diff --git a/keras_core/layers/__init__.py b/keras_core/layers/__init__.py
index c9f9fb8ea..78b22788c 100644
--- a/keras_core/layers/__init__.py
+++ b/keras_core/layers/__init__.py
@@ -68,3 +68,4 @@ from keras_core.layers.regularization.spatial_dropout import SpatialDropout2D
 from keras_core.layers.regularization.spatial_dropout import SpatialDropout3D
 from keras_core.layers.reshaping.flatten import Flatten
 from keras_core.layers.reshaping.reshape import Reshape
+from keras_core.layers.reshaping.up_sampling1d import UpSampling1D
diff --git a/keras_core/layers/reshaping/up_sampling1d.py b/keras_core/layers/reshaping/up_sampling1d.py
new file mode 100644
index 000000000..10e81fdff
--- /dev/null
+++ b/keras_core/layers/reshaping/up_sampling1d.py
@@ -0,0 +1,59 @@
+from keras_core import operations as ops
+from keras_core.layers.input_spec import InputSpec
+from keras_core.layers.layer import Layer
+
+
+class UpSampling1D(Layer):
+    """Upsampling layer for 1D inputs.
+
+    Repeats each temporal step `size` times along the time axis.
+
+    Examples:
+
+    >>> input_shape = (2, 2, 3)
+    >>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
+    >>> print(x)
+    [[[ 0  1  2]
+      [ 3  4  5]]
+     [[ 6  7  8]
+      [ 9 10 11]]]
+    >>> y = keras_core.layers.UpSampling1D(size=2)(x)
+    >>> print(y)
+    [[[ 0.  1.  2.]
+      [ 0.  1.  2.]
+      [ 3.  4.  5.]
+      [ 3.  4.  5.]]
+
+     [[ 6.  7.  8.]
+      [ 6.  7.  8.]
+      [ 9. 10. 11.]
+      [ 9. 10. 11.]]]
+
+    Args:
+        size: Integer. Upsampling factor.
+
+    Input shape:
+        3D tensor with shape: `(batch_size, steps, features)`.
+
+    Output shape:
+        3D tensor with shape: `(batch_size, upsampled_steps, features)`.
+    """
+
+    def __init__(self, size=2, **kwargs):
+        super().__init__(**kwargs)
+        self.size = int(size)
+        self.input_spec = InputSpec(ndim=3)
+
+    def compute_output_shape(self, input_shape):
+        size = (
+            self.size * input_shape[1] if input_shape[1] is not None else None
+        )
+        return [input_shape[0], size, input_shape[2]]
+
+    def call(self, inputs):
+        return ops.repeat(x=inputs, repeats=self.size, axis=1)
+
+    def get_config(self):
+        config = {"size": self.size}
+        base_config = super().get_config()
+        return {**base_config, **config}
diff --git a/keras_core/layers/reshaping/up_sampling1d_test.py b/keras_core/layers/reshaping/up_sampling1d_test.py
new file mode 100644
index 000000000..941755936
--- /dev/null
+++ b/keras_core/layers/reshaping/up_sampling1d_test.py
@@ -0,0 +1,66 @@
+import numpy as np
+import pytest
+
+from keras_core import backend
+from keras_core import layers
+from keras_core import testing
+from keras_core.backend.common.keras_tensor import KerasTensor
+
+
+class UpSamplingTest(testing.TestCase):
+    def test_upsampling_1d(self):
+        self.run_layer_test(
+            layers.UpSampling1D,
+            init_kwargs={"size": 2},
+            input_shape=(3, 5, 4),
+            expected_output_shape=(3, 10, 4),
+            expected_output_dtype="float32",
+            expected_num_trainable_weights=0,
+            expected_num_non_trainable_weights=0,
+            expected_num_seed_generators=0,
+            expected_num_losses=0,
+            supports_masking=False,
+        )
+
+    def test_upsampling_1d_correctness(self):
+        np.testing.assert_array_equal(
+            layers.UpSampling1D(size=2)(np.arange(12).reshape((2, 2, 3))),
+            np.array(
+                [
+                    [
+                        [0.0, 1.0, 2.0],
+                        [0.0, 1.0, 2.0],
+                        [3.0, 4.0, 5.0],
+                        [3.0, 4.0, 5.0],
+                    ],
+                    [
+                        [6.0, 7.0, 8.0],
+                        [6.0, 7.0, 8.0],
+                        [9.0, 10.0, 11.0],
+                        [9.0, 10.0, 11.0],
+                    ],
+                ]
+            ),
+        )
+
+    def test_upsampling_1d_correctness_with_ones(self):
+        np.testing.assert_array_equal(
+            layers.UpSampling1D(size=3)(np.ones((2, 1, 5))), np.ones((2, 3, 5))
+        )
+
+    @pytest.mark.skipif(
+        not backend.DYNAMIC_SHAPES_OK,
+        reason="Backend does not support dynamic shapes",
+    )
+    def test_upsampling_1d_with_dynamic_shape(self):
+        x = KerasTensor([None, 2, 3])
+        self.assertEqual(layers.UpSampling1D(size=2)(x).shape, (None, 4, 3))
+        self.assertEqual(layers.UpSampling1D(size=4)(x).shape, (None, 8, 3))
+
+        y = KerasTensor([2, None, 3])
+        self.assertEqual(layers.UpSampling1D(size=2)(y).shape, (2, None, 3))
+        self.assertEqual(layers.UpSampling1D(size=4)(y).shape, (2, None, 3))
+
+        z = KerasTensor([2, 3, None])
+        self.assertEqual(layers.UpSampling1D(size=2)(z).shape, (2, 6, None))
+        self.assertEqual(layers.UpSampling1D(size=4)(z).shape, (2, 12, None))