diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt
index fd1cd3d0022c96..faac02888af006 100644
--- a/doc/source/whatsnew/v0.20.0.txt
+++ b/doc/source/whatsnew/v0.20.0.txt
@@ -320,6 +320,7 @@ Other Enhancements
 ^^^^^^^^^^^^^^^^^^
 
 - Integration with the ``feather-format``, including a new top-level ``pd.read_feather()`` and ``DataFrame.to_feather()`` method, see :ref:`here <io.feather>`.
+- The ``Series`` constructor will now accept timestamp dtypes that do not specify frequency like ``np.datetime64`` (:issue:`15524`)
 - ``Series.str.replace()`` now accepts a callable, as replacement, which is passed to ``re.sub`` (:issue:`15055`)
 - ``Series.str.replace()`` now accepts a compiled regular expression as a pattern (:issue:`15446`)
 
diff --git a/pandas/tests/series/test_constructors.py b/pandas/tests/series/test_constructors.py
index dbe2db67359f3f..c429be2680f18d 100644
--- a/pandas/tests/series/test_constructors.py
+++ b/pandas/tests/series/test_constructors.py
@@ -839,3 +839,27 @@ def test_constructor_cast_object(self):
         s = Series(date_range('1/1/2000', periods=10), dtype=object)
         exp = Series(date_range('1/1/2000', periods=10))
         tm.assert_series_equal(s, exp)
+
+    def test_constructor_generic_timestamp(self):
+        # see gh-15524
+        dtype = np.timedelta64
+        s = Series([], dtype=dtype)
+
+        assert s.empty
+        assert s.dtype == 'm8[ns]'
+
+        dtype = np.datetime64
+        s = Series([], dtype=dtype)
+
+        assert s.empty
+        assert s.dtype == 'M8[ns]'
+
+        # These timestamps have the wrong frequencies,
+        # so an Exception should be raised now.
+        msg = "cannot convert timedeltalike"
+        with tm.assertRaisesRegexp(TypeError, msg):
+            Series([], dtype='m8[ps]')
+
+        msg = "cannot convert datetimelike"
+        with tm.assertRaisesRegexp(TypeError, msg):
+            Series([], dtype='M8[ps]')
diff --git a/pandas/tests/series/test_dtypes.py b/pandas/tests/series/test_dtypes.py
index a2aaff25516ae0..1127c3381f57db 100644
--- a/pandas/tests/series/test_dtypes.py
+++ b/pandas/tests/series/test_dtypes.py
@@ -153,6 +153,38 @@ def test_astype_dict(self):
         self.assertRaises(KeyError, s.astype, {'abc': str, 'def': str})
         self.assertRaises(KeyError, s.astype, {0: str})
 
+    def test_astype_generic_timestamp(self):
+        # see gh-15524
+        data = [1]
+
+        s = Series(data)
+        dtype = np.datetime64
+        result = s.astype(dtype)
+        expected = Series(data, dtype=dtype)
+        assert_series_equal(result, expected)
+
+        s = Series(data)
+        dtype = np.timedelta64
+        result = s.astype(dtype)
+        expected = Series(data, dtype=dtype)
+        assert_series_equal(result, expected)
+
+    def test_astype_empty_constructor_equality(self):
+        # see gh-15524
+
+        for dtype in np.typecodes['All']:
+            if dtype not in ('S', 'V'):  # poor support (if any) currently
+                init_empty = Series([], dtype=dtype)
+                astype_empty = Series([]).astype(dtype)
+
+                try:
+                    assert_series_equal(init_empty, astype_empty)
+                except AssertionError as e:
+                    name = np.dtype(dtype).name
+                    msg = "{dtype} failed: ".format(dtype=name) + str(e)
+
+                    raise AssertionError(msg)
+
     def test_complexx(self):
         # GH4819
         # complex access for ndarray compat
diff --git a/pandas/types/cast.py b/pandas/types/cast.py
index 580ce12de33335..1209d6dc63895c 100644
--- a/pandas/types/cast.py
+++ b/pandas/types/cast.py
@@ -603,6 +603,14 @@ def astype_nansafe(arr, dtype, copy=True):
         # work around NumPy brokenness, #1987
         return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape)
 
+    # NumPy arrays don't handle generic timestamp dtypes well. Since
+    # we only use the nanosecond frequency, interpret generic timestamp
+    # dtypes as nanosecond frequency.
+    if dtype.name == "datetime64":
+        dtype = np.dtype('M8[ns]')
+    elif dtype.name == "timedelta64":
+        dtype = np.dtype('m8[ns]')
+
     if copy:
         return arr.astype(dtype)
     return arr.view(dtype)
@@ -855,7 +863,7 @@ def maybe_cast_to_datetime(value, dtype, errors='raise'):
 
             # force the dtype if needed
             if is_datetime64 and not is_dtype_equal(dtype, _NS_DTYPE):
-                if dtype.name == 'datetime64[ns]':
+                if dtype.name in ('datetime64', 'datetime64[ns]'):
                     dtype = _NS_DTYPE
                 else:
                     raise TypeError("cannot convert datetimelike to "
@@ -869,7 +877,7 @@ def maybe_cast_to_datetime(value, dtype, errors='raise'):
                     value = [value]
 
             elif is_timedelta64 and not is_dtype_equal(dtype, _TD_DTYPE):
-                if dtype.name == 'timedelta64[ns]':
+                if dtype.name in ('timedelta64', 'timedelta64[ns]'):
                     dtype = _TD_DTYPE
                 else:
                     raise TypeError("cannot convert timedeltalike to "