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+# Copyright 2024 The Google Research Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Directory to extract time covariates.
+
+Extract time covariates from datetime.
+"""
+
+import numpy as np
+import pandas as pd
+from pandas.tseries.holiday import EasterMonday
+from pandas.tseries.holiday import GoodFriday
+from pandas.tseries.holiday import Holiday
+from pandas.tseries.holiday import SU
+from pandas.tseries.holiday import TH
+from pandas.tseries.holiday import USColumbusDay
+from pandas.tseries.holiday import USLaborDay
+from pandas.tseries.holiday import USMartinLutherKingJr
+from pandas.tseries.holiday import USMemorialDay
+from pandas.tseries.holiday import USPresidentsDay
+from pandas.tseries.holiday import USThanksgivingDay
+from pandas.tseries.offsets import DateOffset
+from pandas.tseries.offsets import Day
+from pandas.tseries.offsets import Easter
+from sklearn.preprocessing import StandardScaler
+from tqdm import tqdm
+
+
+# This is 183 to cover half a year (in both directions), also for leap years
+# + 17 as Eastern can be between March, 22 - April, 25
+MAX_WINDOW = 183 + 17
+
+
+def _distance_to_holiday(holiday):
+  """Return distance to given holiday."""
+
+  def _distance_to_day(index):
+    holiday_date = holiday.dates(
+        index - pd.Timedelta(days=MAX_WINDOW),
+        index + pd.Timedelta(days=MAX_WINDOW),
+    )
+    assert (
+        len(holiday_date) != 0  # pylint: disable=g-explicit-length-test
+    ), f"No closest holiday for the date index {index} found."
+    # It sometimes returns two dates if it is exactly half a year after the
+    # holiday. In this case, the smaller distance (182 days) is returned.
+    return (index - holiday_date[0]).days
+
+  return _distance_to_day
+
+
+EasterSunday = Holiday(
+    "Easter Sunday", month=1, day=1, offset=[Easter(), Day(0)]
+)
+NewYearsDay = Holiday("New Years Day", month=1, day=1)
+SuperBowl = Holiday(
+    "Superbowl", month=2, day=1, offset=DateOffset(weekday=SU(1))
+)
+MothersDay = Holiday(
+    "Mothers Day", month=5, day=1, offset=DateOffset(weekday=SU(2))
+)
+IndependenceDay = Holiday("Independence Day", month=7, day=4)
+ChristmasEve = Holiday("Christmas", month=12, day=24)
+ChristmasDay = Holiday("Christmas", month=12, day=25)
+NewYearsEve = Holiday("New Years Eve", month=12, day=31)
+BlackFriday = Holiday(
+    "Black Friday",
+    month=11,
+    day=1,
+    offset=[pd.DateOffset(weekday=TH(4)), Day(1)],
+)
+CyberMonday = Holiday(
+    "Cyber Monday",
+    month=11,
+    day=1,
+    offset=[pd.DateOffset(weekday=TH(4)), Day(4)],
+)
+
+HOLIDAYS = [
+    EasterMonday,
+    GoodFriday,
+    USColumbusDay,
+    USLaborDay,
+    USMartinLutherKingJr,
+    USMemorialDay,
+    USPresidentsDay,
+    USThanksgivingDay,
+    EasterSunday,
+    NewYearsDay,
+    SuperBowl,
+    MothersDay,
+    IndependenceDay,
+    ChristmasEve,
+    ChristmasDay,
+    NewYearsEve,
+    BlackFriday,
+    CyberMonday,
+]
+
+
+class TimeCovariates(object):
+  """Extract all time covariates except for holidays."""
+
+  def __init__(
+      self,
+      datetimes,
+      normalized=True,
+      holiday=False,
+  ):
+    """Init function.
+
+    Args:
+      datetimes: pandas DatetimeIndex (lowest granularity supported is min)
+      normalized: whether to normalize features or not
+      holiday: fetch holiday features or not
+
+    Returns:
+      None
+    """
+    self.normalized = normalized
+    self.dti = datetimes
+    self.holiday = holiday
+
+  def _minute_of_hour(self):
+    minutes = np.array(self.dti.minute, dtype=np.float32)
+    if self.normalized:
+      minutes = minutes / 59.0 - 0.5
+    return minutes
+
+  def _hour_of_day(self):
+    hours = np.array(self.dti.hour, dtype=np.float32)
+    if self.normalized:
+      hours = hours / 23.0 - 0.5
+    return hours
+
+  def _day_of_week(self):
+    day_week = np.array(self.dti.dayofweek, dtype=np.float32)
+    if self.normalized:
+      day_week = day_week / 6.0 - 0.5
+    return day_week
+
+  def _day_of_month(self):
+    day_month = np.array(self.dti.day, dtype=np.float32)
+    if self.normalized:
+      day_month = day_month / 30.0 - 0.5
+    return day_month
+
+  def _day_of_year(self):
+    day_year = np.array(self.dti.dayofyear, dtype=np.float32)
+    if self.normalized:
+      day_year = day_year / 364.0 - 0.5
+    return day_year
+
+  def _month_of_year(self):
+    month_year = np.array(self.dti.month, dtype=np.float32)
+    if self.normalized:
+      month_year = month_year / 11.0 - 0.5
+    return month_year
+
+  def _week_of_year(self):
+    week_year = np.array(self.dti.strftime("%U").astype(int), dtype=np.float32)
+    if self.normalized:
+      week_year = week_year / 51.0 - 0.5
+    return week_year
+
+  def _get_holidays(self):
+    dti_series = self.dti.to_series()
+    hol_variates = np.vstack([
+        dti_series.apply(_distance_to_holiday(h)).values for h in tqdm(HOLIDAYS)
+    ])
+    # hol_variates is (num_holiday, num_time_steps), the normalization should be
+    # performed in the num_time_steps dimension.
+    return StandardScaler().fit_transform(hol_variates.T).T
+
+  def get_covariates(self):
+    """Get all time covariates."""
+    moh = self._minute_of_hour().reshape(1, -1)
+    hod = self._hour_of_day().reshape(1, -1)
+    dom = self._day_of_month().reshape(1, -1)
+    dow = self._day_of_week().reshape(1, -1)
+    doy = self._day_of_year().reshape(1, -1)
+    moy = self._month_of_year().reshape(1, -1)
+    woy = self._week_of_year().reshape(1, -1)
+
+    all_covs = [
+        moh,
+        hod,
+        dom,
+        dow,
+        doy,
+        moy,
+        woy,
+    ]
+    columns = ["moh", "hod", "dom", "dow", "doy", "moy", "woy"]
+    if self.holiday:
+      hol_covs = self._get_holidays()
+      all_covs.append(hol_covs)
+      columns += [f"hol_{i}" for i in range(len(HOLIDAYS))]
+
+    return pd.DataFrame(
+        data=np.vstack(all_covs).transpose(),
+        columns=columns,
+        index=self.dti,
+    )