diff --git a/testing.ipynb b/testing.ipynb
index 64746d7..c74d549 100644
--- a/testing.ipynb
+++ b/testing.ipynb
@@ -478,7 +478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "aead3e77-2670-4541-846a-5537b01f3d2e",
    "metadata": {},
    "outputs": [],
@@ -493,7 +493,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "f287e05f",
    "metadata": {},
    "outputs": [],
@@ -583,34 +583,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 12,
    "id": "c85b5dd9-9a88-4608-ac58-1a141295f63f",
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = sample_data_generator(num=261, frequency=fc.AllFrequencies.W, mu=0.6, sigma=0.7)\n",
+    "data = sample_data_generator(num=261, frequency=fc.AllFrequencies.W)\n",
     "ts = fc.TimeSeries(data, \"W\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 13,
    "id": "0488a4d0-bca1-4341-9fae-1fd254adc0dc",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "TimeSeries([(datetime.datetime(2017, 1, 1, 0, 0), 1040.39),\n",
-       "\t    (datetime.datetime(2017, 1, 8, 0, 0), 1032.83),\n",
-       "\t    (datetime.datetime(2017, 1, 15, 0, 0), 1120.5)\n",
+       "TimeSeries([(datetime.datetime(2017, 1, 1, 0, 0), 1003.03),\n",
+       "\t    (datetime.datetime(2017, 1, 8, 0, 0), 1002.71),\n",
+       "\t    (datetime.datetime(2017, 1, 15, 0, 0), 1008.77)\n",
        "\t    ...\n",
-       "\t    (datetime.datetime(2021, 12, 12, 0, 0), 2007.18),\n",
-       "\t    (datetime.datetime(2021, 12, 19, 0, 0), 1987.49),\n",
-       "\t    (datetime.datetime(2021, 12, 26, 0, 0), 1924.2)], frequency='W')"
+       "\t    (datetime.datetime(2021, 12, 12, 0, 0), 1107.21),\n",
+       "\t    (datetime.datetime(2021, 12, 19, 0, 0), 1106.66),\n",
+       "\t    (datetime.datetime(2021, 12, 26, 0, 0), 1104.32)], frequency='W')"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -621,429 +621,71 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "cd0eb38c",
+   "execution_count": 14,
+   "id": "04624145-4fce-484c-aa69-0d17d159b598",
    "metadata": {},
    "outputs": [],
    "source": [
-    "dates = fc.create_date_series(ts.start_date, ts.end_date, 'M')"
+    "tst = ts.transform('Q', 'mean', False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "69c48512",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prev_date = dates[0]\n",
-    "for i in dates[1:]:\n",
-    "    cur_date = i\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "43fa2254",
+   "execution_count": 16,
+   "id": "75ed1666-5fc8-4707-bf42-62d44adcae18",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "TimeSeries([(datetime.datetime(2017, 1, 8, 0, 0), 1032.83),\n",
-       "\t(datetime.datetime(2017, 1, 15, 0, 0), 1120.5),\n",
-       "\t(datetime.datetime(2017, 1, 22, 0, 0), 1125.86),\n",
-       "\t(datetime.datetime(2017, 1, 29, 0, 0), 1178.74)], frequency='W')"
+       "20"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "ts[(ts.dates < '2017-01-31') & (ts.dates > '2017-01-01')]"
+    "len(tst)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "32a82399-e056-45d6-86a3-b9f0855aed27",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Series([datetime.datetime(2017, 1, 1, 0, 0), datetime.datetime(2017, 2, 1, 0, 0), datetime.datetime(2017, 3, 1, 0, 0), datetime.datetime(2017, 4, 1, 0, 0), datetime.datetime(2017, 5, 1, 0, 0), datetime.datetime(2017, 6, 1, 0, 0), datetime.datetime(2017, 7, 1, 0, 0), datetime.datetime(2017, 8, 1, 0, 0), datetime.datetime(2017, 9, 1, 0, 0), datetime.datetime(2017, 10, 1, 0, 0), datetime.datetime(2017, 11, 1, 0, 0), datetime.datetime(2017, 12, 1, 0, 0), datetime.datetime(2018, 1, 1, 0, 0), datetime.datetime(2018, 2, 1, 0, 0), datetime.datetime(2018, 3, 1, 0, 0), datetime.datetime(2018, 4, 1, 0, 0), datetime.datetime(2018, 5, 1, 0, 0), datetime.datetime(2018, 6, 1, 0, 0), datetime.datetime(2018, 7, 1, 0, 0), datetime.datetime(2018, 8, 1, 0, 0), datetime.datetime(2018, 9, 1, 0, 0), datetime.datetime(2018, 10, 1, 0, 0), datetime.datetime(2018, 11, 1, 0, 0), datetime.datetime(2018, 12, 1, 0, 0), datetime.datetime(2019, 1, 1, 0, 0), datetime.datetime(2019, 2, 1, 0, 0), datetime.datetime(2019, 3, 1, 0, 0), datetime.datetime(2019, 4, 1, 0, 0), datetime.datetime(2019, 5, 1, 0, 0), datetime.datetime(2019, 6, 1, 0, 0), datetime.datetime(2019, 7, 1, 0, 0), datetime.datetime(2019, 8, 1, 0, 0), datetime.datetime(2019, 9, 1, 0, 0), datetime.datetime(2019, 10, 1, 0, 0), datetime.datetime(2019, 11, 1, 0, 0), datetime.datetime(2019, 12, 1, 0, 0), datetime.datetime(2020, 1, 1, 0, 0), datetime.datetime(2020, 2, 1, 0, 0), datetime.datetime(2020, 3, 1, 0, 0), datetime.datetime(2020, 4, 1, 0, 0), datetime.datetime(2020, 5, 1, 0, 0), datetime.datetime(2020, 6, 1, 0, 0), datetime.datetime(2020, 7, 1, 0, 0), datetime.datetime(2020, 8, 1, 0, 0), datetime.datetime(2020, 9, 1, 0, 0), datetime.datetime(2020, 10, 1, 0, 0), datetime.datetime(2020, 11, 1, 0, 0), datetime.datetime(2020, 12, 1, 0, 0), datetime.datetime(2021, 1, 1, 0, 0), datetime.datetime(2021, 2, 1, 0, 0), datetime.datetime(2021, 3, 1, 0, 0), datetime.datetime(2021, 4, 1, 0, 0), datetime.datetime(2021, 5, 1, 0, 0), datetime.datetime(2021, 6, 1, 0, 0), datetime.datetime(2021, 7, 1, 0, 0), datetime.datetime(2021, 8, 1, 0, 0), datetime.datetime(2021, 9, 1, 0, 0), datetime.datetime(2021, 10, 1, 0, 0), datetime.datetime(2021, 11, 1, 0, 0), datetime.datetime(2021, 12, 1, 0, 0)], data_type='datetime')"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "352a71a3-5469-4464-8a93-17f4660822fd",
+   "execution_count": 15,
+   "id": "bccd7d1c-2d57-444c-af68-290f476f2b05",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2017-01-01 00:00:00 1040.39\n",
-      "2017-01-08 00:00:00 1032.83\n",
-      "2017-01-15 00:00:00 1120.5\n",
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-      "2021-08-15 00:00:00 2004.8\n",
-      "2021-08-22 00:00:00 2181.39\n",
-      "2021-08-29 00:00:00 2112.61\n",
-      "2021-09-05 00:00:00 2176.47\n",
-      "2021-09-12 00:00:00 2255.73\n",
-      "2021-09-19 00:00:00 2224.32\n",
-      "2021-09-26 00:00:00 2195.41\n",
-      "2021-10-03 00:00:00 2255.36\n",
-      "2021-10-10 00:00:00 2238.18\n",
-      "2021-10-17 00:00:00 2290.97\n",
-      "2021-10-24 00:00:00 2380.52\n",
-      "2021-10-31 00:00:00 2237.11\n",
-      "2021-11-07 00:00:00 2234.85\n",
-      "2021-11-14 00:00:00 2281.46\n",
-      "2021-11-21 00:00:00 2225.14\n",
-      "2021-11-28 00:00:00 2200.28\n",
-      "2021-12-05 00:00:00 2104.56\n",
-      "2021-12-12 00:00:00 2007.18\n",
-      "2021-12-19 00:00:00 1987.49\n",
-      "2021-12-26 00:00:00 1924.2\n"
+      "(datetime.datetime(2017, 1, 1, 0, 0), 1010.4553846153846)\n",
+      "(datetime.datetime(2017, 4, 1, 0, 0), 1019.34)\n",
+      "(datetime.datetime(2017, 7, 1, 0, 0), 1015.3515384615384)\n",
+      "(datetime.datetime(2017, 10, 1, 0, 0), 1031.2892857142858)\n",
+      "(datetime.datetime(2018, 1, 1, 0, 0), 1054.7216666666666)\n",
+      "(datetime.datetime(2018, 4, 1, 0, 0), 1059.736153846154)\n",
+      "(datetime.datetime(2018, 7, 1, 0, 0), 1049.1100000000001)\n",
+      "(datetime.datetime(2018, 10, 1, 0, 0), 1051.663076923077)\n",
+      "(datetime.datetime(2019, 1, 1, 0, 0), 1062.2869230769231)\n",
+      "(datetime.datetime(2019, 4, 1, 0, 0), 1059.7423076923076)\n",
+      "(datetime.datetime(2019, 7, 1, 0, 0), 1050.7661538461539)\n",
+      "(datetime.datetime(2019, 10, 1, 0, 0), 1045.2061538461537)\n",
+      "(datetime.datetime(2020, 1, 1, 0, 0), 1046.11)\n",
+      "(datetime.datetime(2020, 4, 1, 0, 0), 1053.126923076923)\n",
+      "(datetime.datetime(2020, 7, 1, 0, 0), 1053.273846153846)\n",
+      "(datetime.datetime(2020, 10, 1, 0, 0), 1064.2384615384615)\n",
+      "(datetime.datetime(2021, 1, 1, 0, 0), 1073.1538461538462)\n",
+      "(datetime.datetime(2021, 4, 1, 0, 0), 1094.3215384615385)\n",
+      "(datetime.datetime(2021, 7, 1, 0, 0), 1104.3584615384616)\n",
+      "(datetime.datetime(2021, 10, 1, 0, 0), 1112.806923076923)\n"
      ]
     }
    ],
    "source": [
-    "for i, j in ts:\n",
-    "    print(i, j)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "14c72558-0500-44e4-a893-628a4102ff53",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2017-01-01 00:00:00 1099.664\n",
-      "2017-02-01 00:00:00 1147.25\n",
-      "2017-03-01 00:00:00 1167.5249999999999\n",
-      "2017-04-01 00:00:00 1205.436\n",
-      "2017-05-01 00:00:00 1285.345\n",
-      "2017-06-01 00:00:00 1226.3175\n",
-      "2017-07-01 00:00:00 1119.366\n",
-      "2017-08-01 00:00:00 1096.86\n",
-      "2017-09-01 00:00:00 1173.1475\n",
-      "2017-10-01 00:00:00 1210.18\n",
-      "2017-11-01 00:00:00 1391.4975\n",
-      "2017-12-01 00:00:00 1473.172\n",
-      "2018-01-01 00:00:00 1543.2475\n",
-      "2018-02-01 00:00:00 1841.935\n",
-      "2018-03-01 00:00:00 1994.7649999999999\n",
-      "2018-04-01 00:00:00 1933.944\n",
-      "2018-05-01 00:00:00 1768.365\n",
-      "2018-06-01 00:00:00 1798.7775000000001\n",
-      "2018-07-01 00:00:00 1602.54\n",
-      "2018-08-01 00:00:00 1468.445\n",
-      "2018-09-01 00:00:00 1534.14\n",
-      "2018-10-01 00:00:00 1545.12\n",
-      "2018-11-01 00:00:00 1517.2875\n",
-      "2018-12-01 00:00:00 1534.112\n",
-      "2019-01-01 00:00:00 1689.6924999999999\n",
-      "2019-02-01 00:00:00 1829.31\n",
-      "2019-03-01 00:00:00 1612.344\n",
-      "2019-04-01 00:00:00 1561.4325\n",
-      "2019-05-01 00:00:00 1639.56\n",
-      "2019-06-01 00:00:00 1562.152\n",
-      "2019-07-01 00:00:00 1405.61\n",
-      "2019-08-01 00:00:00 1351.45\n",
-      "2019-09-01 00:00:00 1322.7\n",
-      "2019-10-01 00:00:00 1333.6025\n",
-      "2019-11-01 00:00:00 1166.5925\n",
-      "2019-12-01 00:00:00 1163.202\n",
-      "2020-01-01 00:00:00 1167.065\n",
-      "2020-02-01 00:00:00 1170.555\n",
-      "2020-03-01 00:00:00 1210.952\n",
-      "2020-04-01 00:00:00 1284.5774999999999\n",
-      "2020-05-01 00:00:00 1216.634\n",
-      "2020-06-01 00:00:00 1272.9099999999999\n",
-      "2020-07-01 00:00:00 1272.22\n",
-      "2020-08-01 00:00:00 1161.09\n",
-      "2020-09-01 00:00:00 1217.0974999999999\n",
-      "2020-10-01 00:00:00 1313.04\n",
-      "2020-11-01 00:00:00 1310.506\n",
-      "2020-12-01 00:00:00 1440.78\n",
-      "2021-01-01 00:00:00 1438.026\n",
-      "2021-02-01 00:00:00 1437.3474999999999\n",
-      "2021-03-01 00:00:00 1524.5225\n",
-      "2021-04-01 00:00:00 1764.155\n",
-      "2021-05-01 00:00:00 1830.142\n",
-      "2021-06-01 00:00:00 1981.4175\n",
-      "2021-07-01 00:00:00 1875.5275000000001\n",
-      "2021-08-01 00:00:00 2023.024\n",
-      "2021-09-01 00:00:00 2212.9825\n",
-      "2021-10-01 00:00:00 2280.428\n",
-      "2021-11-01 00:00:00 2235.4325\n"
-     ]
-    }
-   ],
-   "source": [
-    "prev_date = dates[0]\n",
-    "for date in dates[1:]:\n",
-    "    cur_ts = ts[(ts.dates < date) & (ts.dates >= prev_date)]\n",
-    "    print(prev_date, cur_ts.mean())\n",
-    "    prev_date = date"
+    "for i in tst:\n",
+    "    print(i)"
    ]
   }
  ],
@@ -1066,7 +708,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.2"
   }
  },
  "nbformat": 4,