diff --git a/pandas-stubs/_libs/interval.pyi b/pandas-stubs/_libs/interval.pyi
index 82c9b751d..0dd40c964 100644
--- a/pandas-stubs/_libs/interval.pyi
+++ b/pandas-stubs/_libs/interval.pyi
@@ -27,9 +27,9 @@ from pandas._typing import (
 
 VALID_CLOSED: frozenset[str]
 
-_OrderableScalarT = TypeVar("_OrderableScalarT", int, float)
-_OrderableTimesT = TypeVar("_OrderableTimesT", Timestamp, Timedelta)
-_OrderableT = TypeVar("_OrderableT", int, float, Timestamp, Timedelta)
+_OrderableScalarT = TypeVar("_OrderableScalarT", bound=int | float)
+_OrderableTimesT = TypeVar("_OrderableTimesT", bound=Timestamp | Timedelta)
+_OrderableT = TypeVar("_OrderableT", bound=int | float | Timestamp | Timedelta)
 
 class _LengthDescriptor:
     @overload
diff --git a/pandas-stubs/_typing.pyi b/pandas-stubs/_typing.pyi
index 673a50446..106ada7b0 100644
--- a/pandas-stubs/_typing.pyi
+++ b/pandas-stubs/_typing.pyi
@@ -360,13 +360,7 @@ NDFrameT = TypeVar("NDFrameT", bound=NDFrame)
 IndexT = TypeVar("IndexT", bound=Index)
 
 # Interval closed type
-IntervalT = TypeVar(
-    "IntervalT",
-    Interval[int],
-    Interval[float],
-    Interval[Timestamp],
-    Interval[Timedelta],
-)
+IntervalT = TypeVar("IntervalT", bound=Interval)
 IntervalClosedType: TypeAlias = Literal["left", "right", "both", "neither"]
 
 TakeIndexer: TypeAlias = Sequence[int] | Sequence[np.integer] | npt.NDArray[np.integer]
diff --git a/pandas-stubs/core/series.pyi b/pandas-stubs/core/series.pyi
index 032ff40a3..ae6daa5da 100644
--- a/pandas-stubs/core/series.pyi
+++ b/pandas-stubs/core/series.pyi
@@ -27,6 +27,7 @@ from matplotlib.axes import (
 import numpy as np
 from pandas import (
     Period,
+    PeriodDtype,
     Timedelta,
     Timestamp,
 )
@@ -104,6 +105,7 @@ from pandas._typing import (
     CategoryDtypeArg,
     ComplexDtypeArg,
     CompressionOptions,
+    Dtype,
     DtypeBackend,
     DtypeObj,
     FilePath,
@@ -209,92 +211,55 @@ class Series(IndexOpsMixin, NDFrame, Generic[S1]):
     _ListLike: TypeAlias = ArrayLike | dict[_str, np.ndarray] | list | tuple | Index
     __hash__: ClassVar[None]
 
+    # TODO: can __new__ be converted to __init__? Pandas implements __init__
     @overload
     def __new__(
         cls,
         data: DatetimeIndex | Sequence[Timestamp | np.datetime64 | datetime],
         index: Axes | None = ...,
-        dtype=...,
+        dtype: TimestampDtypeArg = ...,
         name: Hashable | None = ...,
         copy: bool = ...,
-        fastpath: bool = ...,
     ) -> TimestampSeries: ...
     @overload
     def __new__(
         cls,
         data: _ListLike,
-        dtype: Literal["datetime64[ns]"],
         index: Axes | None = ...,
+        *,
+        dtype: TimestampDtypeArg,
         name: Hashable | None = ...,
         copy: bool = ...,
-        fastpath: bool = ...,
     ) -> TimestampSeries: ...
     @overload
     def __new__(
         cls,
         data: PeriodIndex,
         index: Axes | None = ...,
-        dtype=...,
+        dtype: PeriodDtype = ...,
         name: Hashable | None = ...,
         copy: bool = ...,
-        fastpath: bool = ...,
     ) -> PeriodSeries: ...
     @overload
     def __new__(
         cls,
         data: TimedeltaIndex | Sequence[Timedelta | np.timedelta64 | timedelta],
         index: Axes | None = ...,
-        dtype=...,
+        dtype: TimedeltaDtypeArg = ...,
         name: Hashable | None = ...,
         copy: bool = ...,
-        fastpath: bool = ...,
     ) -> TimedeltaSeries: ...
     @overload
     def __new__(
         cls,
-        data: IntervalIndex[Interval[int]] | Interval[int] | Sequence[Interval[int]],
-        index: Axes | None = ...,
-        dtype=...,
-        name: Hashable | None = ...,
-        copy: bool = ...,
-        fastpath: bool = ...,
-    ) -> IntervalSeries[int]: ...
-    @overload
-    def __new__(
-        cls,
-        data: IntervalIndex[Interval[float]]
-        | Interval[float]
-        | Sequence[Interval[float]],
-        index: Axes | None = ...,
-        dtype=...,
-        name: Hashable | None = ...,
-        copy: bool = ...,
-        fastpath: bool = ...,
-    ) -> IntervalSeries[float]: ...
-    @overload
-    def __new__(
-        cls,
-        data: IntervalIndex[Interval[Timestamp]]
-        | Interval[Timestamp]
-        | Sequence[Interval[Timestamp]],
-        index: Axes | None = ...,
-        dtype=...,
-        name: Hashable | None = ...,
-        copy: bool = ...,
-        fastpath: bool = ...,
-    ) -> IntervalSeries[Timestamp]: ...
-    @overload
-    def __new__(
-        cls,
-        data: IntervalIndex[Interval[Timedelta]]
-        | Interval[Timedelta]
-        | Sequence[Interval[Timedelta]],
+        data: IntervalIndex[Interval[_OrderableT]]
+        | Interval[_OrderableT]
+        | Sequence[Interval[_OrderableT]],
         index: Axes | None = ...,
-        dtype=...,
+        dtype: Literal["Interval"] = ...,
         name: Hashable | None = ...,
         copy: bool = ...,
-        fastpath: bool = ...,
-    ) -> IntervalSeries[Timedelta]: ...
+    ) -> IntervalSeries[_OrderableT]: ...
     @overload
     def __new__(
         cls,
@@ -303,27 +268,24 @@ class Series(IndexOpsMixin, NDFrame, Generic[S1]):
         index: Axes | None = ...,
         name: Hashable | None = ...,
         copy: bool = ...,
-        fastpath: bool = ...,
     ) -> Self: ...
     @overload
     def __new__(
         cls,
         data: Series[S1] | dict[int, S1] | dict[_str, S1] = ...,
         index: Axes | None = ...,
-        dtype=...,
+        dtype: Dtype = ...,
         name: Hashable | None = ...,
         copy: bool = ...,
-        fastpath: bool = ...,
     ) -> Self: ...
     @overload
     def __new__(
         cls,
         data: object | _ListLike | None = ...,
         index: Axes | None = ...,
-        dtype=...,
+        dtype: Dtype = ...,
         name: Hashable | None = ...,
         copy: bool = ...,
-        fastpath: bool = ...,
     ) -> Series: ...
     @property
     def hasnans(self) -> bool: ...