
+        # In pandas >= 2.2, period end frequencies have been renamed, e.g. "M" -> "ME"
+        if Version(pd.__version__) >= Version("2.2.0"):
+            base_freq += "E"
 
     return base_freq
 
@@ -250,17 +254,13 @@ def time_features_from_frequency_str(freq_str: str) -> List[TimeFeature]:
     Unsupported frequency {freq_str}
 
     The following frequencies are supported:
-
-        Y   - yearly
-            alias: A
-        Q   - quarterly
-        M   - monthly
-        W   - weekly
-        D   - daily
-        B   - business days
-        H   - hourly
-        T   - minutely
-            alias: min
-        S   - secondly
+    
     """
+
+    for offset_cls in features_by_offsets:
+        offset = offset_cls()
+        supported_freq_msg += (
+            f"\t{offset.freqstr.split('-')[0]} - {offset_cls.__name__}"
+        )
+
     raise RuntimeError(supported_freq_msg)
diff --git a/src/gluonts/time_feature/lag.py b/src/gluonts/time_feature/lag.py
index 9abadb3a7f..c2215f139f 100644
--- a/src/gluonts/time_feature/lag.py
+++ b/src/gluonts/time_feature/lag.py
@@ -105,14 +105,14 @@ def _make_lags_for_month(multiple, num_cycles=3):
     # normalize offset name, so that both `W` and `W-SUN` refer to `W`
     offset_name = norm_freq_str(offset.name)
 
-    if offset_name == "A":
+    if offset_name in ["A", "Y", "YE"]:
         lags = []
-    elif offset_name == "Q":
+    elif offset_name in ["Q", "QE"]:
         assert (
             offset.n == 1
         ), "Only multiple 1 is supported for quarterly. Use x month instead."
         lags = _make_lags_for_month(offset.n * 3.0)
-    elif offset_name == "M":
+    elif offset_name in ["M", "ME"]:
         lags = _make_lags_for_month(offset.n)
     elif offset_name == "W":
         lags = _make_lags_for_week(offset.n)
@@ -124,14 +124,14 @@ def _make_lags_for_month(multiple, num_cycles=3):
         lags = _make_lags_for_day(
             offset.n, days_in_week=5, days_in_month=22
         ) + _make_lags_for_week(offset.n / 5.0)
-    elif offset_name == "H":
+    elif offset_name in ["H", "h"]:
         lags = (
             _make_lags_for_hour(offset.n)
             + _make_lags_for_day(offset.n / 24)
             + _make_lags_for_week(offset.n / (24 * 7))
         )
     # minutes
-    elif offset_name == "T":
+    elif offset_name in ["T", "min"]:
         lags = (
             _make_lags_for_minute(offset.n)
             + _make_lags_for_hour(offset.n / 60)
@@ -139,7 +139,7 @@ def _make_lags_for_month(multiple, num_cycles=3):
             + _make_lags_for_week(offset.n / (60 * 24 * 7))
         )
     # second
-    elif offset_name == "S":
+    elif offset_name in ["S", "s"]:
         lags = (
             _make_lags_for_second(offset.n)
             + _make_lags_for_minute(offset.n / 60)
diff --git a/src/gluonts/time_feature/seasonality.py b/src/gluonts/time_feature/seasonality.py
index 6517f87357..9cb2581a24 100644
--- a/src/gluonts/time_feature/seasonality.py
+++ b/src/gluonts/time_feature/seasonality.py
@@ -22,13 +22,18 @@
 
 DEFAULT_SEASONALITIES = {
     "S": 3600,  # 1 hour
+    "s": 3600,  # 1 hour
     "T": 1440,  # 1 day
+    "min": 1440,  # 1 day
     "H": 24,  # 1 day
+    "h": 24,  # 1 day
     "D": 1,  # 1 day
     "W": 1,  # 1 week
     "M": 12,
+    "ME": 12,
     "B": 5,
     "Q": 4,
+    "QE": 4,
 }
 
 
@@ -36,7 +41,7 @@ def get_seasonality(freq: str, seasonalities=DEFAULT_SEASONALITIES) -> int:
     """
     Return the seasonality of a given frequency:
 
-    >>> get_seasonality("2H")
+    >>> get_seasonality("2h")
     12
     """
     offset = pd.tseries.frequencies.to_offset(freq)
diff --git a/test/core/test_serde.py b/test/core/test_serde.py
index 34ecdcb9ff..b651874d1c 100644
--- a/test/core/test_serde.py
+++ b/test/core/test_serde.py
@@ -142,3 +142,26 @@ def test_serde_method():
 def test_np_str_dtype():
     a = np.array(["foo"])
     serde.decode(serde.encode(a.dtype)) == a.dtype
+
+
+@pytest.mark.parametrize(
+    "obj",
+    [
+        {"__kind__": 42, "class": cls_str}
+        for cls_str in [
+            "builtins.eval",
+            "builtins.exec",
+            "builtins.compile",
+            "builtins.open",
+            "builtins.input",
+            "eval",
+            "exec",
+            "compile",
+            "open",
+            "input",
+        ]
+    ],
+)
+def test_decode_disallow(obj):
+    with pytest.raises(ValueError):
+        serde.decode(obj)
diff --git a/test/time_feature/__init__.py b/test/time_feature/__init__.py
new file mode 100644
index 0000000000..f342912f9b
--- /dev/null
+++ b/test/time_feature/__init__.py
@@ -0,0 +1,12 @@
+# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License").
+# You may not use this file except in compliance with the License.
+# A copy of the License is located at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# or in the "license" file accompanying this file. This file is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
+# express or implied. See the License for the specific language governing
+# permissions and limitations under the License.
diff --git a/test/time_feature/common.py b/test/time_feature/common.py
new file mode 100644
index 0000000000..89e19a23f8
--- /dev/null
+++ b/test/time_feature/common.py
@@ -0,0 +1,28 @@
+# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License").
+# You may not use this file except in compliance with the License.
+# A copy of the License is located at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# or in the "license" file accompanying this file. This file is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
+# express or implied. See the License for the specific language governing
+# permissions and limitations under the License.
+
+import pandas as pd
+from packaging.version import Version
+
+if Version(pd.__version__) <= Version("2.2.0"):
+    S = "S"
+    H = "H"
+    M = "M"
+    Q = "Q"
+    Y = "A"
+else:
+    S = "s"
+    H = "h"
+    M = "ME"
+    Q = "QE"
+    Y = "YE"
diff --git a/test/time_feature/test_agg_lags.py b/test/time_feature/test_agg_lags.py
index dd3b2f2d9b..6e299e498d 100644
--- a/test/time_feature/test_agg_lags.py
+++ b/test/time_feature/test_agg_lags.py
@@ -16,7 +16,6 @@
 import pytest
 
 from gluonts.dataset.common import ListDataset
-
 from gluonts.dataset.field_names import FieldName
 from gluonts.transform import AddAggregateLags
 
diff --git a/test/time_feature/test_base.py b/test/time_feature/test_base.py
index b97f43cb93..e448156b2b 100644
--- a/test/time_feature/test_base.py
+++ b/test/time_feature/test_base.py
@@ -11,21 +11,23 @@
 # express or implied. See the License for the specific language governing
 # permissions and limitations under the License.
 
+import pytest
 from pandas.tseries.frequencies import to_offset
 
 from gluonts.time_feature import norm_freq_str
 
+from .common import M, Q, S, Y
 
-def test_norm_freq_str():
-    assert norm_freq_str(to_offset("Y").name) == "A"
-    assert norm_freq_str(to_offset("YS").name) == "A"
-    assert norm_freq_str(to_offset("A").name) == "A"
-    assert norm_freq_str(to_offset("AS").name) == "A"
 
-    assert norm_freq_str(to_offset("Q").name) == "Q"
-    assert norm_freq_str(to_offset("QS").name) == "Q"
-
-    assert norm_freq_str(to_offset("M").name) == "M"
-    assert norm_freq_str(to_offset("MS").name) == "M"
-
-    assert norm_freq_str(to_offset("S").name) == "S"
+@pytest.mark.parametrize(
+    " aliases, normalized_freq_str",
+    [
+        (["Y", "YS", "A", "AS"], Y),
+        (["Q", "QS"], Q),
+        (["M", "MS"], M),
+        (["S"], S),
+    ],
+)
+def test_norm_freq_str(aliases, normalized_freq_str):
+    for alias in aliases:
+        assert norm_freq_str(to_offset(alias).name) == normalized_freq_str
diff --git a/test/time_feature/test_features.py b/test/time_feature/test_features.py
index 96c590fbf2..1c59db9909 100644
--- a/test/time_feature/test_features.py
+++ b/test/time_feature/test_features.py
@@ -16,7 +16,6 @@
 import pytest
 
 from gluonts import zebras as zb
-
 from gluonts.time_feature import (
     Constant,
     TimeFeature,
diff --git a/test/time_feature/test_lag.py b/test/time_feature/test_lag.py
index 05377ef72f..194bffd72d 100644
--- a/test/time_feature/test_lag.py
+++ b/test/time_feature/test_lag.py
@@ -15,12 +15,16 @@
 Test the lags computed for different frequencies
 """
 
+import pytest
+
 import gluonts.time_feature.lag as date_feature_set
 
+from .common import H, M, Q, Y
+
 # These are the expected lags for common frequencies and corner cases.
 # By default all frequencies have the following lags: [1, 2, 3, 4, 5, 6, 7].
 # Remaining lags correspond to the same `season` (+/- `delta`) in previous `k` cycles.
-expected_lags = {
+EXPECTED_LAGS = {
     # (apart from the default lags) centered around each of the last 3 hours (delta = 2)
     "4S": [
         1,
@@ -179,7 +183,7 @@
     ]
     + [329, 330, 331, 494, 495, 496, 659, 660, 661, 707, 708, 709],
     # centered around each of the last 3 hours (delta = 2) + last 7 days (delta = 1) + last 6 weeks (delta = 1)
-    "H": [1, 2, 3, 4, 5, 6, 7]
+    H: [1, 2, 3, 4, 5, 6, 7]
     + [
         23,
         24,
@@ -206,7 +210,7 @@
     + [335, 336, 337, 503, 504, 505, 671, 672, 673, 719, 720, 721],
     # centered around each of the last 7 days (delta = 1) + last 4 weeks (delta = 1) + last 1 month (delta = 1) +
     #  last 8th and 12th weeks (delta = 0)
-    "6H": [
+    ("6" + H): [
         1,
         2,
         3,
@@ -237,21 +241,21 @@
     + [224, 336],
     # centered around each of the last 7 days (delta = 1) + last 4 weeks (delta = 1) + last 1 month (delta = 1) +
     #  last 8th and 12th weeks (delta = 0) + last year (delta = 1)
-    "12H": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
+    ("12" + H): [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
     + [27, 28, 29, 41, 42, 43, 55, 56, 57]
     + [59, 60, 61]
     + [112, 168]
     + [727, 728, 729],
     # centered around each of the last 7 days (delta = 1) + last 4 weeks (delta = 1) + last 1 month (delta = 1) +
     #  last 8th and 12th weeks (delta = 0) + last 3 years (delta = 1)
-    "23H": [1, 2, 3, 4, 5, 6, 7, 8]
+    ("23" + H): [1, 2, 3, 4, 5, 6, 7, 8]
     + [13, 14, 15, 20, 21, 22, 28, 29]
     + [30, 31, 32]
     + [58, 87]
     + [378, 379, 380, 758, 759, 760, 1138, 1139, 1140],
     # centered around each of the last 7 days (delta = 1) + last 4 weeks (delta = 1) + last 1 month (delta = 1) +
     #  last 8th and 12th weeks (delta = 0) + last 3 years (delta = 1)
-    "25H": [1, 2, 3, 4, 5, 6, 7]
+    ("25" + H): [1, 2, 3, 4, 5, 6, 7]
     + [12, 13, 14, 19, 20, 21, 25, 26, 27]
     + [28, 29]
     + [53, 80]
@@ -285,64 +289,31 @@
     # centered around each of the last 3 years (delta = 1)
     "5W": [1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 19, 20, 21, 30, 31, 32],
     # centered around each of the last 3 years (delta = 1)
-    "M": [1, 2, 3, 4, 5, 6, 7, 11, 12, 13, 23, 24, 25, 35, 36, 37],
+    M: [1, 2, 3, 4, 5, 6, 7, 11, 12, 13, 23, 24, 25, 35, 36, 37],
     # default
-    "6M": [1, 2, 3, 4, 5, 6, 7],
+    "6" + M: [1, 2, 3, 4, 5, 6, 7],
     # default
-    "12M": [1, 2, 3, 4, 5, 6, 7],
+    "12" + M: [1, 2, 3, 4, 5, 6, 7],
+    Q: [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13],
+    "QS": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13],
+    Y: [1, 2, 3, 4, 5, 6, 7],
+    "YS": [1, 2, 3, 4, 5, 6, 7],
 }
 
 # For the default multiple (1)
-for freq in ["min", "H", "D", "W", "M"]:
-    expected_lags["1" + freq] = expected_lags[freq]
+for freq in ["min", H, "D", "W", M]:
+    EXPECTED_LAGS["1" + freq] = EXPECTED_LAGS[freq]
 
 # For frequencies that do not have unique form
-expected_lags["60min"] = expected_lags["1H"]
-expected_lags["24H"] = expected_lags["1D"]
-expected_lags["7D"] = expected_lags["1W"]
-
-
-def test_lags():
-    freq_strs = [
-        "4S",
-        "min",
-        "1min",
-        "15min",
-        "30min",
-        "59min",
-        "60min",
-        "61min",
-        "H",
-        "1H",
-        "6H",
-        "12H",
-        "23H",
-        "24H",
-        "25H",
-        "D",
-        "1D",
-        "2D",
-        "6D",
-        "7D",
-        "8D",
-        "W",
-        "1W",
-        "3W",
-        "4W",
-        "5W",
-        "M",
-        "6M",
-        "12M",
-    ]
+EXPECTED_LAGS["60min"] = EXPECTED_LAGS["1" + H]
+EXPECTED_LAGS["24" + H] = EXPECTED_LAGS["1D"]
+EXPECTED_LAGS["7D"] = EXPECTED_LAGS["1W"]
 
-    for freq_str in freq_strs:
-        lags = date_feature_set.get_lags_for_frequency(freq_str)
 
-        assert (
-            lags == expected_lags[freq_str]
-        ), "lags do not match for the frequency '{}':\nexpected: {},\nprovided: {}".format(
-            freq_str, expected_lags[freq_str], lags
-        )
+@pytest.mark.parametrize("freq_str, expected_lags", EXPECTED_LAGS.items())
+def test_lags(freq_str, expected_lags):
+    lags = date_feature_set.get_lags_for_frequency(freq_str)
+    assert lags == expected_lags
 
 
 if __name__ == "__main__":
diff --git a/test/time_feature/test_seasonality.py b/test/time_feature/test_seasonality.py
index 0323e52ebe..3817416c2c 100644
--- a/test/time_feature/test_seasonality.py
+++ b/test/time_feature/test_seasonality.py
@@ -15,25 +15,42 @@
 
 from gluonts.time_feature import get_seasonality
 
+from .common import H, M, Q, Y
 
-@pytest.mark.parametrize(
-    "freq, expected_seasonality",
-    [
-        ("30min", 48),
-        ("1H", 24),
-        ("H", 24),
-        ("2H", 12),
-        ("3H", 8),
-        ("4H", 6),
-        ("15H", 1),
-        ("5B", 1),
-        ("1B", 5),
-        ("2W", 1),
-        ("3M", 4),
-        ("1D", 1),
-        ("7D", 1),
-        ("8D", 1),
-    ],
-)
+TEST_CASES = [
+    ("30min", 48),
+    ("5B", 1),
+    ("1B", 5),
+    ("2W", 1),
+    ("1D", 1),
+    ("7D", 1),
+    ("8D", 1),
+    # Monthly
+    ("MS", 12),
+    ("3MS", 4),
+    (M, 12),
+    ("3" + M, 4),
+    # Quarterly
+    ("QS", 4),
+    ("2QS", 2),
+    (Q, 4),
+    ("2" + Q, 2),
+    ("3" + Q, 1),
+    # Hourly
+    ("1" + H, 24),
+    (H, 24),
+    ("2" + H, 12),
+    ("3" + H, 8),
+    ("4" + H, 6),
+    ("15" + H, 1),
+    # Yearly
+    (Y, 1),
+    ("2" + Y, 1),
+    ("YS", 1),
+    ("2YS", 1),
+]
+
+
+@pytest.mark.parametrize("freq, expected_seasonality", TEST_CASES)
 def test_get_seasonality(freq, expected_seasonality):
     assert get_seasonality(freq) == expected_seasonality