diff --git a/local_environment.yml b/local_environment.yml
index 0a4f5771..ded66892 100644
--- a/local_environment.yml
+++ b/local_environment.yml
@@ -2,7 +2,7 @@ name: mlforecast
 channels:
   - conda-forge
 dependencies:
-  - coreforecast>=0.0.5
+  - fsspec
   - holidays<0.21
   - lightgbm
   - matplotlib
diff --git a/mlforecast/target_transforms.py b/mlforecast/target_transforms.py
index fe79a5d0..af8efe01 100644
--- a/mlforecast/target_transforms.py
+++ b/mlforecast/target_transforms.py
@@ -147,6 +147,11 @@ def stack(scalers: Sequence["Differences"]) -> "Differences":
         core_scaler = first_scaler.scalers_[0]
         diffs = first_scaler.differences
         out = Differences(diffs)
+        out.fitted_ = []
+        for i in range(len(scalers[0].fitted_)):
+            data = np.hstack([sc.fitted_[i].data for sc in scalers])
+            sizes = np.hstack([np.diff(sc.fitted_[i].indptr) for sc in scalers])
+            out.fitted_.append(GroupedArray(data, np.append(0, sizes.cumsum())))
         out.scalers_ = [
             core_scaler.stack([sc.scalers_[i] for sc in scalers])
             for i in range(len(diffs))
diff --git a/nbs/docs/getting-started/quick_start_distributed.ipynb b/nbs/docs/getting-started/quick_start_distributed.ipynb
index 3a9b2bf8..ebc386af 100644
--- a/nbs/docs/getting-started/quick_start_distributed.ipynb
+++ b/nbs/docs/getting-started/quick_start_distributed.ipynb
@@ -278,7 +278,7 @@
     "fcst = DistributedMLForecast(\n",
     "    models=models,\n",
     "    freq='D',\n",
-    "    target_transforms=[Differences([1])],\n",
+    "    target_transforms=[Differences([7])],\n",
     "    lags=[7],\n",
     "    lag_transforms={\n",
     "        1: [ExpandingMean()],\n",
@@ -350,6 +350,7 @@
     "fcst_np = DistributedMLForecast(\n",
     "    models=models,\n",
     "    freq='D',\n",
+    "    target_transforms=[Differences([7])],    \n",
     "    lags=[7],\n",
     "    lag_transforms={\n",
     "        1: [ExpandingMean()],\n",
@@ -424,36 +425,36 @@
        "      <th>0</th>\n",
        "      <td>id_00</td>\n",
        "      <td>2002-09-27</td>\n",
-       "      <td>3.828538</td>\n",
-       "      <td>3.519942</td>\n",
+       "      <td>20.999371</td>\n",
+       "      <td>21.892795</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>id_00</td>\n",
        "      <td>2002-09-28</td>\n",
-       "      <td>91.890763</td>\n",
-       "      <td>84.406499</td>\n",
+       "      <td>84.771692</td>\n",
+       "      <td>83.002009</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>id_00</td>\n",
        "      <td>2002-09-29</td>\n",
-       "      <td>165.303125</td>\n",
-       "      <td>157.733402</td>\n",
+       "      <td>162.389419</td>\n",
+       "      <td>163.528475</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>id_00</td>\n",
        "      <td>2002-09-30</td>\n",
-       "      <td>243.340097</td>\n",
-       "      <td>231.514761</td>\n",
+       "      <td>245.002456</td>\n",
+       "      <td>245.472042</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>id_00</td>\n",
        "      <td>2002-10-01</td>\n",
-       "      <td>314.397042</td>\n",
-       "      <td>305.351770</td>\n",
+       "      <td>317.240952</td>\n",
+       "      <td>313.948840</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -461,11 +462,11 @@
       ],
       "text/plain": [
        "  unique_id         ds  DaskXGBForecast  DaskLGBMForecast\n",
-       "0     id_00 2002-09-27         3.828538          3.519942\n",
-       "1     id_00 2002-09-28        91.890763         84.406499\n",
-       "2     id_00 2002-09-29       165.303125        157.733402\n",
-       "3     id_00 2002-09-30       243.340097        231.514761\n",
-       "4     id_00 2002-10-01       314.397042        305.351770"
+       "0     id_00 2002-09-27        20.999371         21.892795\n",
+       "1     id_00 2002-09-28        84.771692         83.002009\n",
+       "2     id_00 2002-09-29       162.389419        163.528475\n",
+       "3     id_00 2002-09-30       245.002456        245.472042\n",
+       "4     id_00 2002-10-01       317.240952        313.948840"
       ]
      },
      "execution_count": null,
@@ -510,6 +511,7 @@
     "fcst_exog = DistributedMLForecast(\n",
     "    models=models,\n",
     "    freq='D',\n",
+    "    target_transforms=[Differences([7])],    \n",
     "    lags=[7],\n",
     "    lag_transforms={\n",
     "        1: [ExpandingMean()],\n",
@@ -627,7 +629,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "preds = fa.as_pandas(fcst.predict(10)).sort_values(['unique_id', 'ds']).reset_index(drop=True)\n",
     "local_fcst = fcst.to_local()\n",
     "local_preds = local_fcst.predict(10)\n",
     "# we don't check the dtype because sometimes these are arrow dtypes\n",
@@ -663,7 +664,107 @@
    "execution_count": null,
    "id": "07e80450-d582-42bb-8bf4-ff925d5e74e1",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>unique_id</th>\n",
+       "      <th>ds</th>\n",
+       "      <th>DaskXGBForecast</th>\n",
+       "      <th>DaskLGBMForecast</th>\n",
+       "      <th>cutoff</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>id_00</td>\n",
+       "      <td>2002-08-16</td>\n",
+       "      <td>22.706938</td>\n",
+       "      <td>21.967568</td>\n",
+       "      <td>2002-08-15</td>\n",
+       "      <td>11.878591</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>id_00</td>\n",
+       "      <td>2002-08-17</td>\n",
+       "      <td>95.885948</td>\n",
+       "      <td>98.285482</td>\n",
+       "      <td>2002-08-15</td>\n",
+       "      <td>75.108162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>id_00</td>\n",
+       "      <td>2002-08-18</td>\n",
+       "      <td>172.546631</td>\n",
+       "      <td>171.527272</td>\n",
+       "      <td>2002-08-15</td>\n",
+       "      <td>175.278407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>id_00</td>\n",
+       "      <td>2002-08-19</td>\n",
+       "      <td>238.256594</td>\n",
+       "      <td>238.375726</td>\n",
+       "      <td>2002-08-15</td>\n",
+       "      <td>226.062025</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>id_00</td>\n",
+       "      <td>2002-08-20</td>\n",
+       "      <td>306.005923</td>\n",
+       "      <td>305.146636</td>\n",
+       "      <td>2002-08-15</td>\n",
+       "      <td>318.433401</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  unique_id         ds  DaskXGBForecast  DaskLGBMForecast     cutoff  \\\n",
+       "0     id_00 2002-08-16        22.706938         21.967568 2002-08-15   \n",
+       "1     id_00 2002-08-17        95.885948         98.285482 2002-08-15   \n",
+       "2     id_00 2002-08-18       172.546631        171.527272 2002-08-15   \n",
+       "3     id_00 2002-08-19       238.256594        238.375726 2002-08-15   \n",
+       "4     id_00 2002-08-20       306.005923        305.146636 2002-08-15   \n",
+       "\n",
+       "            y  \n",
+       "0   11.878591  \n",
+       "1   75.108162  \n",
+       "2  175.278407  \n",
+       "3  226.062025  \n",
+       "4  318.433401  "
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cv_res.compute().head()"
    ]
@@ -743,6 +844,7 @@
     "non_std_series = non_std_series.rename(columns={'ds': 'time', 'y': 'value', 'unique_id': 'some_id'})\n",
     "flow_params = dict(\n",
     "    models=[DaskXGBForecast(random_state=0)],\n",
+    "    target_transforms=[Differences([7])],    \n",
     "    lags=[7],\n",
     "    lag_transforms={\n",
     "        1: [ExpandingMean()],\n",
@@ -865,7 +967,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "models = [SparkLGBMForecast(), SparkXGBForecast()]"
+    "models = [SparkLGBMForecast(seed=0), SparkXGBForecast(random_state=0)]"
    ]
   },
   {
@@ -886,6 +988,7 @@
     "fcst = DistributedMLForecast(\n",
     "    models,\n",
     "    freq='D',\n",
+    "    target_transforms=[Differences([7])],    \n",
     "    lags=[1],\n",
     "    lag_transforms={\n",
     "        1: [ExpandingMean()],\n",
@@ -922,7 +1025,7 @@
     "num_partitions_test = 10\n",
     "fcst_np = DistributedMLForecast(\n",
     "    models=models,\n",
-    "    freq='D',\n",
+    "    freq='D',    \n",
     "    lags=[7],\n",
     "    lag_transforms={\n",
     "        1: [ExpandingMean()],\n",
@@ -965,7 +1068,88 @@
    "execution_count": null,
    "id": "5da6bbd2-9806-4b12-91a2-1b5571ae1550",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>unique_id</th>\n",
+       "      <th>ds</th>\n",
+       "      <th>SparkLGBMForecast</th>\n",
+       "      <th>SparkXGBForecast</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>id_00</td>\n",
+       "      <td>2001-05-15</td>\n",
+       "      <td>431.677682</td>\n",
+       "      <td>424.488985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>id_00</td>\n",
+       "      <td>2001-05-16</td>\n",
+       "      <td>503.673189</td>\n",
+       "      <td>502.923172</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>id_00</td>\n",
+       "      <td>2001-05-17</td>\n",
+       "      <td>8.150285</td>\n",
+       "      <td>8.019412</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>id_00</td>\n",
+       "      <td>2001-05-18</td>\n",
+       "      <td>97.620923</td>\n",
+       "      <td>97.031792</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>id_00</td>\n",
+       "      <td>2001-05-19</td>\n",
+       "      <td>194.568960</td>\n",
+       "      <td>193.862475</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  unique_id         ds  SparkLGBMForecast  SparkXGBForecast\n",
+       "0     id_00 2001-05-15         431.677682        424.488985\n",
+       "1     id_00 2001-05-16         503.673189        502.923172\n",
+       "2     id_00 2001-05-17           8.150285          8.019412\n",
+       "3     id_00 2001-05-18          97.620923         97.031792\n",
+       "4     id_00 2001-05-19         194.568960        193.862475"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "preds.head()"
    ]
@@ -993,7 +1177,15 @@
    "execution_count": null,
    "id": "3a1bff80-c052-4a24-a225-33b774d7d75a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
    "source": [
     "save_dir = build_unique_name('spark')\n",
     "save_path = f's3://nixtla-tmp/mlf/{save_dir}'\n",
@@ -1005,7 +1197,7 @@
    "id": "9bd4a6de-1c34-4112-bb18-2818b808b8eb",
    "metadata": {},
    "source": [
-    "Once you've saved your forecast object you can then load it back by specifying the path where it was saved along with an engine, which will be used to perform the distributed computations (in this case the dask client)."
+    "Once you've saved your forecast object you can then load it back by specifying the path where it was saved along with an engine, which will be used to perform the distributed computations (in this case the spark session)."
    ]
   },
   {
@@ -1013,7 +1205,15 @@
    "execution_count": null,
    "id": "6191d277-ce70-464b-a44f-0332a96ec7c6",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
    "source": [
     "fcst2 = DistributedMLForecast.load(save_path, engine=spark)"
    ]
@@ -1089,7 +1289,107 @@
    "execution_count": null,
    "id": "0c735385-7104-4ced-a253-d4a16b8bbb4e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>unique_id</th>\n",
+       "      <th>ds</th>\n",
+       "      <th>SparkLGBMForecast</th>\n",
+       "      <th>SparkXGBForecast</th>\n",
+       "      <th>cutoff</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>id_12</td>\n",
+       "      <td>2001-04-03</td>\n",
+       "      <td>342.978379</td>\n",
+       "      <td>341.930127</td>\n",
+       "      <td>2001-04-02</td>\n",
+       "      <td>328.907629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>id_23</td>\n",
+       "      <td>2001-04-03</td>\n",
+       "      <td>429.591043</td>\n",
+       "      <td>428.320398</td>\n",
+       "      <td>2001-04-02</td>\n",
+       "      <td>424.716749</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>id_26</td>\n",
+       "      <td>2001-04-10</td>\n",
+       "      <td>7.554284</td>\n",
+       "      <td>7.707686</td>\n",
+       "      <td>2001-04-02</td>\n",
+       "      <td>19.814264</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>id_18</td>\n",
+       "      <td>2001-04-11</td>\n",
+       "      <td>98.885044</td>\n",
+       "      <td>98.848126</td>\n",
+       "      <td>2001-04-02</td>\n",
+       "      <td>98.877898</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>id_00</td>\n",
+       "      <td>2001-04-13</td>\n",
+       "      <td>122.727000</td>\n",
+       "      <td>117.713487</td>\n",
+       "      <td>2001-04-02</td>\n",
+       "      <td>98.526008</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  unique_id         ds  SparkLGBMForecast  SparkXGBForecast     cutoff  \\\n",
+       "0     id_12 2001-04-03         342.978379        341.930127 2001-04-02   \n",
+       "1     id_23 2001-04-03         429.591043        428.320398 2001-04-02   \n",
+       "2     id_26 2001-04-10           7.554284          7.707686 2001-04-02   \n",
+       "3     id_18 2001-04-11          98.885044         98.848126 2001-04-02   \n",
+       "4     id_00 2001-04-13         122.727000        117.713487 2001-04-02   \n",
+       "\n",
+       "            y  \n",
+       "0  328.907629  \n",
+       "1  424.716749  \n",
+       "2   19.814264  \n",
+       "3   98.877898  \n",
+       "4   98.526008  "
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cv_res.head()"
    ]
@@ -1198,10 +1498,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "models = [\n",
-    "    RayLGBMForecast(),\n",
-    "    RayXGBForecast(),\n",
-    "]"
+    "models = [RayLGBMForecast(random_state=0), RayXGBForecast(random_state=0)]"
    ]
   },
   {
@@ -1240,6 +1537,7 @@
     "fcst = DistributedMLForecast(\n",
     "    models,\n",
     "    freq='D',\n",
+    "    target_transforms=[Differences([7])],\n",
     "    lags=[1],\n",
     "    lag_transforms={\n",
     "        1: [ExpandingMean()],\n",
@@ -1322,7 +1620,88 @@
    "execution_count": null,
    "id": "962ee74e-1346-4991-bcf2-5b2a887eb5b0",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>unique_id</th>\n",
+       "      <th>ds</th>\n",
+       "      <th>RayLGBMForecast</th>\n",
+       "      <th>RayXGBForecast</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>id_00</td>\n",
+       "      <td>2001-05-15</td>\n",
+       "      <td>431.677682</td>\n",
+       "      <td>427.262462</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>id_00</td>\n",
+       "      <td>2001-05-16</td>\n",
+       "      <td>503.673189</td>\n",
+       "      <td>502.605670</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>id_00</td>\n",
+       "      <td>2001-05-17</td>\n",
+       "      <td>8.150285</td>\n",
+       "      <td>7.604773</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>id_00</td>\n",
+       "      <td>2001-05-18</td>\n",
+       "      <td>97.620923</td>\n",
+       "      <td>97.582869</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>id_00</td>\n",
+       "      <td>2001-05-19</td>\n",
+       "      <td>194.568960</td>\n",
+       "      <td>192.818578</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  unique_id         ds  RayLGBMForecast  RayXGBForecast\n",
+       "0     id_00 2001-05-15       431.677682      427.262462\n",
+       "1     id_00 2001-05-16       503.673189      502.605670\n",
+       "2     id_00 2001-05-17         8.150285        7.604773\n",
+       "3     id_00 2001-05-18        97.620923       97.582869\n",
+       "4     id_00 2001-05-19       194.568960      192.818578"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "preds.head()"
    ]
@@ -1362,7 +1741,7 @@
    "id": "fcce44d0-3035-4919-830a-d45a3d47d9b1",
    "metadata": {},
    "source": [
-    "Once you've saved your forecast object you can then load it back by specifying the path where it was saved along with an engine, which will be used to perform the distributed computations (in this case the dask client)."
+    "Once you've saved your forecast object you can then load it back by specifying the path where it was saved along with an engine, which will be used to perform the distributed computations (in this case the 'ray' string)."
    ]
   },
   {
@@ -1446,7 +1825,100 @@
    "execution_count": null,
    "id": "d9ecc467-80c7-4c0e-98d6-af77c9fc7fe8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>unique_id</th>\n",
+       "      <th>ds</th>\n",
+       "      <th>RayLGBMForecast</th>\n",
+       "      <th>RayXGBForecast</th>\n",
+       "      <th>cutoff</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>id_10</td>\n",
+       "      <td>2001-05-01</td>\n",
+       "      <td>24.962461</td>\n",
+       "      <td>22.998615</td>\n",
+       "      <td>2001-04-30</td>\n",
+       "      <td>31.878545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>id_10</td>\n",
+       "      <td>2001-05-02</td>\n",
+       "      <td>53.219645</td>\n",
+       "      <td>54.298105</td>\n",
+       "      <td>2001-04-30</td>\n",
+       "      <td>48.349363</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>id_10</td>\n",
+       "      <td>2001-05-03</td>\n",
+       "      <td>78.068732</td>\n",
+       "      <td>76.111907</td>\n",
+       "      <td>2001-04-30</td>\n",
+       "      <td>71.607111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>id_10</td>\n",
+       "      <td>2001-05-04</td>\n",
+       "      <td>103.153889</td>\n",
+       "      <td>104.344135</td>\n",
+       "      <td>2001-04-30</td>\n",
+       "      <td>103.482107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>id_10</td>\n",
+       "      <td>2001-05-05</td>\n",
+       "      <td>116.708231</td>\n",
+       "      <td>115.950523</td>\n",
+       "      <td>2001-04-30</td>\n",
+       "      <td>124.719690</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  unique_id         ds  RayLGBMForecast  RayXGBForecast     cutoff           y\n",
+       "0     id_10 2001-05-01        24.962461       22.998615 2001-04-30   31.878545\n",
+       "1     id_10 2001-05-02        53.219645       54.298105 2001-04-30   48.349363\n",
+       "2     id_10 2001-05-03        78.068732       76.111907 2001-04-30   71.607111\n",
+       "3     id_10 2001-05-04       103.153889      104.344135 2001-04-30  103.482107\n",
+       "4     id_10 2001-05-05       116.708231      115.950523 2001-04-30  124.719690"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cv_res.head()"
    ]
diff --git a/nbs/target_transforms.ipynb b/nbs/target_transforms.ipynb
index 26221d6f..8975bca5 100644
--- a/nbs/target_transforms.ipynb
+++ b/nbs/target_transforms.ipynb
@@ -221,6 +221,11 @@
     "        core_scaler = first_scaler.scalers_[0]\n",
     "        diffs = first_scaler.differences\n",
     "        out = Differences(diffs)\n",
+    "        out.fitted_ = []\n",
+    "        for i in range(len(scalers[0].fitted_)):\n",
+    "            data = np.hstack([sc.fitted_[i].data for sc in scalers])\n",
+    "            sizes = np.hstack([np.diff(sc.fitted_[i].indptr) for sc in scalers])\n",
+    "            out.fitted_.append(GroupedArray(data, np.append(0, sizes.cumsum())))\n",
     "        out.scalers_ = [\n",
     "            core_scaler.stack([sc.scalers_[i] for sc in scalers])\n",
     "            for i in range(len(diffs))\n",
diff --git a/settings.ini b/settings.ini
index 6eb44dad..17c5b1c3 100644
--- a/settings.ini
+++ b/settings.ini
@@ -15,11 +15,10 @@ language = English
 custom_sidebar = True
 license = apache2
 status = 3
-requirements = cloudpickle coreforecast>=0.0.5 fsspec numba packaging pandas scikit-learn utilsforecast>=0.0.27 window-ops
+requirements = cloudpickle coreforecast>=0.0.7 fsspec numba packaging pandas scikit-learn utilsforecast>=0.0.27 window-ops
 dask_requirements = fugue dask[complete] lightgbm xgboost
 ray_requirements = fugue[ray] lightgbm_ray xgboost_ray
 spark_requirements = fugue pyspark>=3.3 lightgbm xgboost
-lag_tfms_requirements = coreforecast>=0.0.5
 aws_requirements = fsspec[s3]
 gcp_requirements = fsspec[gcs]
 azure_requirements = fsspec[adl]
diff --git a/setup.py b/setup.py
index 33bf3c83..342cc7fa 100644
--- a/setup.py
+++ b/setup.py
@@ -61,7 +61,7 @@
         'dask': dask_requirements,
         'ray': ray_requirements,
         'spark': spark_requirements,
-        'lag_transforms': lag_tfms_requirements,
+        'lag_transforms': [],
         'aws': aws_requirements,
         'azure': azure_requirements,
         'gcp': gcp_requirements,
diff --git a/tests/test_m4.py b/tests/test_m4.py
index 3e320987..853b4397 100644
--- a/tests/test_m4.py
+++ b/tests/test_m4.py
@@ -68,9 +68,9 @@
         },
         "metrics": {
             "lgb": {
-                "SMAPE": 2.984803,
-                "MASE": 3.202900,
-                "OWA": 0.978585,
+                "SMAPE": 2.984652,
+                "MASE": 3.205519,
+                "OWA": 0.978931,
             },
             "enet": {
                 "SMAPE": 2.989625,