diff --git a/CHANGELOG.md b/CHANGELOG.md
index d8416a07e..94bb83678 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -56,7 +56,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - 
 - 
 - 
-- 
+- Fix holidays during loading datasets `traffic_2008_10T` and `traffic_2008_hourly` ([#462](https://github.com/etna-team/etna/pull/462))
 - 
 - 
 - 
diff --git a/etna/datasets/internal_datasets.py b/etna/datasets/internal_datasets.py
index 67cb74469..fdf367efb 100644
--- a/etna/datasets/internal_datasets.py
+++ b/etna/datasets/internal_datasets.py
@@ -15,7 +15,6 @@
 from typing import Tuple
 from typing import Union
 
-import holidays
 import numpy as np
 import pandas as pd
 
@@ -162,7 +161,7 @@ def load_dataset(
         data, dataset_hash = read_dataset(dataset_path=dataset_dir / f"{name}_{part}.csv.gz")
         if dataset_hash != datasets_dict[name]["hash"][part]:
             warnings.warn(
-                f"Local hash and expected hash are different for {name} record part {part}."
+                f"Local hash and expected hash are different for {name} record part {part}. "
                 "The first possible reason is that the local copy of the dataset is out of date. In this case you can "
                 "try setting rebuild_dataset=True to rebuild the dataset. The second possible reason is that the local "
                 "copy of the dataset reflects a more recent version of the data than your version of the library. "
@@ -348,11 +347,22 @@ def read_data(path: Path, part: str) -> np.ndarray:
     targets = np.concatenate([targets_train, targets_test], axis=0)
     targets = targets[np.argsort(ts_indecies)].reshape(-1, 963)
 
-    drop_days = (
-        list(holidays.country_holidays(country="US", years=2008).keys())
-        + list(holidays.country_holidays(country="US", years=2009).keys())[:3]
-        + [date(2009, 3, 8), date(2009, 3, 10)]
-    )
+    # federal holidays and days with anomalies
+    drop_days = [
+        date(2008, 1, 1),
+        date(2008, 1, 21),
+        date(2008, 2, 18),
+        date(2008, 5, 26),
+        date(2008, 7, 4),
+        date(2008, 9, 1),
+        date(2008, 10, 13),
+        date(2008, 11, 11),
+        date(2008, 11, 27),
+        date(2008, 12, 25),
+        date(2009, 1, 1),
+        date(2009, 1, 19),
+        date(2009, 2, 16),
+    ] + [date(2008, 3, 8), date(2009, 3, 9)]
 
     dates_df = pd.DataFrame({"timestamp": pd.date_range("2008-01-01 00:00:00", "2009-03-30 23:50:00", freq="10T")})
     dates_df["dt"] = dates_df["timestamp"].dt.date
@@ -917,9 +927,9 @@ def list_datasets() -> List[str]:
         "freq": "10T",
         "parts": ("train", "test", "full"),
         "hash": {
-            "train": "4d8d1367fd5341475b852fe9779d0b05",
+            "train": "f22f77c170e698f4f51231b24e5bc9f0",
             "test": "261ee7b09e50d1c7e1e74ccf08412f3f",
-            "full": "f0c9229d78cfa5b0abf5be950b6843b2",
+            "full": "d1d05602b15aa30d461e21148483a0c8",
         },
     },
     "traffic_2008_hourly": {
@@ -927,9 +937,9 @@ def list_datasets() -> List[str]:
         "freq": "H",
         "parts": ("train", "test", "full"),
         "hash": {
-            "train": "7e6609cce30ae22004c7d7b1d39a35d5",
+            "train": "161748edc508b4e206344fcbb984bf9a",
             "test": "adc3fa06ee856c6481faa400e9e9f602",
-            "full": "8d988a81e8c368164aada708be27a1c2",
+            "full": "899bc1fa3fc334868a9e41033a2c3a52",
         },
     },
     "traffic_2015_hourly": {