diff --git a/src/uni2ts/data/builder/simple.py b/src/uni2ts/data/builder/simple.py
index 1a35007..b33178c 100644
--- a/src/uni2ts/data/builder/simple.py
+++ b/src/uni2ts/data/builder/simple.py
@@ -37,9 +37,22 @@ def _from_long_dataframe(
     df: pd.DataFrame,
     offset: Optional[int] = None,
     date_offset: Optional[pd.Timestamp] = None,
+    freq: str = "H",
 ) -> tuple[GenFunc, Features]:
     items = df.item_id.unique()
 
+    # Infer the freq and generate the prompt
+    inferred_freq = pd.infer_freq(df.index)
+
+    if inferred_freq is not None:
+        print(
+            f"Inferred frequency: {inferred_freq}. Using this value for the 'freq' parameter."
+        )
+    else:
+        print(
+            f"Inferred frequency is None. Using predefined {freq} for the 'freq' parameter."
+        )
+
     def example_gen_func() -> Generator[dict[str, Any], None, None]:
         for item_id in items:
             item_df = df.query(f'item_id == "{item_id}"').drop("item_id", axis=1)
@@ -50,7 +63,11 @@ def example_gen_func() -> Generator[dict[str, Any], None, None]:
             yield {
                 "target": item_df.to_numpy(),
                 "start": item_df.index[0],
-                "freq": pd.infer_freq(item_df.index),
+                "freq": (
+                    pd.infer_freq(df.index)
+                    if pd.infer_freq(df.index) is not None
+                    else freq
+                ),
                 "item_id": item_id,
             }
 
@@ -70,6 +87,7 @@ def _from_wide_dataframe(
     df: pd.DataFrame,
     offset: Optional[int] = None,
     date_offset: Optional[pd.Timestamp] = None,
+    freq: str = "H",
 ) -> tuple[GenFunc, Features]:
     if offset is not None:
         df = df.iloc[:offset]
@@ -78,12 +96,28 @@ def _from_wide_dataframe(
 
     print(df)
 
+    # Infer the freq and generate the prompt
+    inferred_freq = pd.infer_freq(df.index)
+
+    if inferred_freq is not None:
+        print(
+            f"Inferred frequency: {inferred_freq}. Using this value for the 'freq' parameter."
+        )
+    else:
+        print(
+            f"Inferred frequency is None. Using predefined {freq} for the 'freq' parameter."
+        )
+
     def example_gen_func() -> Generator[dict[str, Any], None, None]:
         for i in range(len(df.columns)):
             yield {
                 "target": df.iloc[:, i].to_numpy(),
                 "start": df.index[0],
-                "freq": pd.infer_freq(df.index),
+                "freq": (
+                    pd.infer_freq(df.index)
+                    if pd.infer_freq(df.index) is not None
+                    else freq
+                ),
                 "item_id": f"item_{i}",
             }
 
@@ -103,17 +137,32 @@ def _from_wide_dataframe_multivariate(
     df: pd.DataFrame,
     offset: Optional[int] = None,
     date_offset: Optional[pd.Timestamp] = None,
+    freq: str = "H",
 ) -> tuple[GenFunc, Features]:
     if offset is not None:
         df = df.iloc[:offset]
     elif date_offset is not None:
         df = df[df.index <= date_offset]
 
+    # Infer the freq and generate the prompt
+    inferred_freq = pd.infer_freq(df.index)
+
+    if inferred_freq is not None:
+        print(
+            f"Inferred frequency: {inferred_freq}. Using this value for the 'freq' parameter."
+        )
+    else:
+        print(
+            f"Inferred frequency is None. Using predefined {freq} for the 'freq' parameter."
+        )
+
     def example_gen_func() -> Generator[dict[str, Any], None, None]:
         yield {
             "target": df.to_numpy().T,
             "start": df.index[0],
-            "freq": pd.infer_freq(df.index),
+            "freq": (
+                pd.infer_freq(df.index) if pd.infer_freq(df.index) is not None else freq
+            ),
             "item_id": "item_0",
         }
 
@@ -145,6 +194,7 @@ def build_dataset(
         dataset_type: str,
         offset: Optional[int] = None,
         date_offset: Optional[pd.Timestamp] = None,
+        freq: str = "H",
     ):
         assert offset is None or date_offset is None, (
             "One or neither offset and date_offset must be specified, but not both. "
@@ -166,7 +216,7 @@ def build_dataset(
             )
 
         example_gen_func, features = _from_dataframe(
-            df, offset=offset, date_offset=date_offset
+            df, freq=freq, offset=offset, date_offset=date_offset
         )
         hf_dataset = datasets.Dataset.from_generator(
             example_gen_func, features=features
@@ -203,7 +253,7 @@ class SimpleEvalDatasetBuilder(DatasetBuilder):
     def __post_init__(self):
         self.storage_path = Path(self.storage_path)
 
-    def build_dataset(self, file: Path, dataset_type: str):
+    def build_dataset(self, file: Path, dataset_type: str, freq: str = "H"):
         df = pd.read_csv(file, index_col=0, parse_dates=True)
 
         if dataset_type == "long":
@@ -218,7 +268,7 @@ def build_dataset(self, file: Path, dataset_type: str):
                 " Valid options are 'long', 'wide', and 'wide_multivariate'."
             )
 
-        example_gen_func, features = _from_dataframe(df)
+        example_gen_func, features = _from_dataframe(df, freq=freq)
         hf_dataset = datasets.Dataset.from_generator(
             example_gen_func, features=features
         )
@@ -289,6 +339,13 @@ def generate_eval_builders(
         type=str,
         default=None,
     )
+    # Define the `freq` argument with a default value. Use this value as 'freq' if 'freq' is None.
+    parser.add_argument(
+        "--freq",
+        default="H",  # Set the default value
+        help="The user specified frequency",
+    )
+
     args = parser.parse_args()
 
     SimpleDatasetBuilder(dataset=args.dataset_name).build_dataset(
@@ -296,6 +353,7 @@ def generate_eval_builders(
         dataset_type=args.dataset_type,
         offset=args.offset,
         date_offset=pd.Timestamp(args.date_offset) if args.date_offset else None,
+        freq=args.freq,
     )
 
     if args.offset is not None or args.date_offset is not None:
@@ -307,4 +365,6 @@ def generate_eval_builders(
             prediction_length=None,
             context_length=None,
             patch_size=None,
-        ).build_dataset(file=Path(args.file_path), dataset_type=args.dataset_type)
+        ).build_dataset(
+            file=Path(args.file_path), dataset_type=args.dataset_type, freq=args.freq
+        )