diff --git a/tests/cda/timeseries/timeseries_CDA_test.py b/tests/cda/timeseries/timeseries_CDA_test.py
new file mode 100644
index 00000000..42e78d4b
--- /dev/null
+++ b/tests/cda/timeseries/timeseries_CDA_test.py
@@ -0,0 +1,150 @@
+from datetime import datetime, timedelta, timezone
+
+import pandas as pd
+import pytest
+
+import cwms
+import cwms.timeseries.timeseries as ts
+
+TEST_OFFICE = "SPK"
+TEST_LOCATION_ID = "pytest_loc"
+TEST_TS_ID = f"{TEST_LOCATION_ID}.Flow.Inst.1Hour.0.Raw"
+TEST_UNIT = "cfs"
+NOW = datetime.now(timezone.utc)
+START_TIME = NOW - timedelta(hours=2)
+END_TIME = NOW
+TEST_DATA = pd.DataFrame(
+    {
+        "date-time": [
+            START_TIME.isoformat(),
+            (START_TIME + timedelta(hours=1)).isoformat(),
+        ],
+        "value": [65.0, 66.5],
+        "quality-code": [0, 0],
+    }
+)
+
+SECOND_TS_ID = f"{TEST_LOCATION_ID}.Flow.Inst.1Day.0.Raw"
+SECOND_TEST_DATA = pd.DataFrame(
+    {
+        "date-time": [
+            START_TIME.isoformat(),
+            (START_TIME + timedelta(days=1)).isoformat(),
+        ],
+        "value": [72.1, 74.0],
+        "quality-code": [0, 0],
+    }
+)
+
+# Setup and teardown fixture for test location
+
+
+@pytest.fixture(scope="module", autouse=True)
+def setup_data():
+    TEST_LATITUDE = 45.1704758
+    TEST_LONGITUDE = -92.8411439
+
+    location_data = {
+        "name": TEST_LOCATION_ID,
+        "office-id": TEST_OFFICE,
+        "latitude": TEST_LATITUDE,
+        "longitude": TEST_LONGITUDE,
+        "elevation": 250.0,
+        "horizontal-datum": "NAD83",
+        "vertical-datum": "NAVD88",
+        "location-type": "TESTING",
+        "public-name": "Test Location",
+        "long-name": "A pytest-generated location",
+        "timezone-name": "America/Los_Angeles",
+        "location-kind": "SITE",
+        "nation": "US",
+    }
+    # fmt: on
+
+    # Store location before tests
+    cwms.store_location(location_data)
+
+    yield
+
+    # Delete location after tests
+    cwms.delete_location(
+        location_id=TEST_LOCATION_ID, office_id=TEST_OFFICE, cascade_delete=True
+    )
+
+
+@pytest.fixture(autouse=True)
+def init_session(request):
+    print("Initializing CWMS API session for timeseries operations test...")
+
+
+def test_store_timeseries():
+    json_data = ts.timeseries_df_to_json(
+        data=TEST_DATA, ts_id=TEST_TS_ID, units=TEST_UNIT, office_id=TEST_OFFICE
+    )
+    ts.store_timeseries(json_data)
+    df = ts.get_timeseries(TEST_TS_ID, TEST_OFFICE, begin=START_TIME, end=END_TIME).df
+    assert not df.empty
+
+
+def test_update_timeseries_value():
+    updated_data = TEST_DATA.copy()
+    updated_data.loc[1, "value"] = 67.2
+    json_data = ts.timeseries_df_to_json(
+        data=updated_data, ts_id=TEST_TS_ID, units=TEST_UNIT, office_id=TEST_OFFICE
+    )
+    ts.store_timeseries(json_data)
+    df = ts.get_timeseries(TEST_TS_ID, TEST_OFFICE, begin=START_TIME, end=END_TIME).df
+    assert 67.2 in df["value"].values
+
+
+def test_delete_timeseries_data():
+    json_data = ts.timeseries_df_to_json(
+        data=TEST_DATA, ts_id=TEST_TS_ID, units=TEST_UNIT, office_id=TEST_OFFICE
+    )
+    ts.store_timeseries(json_data)
+    ts.delete_timeseries(TEST_TS_ID, TEST_OFFICE, START_TIME, END_TIME)
+    df = ts.get_timeseries(TEST_TS_ID, TEST_OFFICE, begin=START_TIME, end=END_TIME).df
+    assert df.empty
+
+
+def test_get_timeseries():
+    json_data = ts.timeseries_df_to_json(
+        data=TEST_DATA, ts_id=TEST_TS_ID, units=TEST_UNIT, office_id=TEST_OFFICE
+    )
+    ts.store_timeseries(json_data)
+    df = ts.get_timeseries(TEST_TS_ID, TEST_OFFICE, begin=START_TIME, end=END_TIME).df
+    assert df.shape[0] == 2
+    assert 65.0 in df["value"].values and 66.5 in df["value"].values
+
+
+def test_store_multi_timeseries_df():
+    multi_df = pd.concat(
+        [
+            TEST_DATA.assign(ts_id=TEST_TS_ID, units=TEST_UNIT),
+            SECOND_TEST_DATA.assign(ts_id=SECOND_TS_ID, units=TEST_UNIT),
+        ],
+        ignore_index=True,
+    )
+    ts.store_multi_timeseries_df(multi_df, office_id=TEST_OFFICE)
+
+    df1 = ts.get_timeseries(TEST_TS_ID, TEST_OFFICE, begin=START_TIME, end=END_TIME).df
+    df2 = ts.get_timeseries(
+        SECOND_TS_ID, TEST_OFFICE, begin=START_TIME, end=END_TIME
+    ).df
+
+    assert not df1.empty
+    assert not df2.empty
+
+
+def test_get_multi_timeseries_df():
+    combined_df = ts.get_multi_timeseries_df(
+        [TEST_TS_ID, SECOND_TS_ID],
+        office_id=TEST_OFFICE,
+        begin=START_TIME,
+        end=END_TIME,
+        melted=True,
+    )
+    ts_ids = combined_df["ts_id"].unique().tolist()
+    assert TEST_TS_ID in ts_ids
+    assert SECOND_TS_ID in ts_ids
+    assert combined_df.shape[0] >= 4