diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproxCountDistinctForIntervals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproxCountDistinctForIntervals.scala
index 19e212d1f9..a7e9a22b13 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproxCountDistinctForIntervals.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproxCountDistinctForIntervals.scala
@@ -61,7 +61,7 @@ case class ApproxCountDistinctForIntervals(
   }
 
   override def inputTypes: Seq[AbstractDataType] = {
-    Seq(TypeCollection(NumericType, TimestampType, DateType), ArrayType)
+    Seq(TypeCollection(NumericType, TimestampType, DateType, TimestampNTZType), ArrayType)
   }
 
   // Mark as lazy so that endpointsExpression is not evaluated during tree transformation.
@@ -79,7 +79,7 @@ case class ApproxCountDistinctForIntervals(
       TypeCheckFailure("The endpoints provided must be constant literals")
     } else {
       endpointsExpression.dataType match {
-        case ArrayType(_: NumericType | DateType | TimestampType, _) =>
+        case ArrayType(_: NumericType | DateType | TimestampType | TimestampNTZType, _) =>
           if (endpoints.length < 2) {
             TypeCheckFailure("The number of endpoints must be >= 2 to construct intervals")
           } else {
@@ -122,7 +122,7 @@ case class ApproxCountDistinctForIntervals(
           n.numeric.toDouble(value.asInstanceOf[n.InternalType])
         case _: DateType =>
           value.asInstanceOf[Int].toDouble
-        case _: TimestampType =>
+        case TimestampType | TimestampNTZType =>
           value.asInstanceOf[Long].toDouble
       }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproxCountDistinctForIntervalsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproxCountDistinctForIntervalsSuite.scala
index 73f18d4fee..9d53673296 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproxCountDistinctForIntervalsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproxCountDistinctForIntervalsSuite.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions.aggregate
 
 import java.sql.{Date, Timestamp}
+import java.time.LocalDateTime
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
@@ -38,7 +39,7 @@ class ApproxCountDistinctForIntervalsSuite extends SparkFunSuite {
       assert(
         wrongColumn.checkInputDataTypes() match {
           case TypeCheckFailure(msg)
-            if msg.contains("requires (numeric or timestamp or date) type") => true
+            if msg.contains("requires (numeric or timestamp or date or timestamp_ntz) type") => true
           case _ => false
         })
     }
@@ -199,7 +200,9 @@ class ApproxCountDistinctForIntervalsSuite extends SparkFunSuite {
       (intRecords.map(DateTimeUtils.toJavaDate),
           intEndpoints.map(DateTimeUtils.toJavaDate), DateType),
       (intRecords.map(DateTimeUtils.toJavaTimestamp(_)),
-          intEndpoints.map(DateTimeUtils.toJavaTimestamp(_)), TimestampType)
+          intEndpoints.map(DateTimeUtils.toJavaTimestamp(_)), TimestampType),
+      (intRecords.map(DateTimeUtils.microsToLocalDateTime(_)),
+        intEndpoints.map(DateTimeUtils.microsToLocalDateTime(_)), TimestampNTZType)
     )
 
     inputs.foreach { case (records, endpoints, dataType) =>
@@ -209,6 +212,7 @@ class ApproxCountDistinctForIntervalsSuite extends SparkFunSuite {
         val value = r match {
           case d: Date => DateTimeUtils.fromJavaDate(d)
           case t: Timestamp => DateTimeUtils.fromJavaTimestamp(t)
+          case ldt: LocalDateTime => DateTimeUtils.localDateTimeToMicros(ldt)
           case _ => r
         }
         input.update(0, value)