diff --git a/mlforecast/distributed/forecast.py b/mlforecast/distributed/forecast.py
index e52f015f..401a1cde 100644
--- a/mlforecast/distributed/forecast.py
+++ b/mlforecast/distributed/forecast.py
@@ -195,7 +195,7 @@ def _preprocess_partition(
             train = part[train_mask]
             valid_keep_cols = part.columns
             if static_features is not None:
-                valid_keep_cols.drop(static_features)
+                valid_keep_cols = valid_keep_cols.drop(static_features)
             valid = part.loc[valid_mask, valid_keep_cols].merge(cutoffs, on=id_col)
         transformed = ts.fit_transform(
             train,
@@ -456,6 +456,8 @@ def _predict(
     ) -> Iterable[pd.DataFrame]:
         for serialized_ts, _, serialized_valid in items:
             valid = cloudpickle.loads(serialized_valid)
+            if valid is not None:
+                X_df = valid
             ts = cloudpickle.loads(serialized_ts)
             res = ts.predict(
                 models=models,
@@ -649,7 +651,11 @@ def cross_validation(
                 engine=self.engine,
             )
             results.append(fa.get_native_as_df(preds))
-        return fa.union(*results)
+        if len(results) == 1:
+            res = results[0]
+        else:
+            res = fa.union(*results)
+        return res
 
     @staticmethod
     def _save_ts(items: List[List[Any]], path: str) -> Iterable[pd.DataFrame]:
diff --git a/nbs/distributed.forecast.ipynb b/nbs/distributed.forecast.ipynb
index 4540ec20..1ccd9418 100644
--- a/nbs/distributed.forecast.ipynb
+++ b/nbs/distributed.forecast.ipynb
@@ -261,7 +261,7 @@
     "            train = part[train_mask]\n",
     "            valid_keep_cols = part.columns\n",
     "            if static_features is not None:\n",
-    "                valid_keep_cols.drop(static_features)\n",
+    "                valid_keep_cols = valid_keep_cols.drop(static_features)\n",
     "            valid = part.loc[valid_mask, valid_keep_cols].merge(cutoffs, on=id_col)\n",
     "        transformed = ts.fit_transform(\n",
     "            train,\n",
@@ -508,6 +508,8 @@
     "    ) -> Iterable[pd.DataFrame]:\n",
     "        for serialized_ts, _, serialized_valid in items:\n",
     "            valid = cloudpickle.loads(serialized_valid)\n",
+    "            if valid is not None:\n",
+    "                X_df = valid\n",
     "            ts = cloudpickle.loads(serialized_ts)\n",
     "            res = ts.predict(\n",
     "                models=models,\n",
@@ -695,7 +697,11 @@
     "                engine=self.engine,\n",
     "            )\n",
     "            results.append(fa.get_native_as_df(preds))\n",
-    "        return fa.union(*results)\n",
+    "        if len(results) == 1:\n",
+    "            res = results[0]\n",
+    "        else:\n",
+    "            res = fa.union(*results)\n",
+    "        return res\n",
     "\n",
     "    @staticmethod\n",
     "    def _save_ts(items: List[List[Any]], path: str) -> Iterable[pd.DataFrame]:\n",
diff --git a/nbs/docs/getting-started/quick_start_distributed.ipynb b/nbs/docs/getting-started/quick_start_distributed.ipynb
index 87e02a05..6f355810 100644
--- a/nbs/docs/getting-started/quick_start_distributed.ipynb
+++ b/nbs/docs/getting-started/quick_start_distributed.ipynb
@@ -53,6 +53,7 @@
     "import pandas as pd\n",
     "import s3fs\n",
     "from sklearn.base import BaseEstimator\n",
+    "from utilsforecast.feature_engineering import fourier\n",
     "\n",
     "from mlforecast.distributed import DistributedMLForecast\n",
     "from mlforecast.lag_transforms import ExpandingMean, ExponentiallyWeightedMean, RollingMean\n",
@@ -136,6 +137,10 @@
        "      <th>y</th>\n",
        "      <th>static_0</th>\n",
        "      <th>static_1</th>\n",
+       "      <th>sin1_7</th>\n",
+       "      <th>sin2_7</th>\n",
+       "      <th>cos1_7</th>\n",
+       "      <th>cos2_7</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>npartitions=10</th>\n",
@@ -144,6 +149,10 @@
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -154,6 +163,10 @@
        "      <td>float64</td>\n",
        "      <td>int64</td>\n",
        "      <td>int64</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>float32</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>id_10</th>\n",
@@ -162,6 +175,10 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -170,6 +187,10 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>id_90</th>\n",
@@ -178,6 +199,10 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>id_99</th>\n",
@@ -186,6 +211,10 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -193,13 +222,13 @@
       ],
       "text/plain": [
        "Dask DataFrame Structure:\n",
-       "               unique_id              ds        y static_0 static_1\n",
-       "npartitions=10                                                     \n",
-       "id_00             object  datetime64[ns]  float64    int64    int64\n",
-       "id_10                ...             ...      ...      ...      ...\n",
-       "...                  ...             ...      ...      ...      ...\n",
-       "id_90                ...             ...      ...      ...      ...\n",
-       "id_99                ...             ...      ...      ...      ...\n",
+       "               unique_id              ds        y static_0 static_1   sin1_7   sin2_7   cos1_7   cos2_7\n",
+       "npartitions=10                                                                                         \n",
+       "id_00             object  datetime64[ns]  float64    int64    int64  float32  float32  float32  float32\n",
+       "id_10                ...             ...      ...      ...      ...      ...      ...      ...      ...\n",
+       "...                  ...             ...      ...      ...      ...      ...      ...      ...      ...\n",
+       "id_90                ...             ...      ...      ...      ...      ...      ...      ...      ...\n",
+       "id_99                ...             ...      ...      ...      ...      ...      ...      ...      ...\n",
        "Dask Name: assign, 5 expressions\n",
        "Expr=Assign(frame=MapPartitions(lambda))"
       ]
@@ -211,8 +240,9 @@
    ],
    "source": [
     "series = generate_daily_series(100, n_static_features=2, equal_ends=True, static_as_categorical=False, min_length=500, max_length=1_000)\n",
+    "train, future = fourier(series, freq='d', season_length=7, k=2, h=7)\n",
     "npartitions = 10\n",
-    "partitioned_series = dd.from_pandas(series.set_index('unique_id'), npartitions=npartitions)  # make sure we split by the id_col\n",
+    "partitioned_series = dd.from_pandas(train.set_index('unique_id'), npartitions=npartitions)  # make sure we split by the id_col\n",
     "partitioned_series = partitioned_series.map_partitions(lambda df: df.reset_index())\n",
     "partitioned_series['unique_id'] = partitioned_series['unique_id'].astype(str)  # can't handle categoricals atm\n",
     "partitioned_series"
@@ -245,7 +275,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "models = [DaskXGBForecast(random_state=0), DaskLGBMForecast(random_state=0)]"
+    "models = [\n",
+    "    DaskXGBForecast(random_state=0),\n",
+    "    DaskLGBMForecast(random_state=0, verbosity=-1),\n",
+    "]"
    ]
   },
   {
@@ -277,7 +310,7 @@
     "    num_threads=1,\n",
     "    engine=client,\n",
     ")\n",
-    "fcst.fit(partitioned_series)"
+    "fcst.fit(partitioned_series, static_features=['static_0', 'static_1'])"
    ]
   },
   {
@@ -354,7 +387,7 @@
     "    fcst_np.fit(test_dd)\n",
     "    test_partition_results_size(fcst_np, num_partitions_test)\n",
     "    preds_np = fcst_np.predict(7).compute().sort_values(['unique_id', 'ds']).reset_index(drop=True)\n",
-    "    preds = fcst.predict(7).compute().sort_values(['unique_id', 'ds']).reset_index(drop=True)\n",
+    "    preds = fcst.predict(7, X_df=future).compute().sort_values(['unique_id', 'ds']).reset_index(drop=True)\n",
     "    pd.testing.assert_frame_equal(\n",
     "        preds[['unique_id', 'ds']], \n",
     "        preds_np[['unique_id', 'ds']], \n",
@@ -415,36 +448,36 @@
        "      <th>0</th>\n",
        "      <td>id_00</td>\n",
        "      <td>2002-09-27 00:00:00</td>\n",
-       "      <td>22.489947</td>\n",
-       "      <td>21.679944</td>\n",
+       "      <td>21.609526</td>\n",
+       "      <td>22.114111</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>id_00</td>\n",
        "      <td>2002-09-28 00:00:00</td>\n",
-       "      <td>81.806826</td>\n",
-       "      <td>84.151205</td>\n",
+       "      <td>85.623013</td>\n",
+       "      <td>84.309696</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>id_00</td>\n",
        "      <td>2002-09-29 00:00:00</td>\n",
-       "      <td>162.705641</td>\n",
-       "      <td>164.024508</td>\n",
+       "      <td>163.107685</td>\n",
+       "      <td>163.20679</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>id_00</td>\n",
        "      <td>2002-09-30 00:00:00</td>\n",
-       "      <td>246.990386</td>\n",
-       "      <td>246.099977</td>\n",
+       "      <td>246.96872</td>\n",
+       "      <td>245.510858</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>id_00</td>\n",
        "      <td>2002-10-01 00:00:00</td>\n",
-       "      <td>314.741463</td>\n",
-       "      <td>315.261537</td>\n",
+       "      <td>318.521367</td>\n",
+       "      <td>314.479718</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -452,11 +485,11 @@
       ],
       "text/plain": [
        "  unique_id                   ds  DaskXGBForecast  DaskLGBMForecast\n",
-       "0     id_00  2002-09-27 00:00:00        22.489947         21.679944\n",
-       "1     id_00  2002-09-28 00:00:00        81.806826         84.151205\n",
-       "2     id_00  2002-09-29 00:00:00       162.705641        164.024508\n",
-       "3     id_00  2002-09-30 00:00:00       246.990386        246.099977\n",
-       "4     id_00  2002-10-01 00:00:00       314.741463        315.261537"
+       "0     id_00  2002-09-27 00:00:00        21.609526         22.114111\n",
+       "1     id_00  2002-09-28 00:00:00        85.623013         84.309696\n",
+       "2     id_00  2002-09-29 00:00:00       163.107685         163.20679\n",
+       "3     id_00  2002-09-30 00:00:00        246.96872        245.510858\n",
+       "4     id_00  2002-10-01 00:00:00       318.521367        314.479718"
       ]
      },
      "execution_count": null,
@@ -465,7 +498,7 @@
     }
    ],
    "source": [
-    "preds = fcst.predict(7).compute()\n",
+    "preds = fcst.predict(7, X_df=future).compute()\n",
     "preds.head()"
    ]
   },
@@ -477,8 +510,8 @@
    "outputs": [],
    "source": [
     "#|hide\n",
-    "preds2 = fcst.predict(7).compute()\n",
-    "preds3 = fcst.predict(7, new_df=partitioned_series).compute()\n",
+    "preds2 = fcst.predict(7, X_df=future).compute()\n",
+    "preds3 = fcst.predict(7, new_df=partitioned_series, X_df=future).compute()\n",
     "pd.testing.assert_frame_equal(preds, preds2)\n",
     "pd.testing.assert_frame_equal(preds, preds3)"
    ]
@@ -604,8 +637,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "preds = fa.as_pandas(fcst.predict(10)).sort_values(['unique_id', 'ds']).reset_index(drop=True)\n",
-    "preds2 = fa.as_pandas(fcst2.predict(10)).sort_values(['unique_id', 'ds']).reset_index(drop=True)\n",
+    "preds = fa.as_pandas(fcst.predict(7, X_df=future)).sort_values(['unique_id', 'ds']).reset_index(drop=True)\n",
+    "preds2 = fa.as_pandas(fcst2.predict(7, X_df=future)).sort_values(['unique_id', 'ds']).reset_index(drop=True)\n",
     "pd.testing.assert_frame_equal(preds, preds2)"
    ]
   },
@@ -635,7 +668,7 @@
     "    num_threads=1,\n",
     "    engine=client,\n",
     ")\n",
-    "upd_fcst.fit(partitioned_series)\n",
+    "upd_fcst.fit(partitioned_series, static_features=['static_0', 'static_1'])\n",
     "\n",
     "new_df = (series.groupby('unique_id', observed=True)['ds'].max() + pd.offsets.Day()).reset_index()\n",
     "new_df['y'] = -1.0\n",
@@ -643,7 +676,7 @@
     "expected = new_df.rename(columns={'y': 'Lag1Model'})\n",
     "expected = expected.astype({'unique_id': str})\n",
     "expected['ds'] += pd.offsets.Day()\n",
-    "upd_preds = upd_fcst.predict(1).compute()\n",
+    "upd_preds = upd_fcst.predict(1, X_df=future).compute()\n",
     "pd.testing.assert_frame_equal(\n",
     "    upd_preds.reset_index(drop=True),\n",
     "    expected.reset_index(drop=True),\n",
@@ -669,7 +702,7 @@
    "outputs": [],
    "source": [
     "local_fcst = fcst.to_local()\n",
-    "local_preds = local_fcst.predict(10)\n",
+    "local_preds = local_fcst.predict(7, X_df=future)\n",
     "# we don't check the dtype because sometimes these are arrow dtypes\n",
     "# or different precisions of float\n",
     "pd.testing.assert_frame_equal(preds, local_preds, check_dtype=False)"
@@ -686,7 +719,7 @@
     "# test to_local without target transforms\n",
     "fcst_no_targ_tfms = DistributedMLForecast(\n",
     "    models=[DaskXGBForecast(n_estimators=5, random_state=0)],\n",
-    "    freq='D',    \n",
+    "    freq='D',\n",
     "    lags=[1],\n",
     "    lag_transforms={1: [ExpandingMean()]},\n",
     "    date_features=['dayofweek'],\n",
@@ -718,6 +751,7 @@
     "    partitioned_series,\n",
     "    n_windows=3,\n",
     "    h=14,\n",
+    "    static_features=['static_0', 'static_1'],\n",
     ")"
    ]
   },
@@ -758,68 +792,68 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>id_01</td>\n",
-       "      <td>2002-08-19 00:00:00</td>\n",
-       "      <td>224.458336</td>\n",
-       "      <td>222.742605</td>\n",
+       "      <th>0</th>\n",
+       "      <td>id_00</td>\n",
+       "      <td>2002-08-16 00:00:00</td>\n",
+       "      <td>23.192749</td>\n",
+       "      <td>21.986437</td>\n",
        "      <td>2002-08-15 00:00:00</td>\n",
-       "      <td>210.723139</td>\n",
+       "      <td>11.878591</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>43</th>\n",
-       "      <td>id_03</td>\n",
-       "      <td>2002-08-17 00:00:00</td>\n",
-       "      <td>2.235601</td>\n",
-       "      <td>2.210624</td>\n",
+       "      <th>30</th>\n",
+       "      <td>id_02</td>\n",
+       "      <td>2002-08-18 00:00:00</td>\n",
+       "      <td>96.59974</td>\n",
+       "      <td>96.568057</td>\n",
        "      <td>2002-08-15 00:00:00</td>\n",
-       "      <td>2.416967</td>\n",
+       "      <td>94.706551</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>id_03</td>\n",
-       "      <td>2002-08-18 00:00:00</td>\n",
-       "      <td>3.276747</td>\n",
-       "      <td>3.239702</td>\n",
+       "      <th>80</th>\n",
+       "      <td>id_05</td>\n",
+       "      <td>2002-08-26 00:00:00</td>\n",
+       "      <td>257.210466</td>\n",
+       "      <td>255.908309</td>\n",
        "      <td>2002-08-15 00:00:00</td>\n",
-       "      <td>3.060194</td>\n",
+       "      <td>246.051086</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>119</th>\n",
-       "      <td>id_08</td>\n",
-       "      <td>2002-08-23 00:00:00</td>\n",
-       "      <td>131.261689</td>\n",
-       "      <td>131.180289</td>\n",
+       "      <th>36</th>\n",
+       "      <td>id_12</td>\n",
+       "      <td>2002-08-24 00:00:00</td>\n",
+       "      <td>401.081335</td>\n",
+       "      <td>401.697836</td>\n",
        "      <td>2002-08-15 00:00:00</td>\n",
-       "      <td>138.668463</td>\n",
+       "      <td>424.296882</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>131</th>\n",
-       "      <td>id_09</td>\n",
-       "      <td>2002-08-21 00:00:00</td>\n",
-       "      <td>27.716417</td>\n",
-       "      <td>28.263963</td>\n",
+       "      <th>91</th>\n",
+       "      <td>id_16</td>\n",
+       "      <td>2002-08-23 00:00:00</td>\n",
+       "      <td>315.036479</td>\n",
+       "      <td>315.368377</td>\n",
        "      <td>2002-08-15 00:00:00</td>\n",
-       "      <td>22.88374</td>\n",
+       "      <td>300.419406</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "    unique_id                   ds  DaskXGBForecast  DaskLGBMForecast  \\\n",
-       "17      id_01  2002-08-19 00:00:00       224.458336        222.742605   \n",
-       "43      id_03  2002-08-17 00:00:00         2.235601          2.210624   \n",
-       "44      id_03  2002-08-18 00:00:00         3.276747          3.239702   \n",
-       "119     id_08  2002-08-23 00:00:00       131.261689        131.180289   \n",
-       "131     id_09  2002-08-21 00:00:00        27.716417         28.263963   \n",
+       "   unique_id                   ds  DaskXGBForecast  DaskLGBMForecast  \\\n",
+       "0      id_00  2002-08-16 00:00:00        23.192749         21.986437   \n",
+       "30     id_02  2002-08-18 00:00:00         96.59974         96.568057   \n",
+       "80     id_05  2002-08-26 00:00:00       257.210466        255.908309   \n",
+       "36     id_12  2002-08-24 00:00:00       401.081335        401.697836   \n",
+       "91     id_16  2002-08-23 00:00:00       315.036479        315.368377   \n",
        "\n",
-       "                  cutoff           y  \n",
-       "17   2002-08-15 00:00:00  210.723139  \n",
-       "43   2002-08-15 00:00:00    2.416967  \n",
-       "44   2002-08-15 00:00:00    3.060194  \n",
-       "119  2002-08-15 00:00:00  138.668463  \n",
-       "131  2002-08-15 00:00:00    22.88374  "
+       "                 cutoff           y  \n",
+       "0   2002-08-15 00:00:00   11.878591  \n",
+       "30  2002-08-15 00:00:00   94.706551  \n",
+       "80  2002-08-15 00:00:00  246.051086  \n",
+       "36  2002-08-15 00:00:00  424.296882  \n",
+       "91  2002-08-15 00:00:00  300.419406  "
       ]
      },
      "execution_count": null,
@@ -831,6 +865,23 @@
     "cv_res.compute().head()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ebb0e756-522d-42cf-9b6e-73b1158c21bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "# single window CV\n",
+    "assert fcst.cross_validation(\n",
+    "    partitioned_series,\n",
+    "    n_windows=1,\n",
+    "    h=5,\n",
+    "    static_features=['static_0', 'static_1'],\n",
+    ").compute()['cutoff'].nunique() == 1"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -865,6 +916,7 @@
     "        i_window=0,\n",
     "        input_size=input_size,\n",
     "    ),\n",
+    "    static_features=['static_0', 'static_1'],\n",
     ")\n",
     "assert reduced_train.groupby('unique_id').size().compute().max() == input_size"
    ]
@@ -881,7 +933,8 @@
     "    partitioned_series,\n",
     "    n_windows=3,\n",
     "    h=14,\n",
-    "    refit=False\n",
+    "    refit=False,\n",
+    "    static_features=['static_0', 'static_1'],\n",
     ")\n",
     "cv_results_df = cv_res.compute().sort_values(['unique_id', 'ds'])\n",
     "cv_results_no_refit_df = cv_res_no_refit.compute().sort_values(['unique_id', 'ds'])\n",
@@ -914,12 +967,18 @@
     "    num_threads=1,\n",
     ")\n",
     "fcst = DistributedMLForecast(freq='D', **flow_params)\n",
-    "fcst.fit(partitioned_series)\n",
-    "preds = fcst.predict(7).compute()\n",
+    "fcst.fit(partitioned_series, static_features=['static_0', 'static_1'])\n",
+    "preds = fcst.predict(7, X_df=future).compute()\n",
     "fcst2 = DistributedMLForecast(freq='D', **flow_params)\n",
-    "fcst2.preprocess(non_std_series, id_col='some_id', time_col='time', target_col='value')\n",
+    "fcst2.preprocess(\n",
+    "    non_std_series,\n",
+    "    id_col='some_id',\n",
+    "    time_col='time',\n",
+    "    target_col='value',\n",
+    "    static_features=['static_0', 'static_1'],\n",
+    ")\n",
     "fcst2.models_ = fcst.models_  # distributed training can end up with different fits\n",
-    "non_std_preds = fcst2.predict(7).compute()\n",
+    "non_std_preds = fcst2.predict(7, X_df=future.rename(columns={'ds': 'time', 'unique_id': 'some_id'})).compute()\n",
     "pd.testing.assert_frame_equal(\n",
     "    preds.drop(columns='ds'),\n",
     "    non_std_preds.drop(columns='time').rename(columns={'some_id': 'unique_id'})\n",
@@ -996,9 +1055,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "series = generate_daily_series(100, n_static_features=2, equal_ends=True, static_as_categorical=False, min_length=500, max_length=1_000)\n",
+    "series['unique_id'] = series['unique_id'].astype(str)  # can't handle categoricals atm\n",
+    "train, future = fourier(series, freq='d', season_length=7, k=2, h=7)\n",
     "numPartitions = 4\n",
-    "series = generate_daily_series(100, n_static_features=2, equal_ends=True, static_as_categorical=False)\n",
-    "spark_series = spark.createDataFrame(series).repartitionByRange(numPartitions, 'unique_id')"
+    "spark_series = spark.createDataFrame(train).repartitionByRange(numPartitions, 'unique_id')"
    ]
   },
   {
@@ -1028,7 +1089,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "models = [SparkLGBMForecast(seed=0), SparkXGBForecast(random_state=0)]"
+    "models = [\n",
+    "    SparkLGBMForecast(seed=0, verbosity=-1),\n",
+    "    SparkXGBForecast(random_state=0),\n",
+    "]"
    ]
   },
   {
@@ -1082,7 +1146,7 @@
    "source": [
     "#| hide\n",
     "# test num_partitions works properly\n",
-    "test_spark_df = spark.createDataFrame(series)\n",
+    "test_spark_df = spark.createDataFrame(train)\n",
     "num_partitions_test = 10\n",
     "fcst_np = DistributedMLForecast(\n",
     "    models=models,\n",
@@ -1096,10 +1160,10 @@
     "    num_threads=1,\n",
     "    num_partitions=num_partitions_test,\n",
     ")\n",
-    "fcst_np.fit(test_spark_df)\n",
+    "fcst_np.fit(test_spark_df, static_features=['static_0', 'static_1'])\n",
     "test_partition_results_size(fcst_np, num_partitions_test)\n",
-    "preds_np = fcst_np.predict(7).toPandas().sort_values(['unique_id', 'ds']).reset_index(drop=True)\n",
-    "preds = fcst.predict(7).toPandas().sort_values(['unique_id', 'ds']).reset_index(drop=True)\n",
+    "preds_np = fcst_np.predict(7, X_df=future).toPandas().sort_values(['unique_id', 'ds']).reset_index(drop=True)\n",
+    "preds = fcst.predict(7, X_df=future).toPandas().sort_values(['unique_id', 'ds']).reset_index(drop=True)\n",
     "pd.testing.assert_frame_equal(\n",
     "    preds[['unique_id', 'ds']], \n",
     "    preds_np[['unique_id', 'ds']], \n",
@@ -1121,7 +1185,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "preds = fcst.predict(14).toPandas()"
+    "preds = fcst.predict(7, X_df=future).toPandas()"
    ]
   },
   {
@@ -1161,37 +1225,37 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>id_00</td>\n",
-       "      <td>2001-05-15</td>\n",
-       "      <td>430.964632</td>\n",
-       "      <td>431.202969</td>\n",
+       "      <td>2002-09-27</td>\n",
+       "      <td>15.102403</td>\n",
+       "      <td>18.631477</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>id_00</td>\n",
-       "      <td>2001-05-16</td>\n",
-       "      <td>505.411960</td>\n",
-       "      <td>504.030227</td>\n",
+       "      <td>2002-09-28</td>\n",
+       "      <td>92.980261</td>\n",
+       "      <td>93.796269</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>id_00</td>\n",
-       "      <td>2001-05-17</td>\n",
-       "      <td>9.889056</td>\n",
-       "      <td>9.706636</td>\n",
+       "      <td>2002-09-29</td>\n",
+       "      <td>160.090375</td>\n",
+       "      <td>159.582315</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>id_00</td>\n",
-       "      <td>2001-05-18</td>\n",
-       "      <td>99.359694</td>\n",
-       "      <td>96.258271</td>\n",
+       "      <td>2002-09-30</td>\n",
+       "      <td>250.416113</td>\n",
+       "      <td>250.861651</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>id_00</td>\n",
-       "      <td>2001-05-19</td>\n",
-       "      <td>196.307731</td>\n",
-       "      <td>197.443618</td>\n",
+       "      <td>2002-10-01</td>\n",
+       "      <td>323.306184</td>\n",
+       "      <td>321.564089</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1199,11 +1263,11 @@
       ],
       "text/plain": [
        "  unique_id         ds  SparkLGBMForecast  SparkXGBForecast\n",
-       "0     id_00 2001-05-15         430.964632        431.202969\n",
-       "1     id_00 2001-05-16         505.411960        504.030227\n",
-       "2     id_00 2001-05-17           9.889056          9.706636\n",
-       "3     id_00 2001-05-18          99.359694         96.258271\n",
-       "4     id_00 2001-05-19         196.307731        197.443618"
+       "0     id_00 2002-09-27          15.102403         18.631477\n",
+       "1     id_00 2002-09-28          92.980261         93.796269\n",
+       "2     id_00 2002-09-29         160.090375        159.582315\n",
+       "3     id_00 2002-09-30         250.416113        250.861651\n",
+       "4     id_00 2002-10-01         323.306184        321.564089"
       ]
      },
      "execution_count": null,
@@ -1308,8 +1372,8 @@
     }
    ],
    "source": [
-    "preds = fa.as_pandas(fcst.predict(10)).sort_values(['unique_id', 'ds']).reset_index(drop=True)\n",
-    "preds2 = fa.as_pandas(fcst2.predict(10)).sort_values(['unique_id', 'ds']).reset_index(drop=True)\n",
+    "preds = fa.as_pandas(fcst.predict(7, X_df=future)).sort_values(['unique_id', 'ds']).reset_index(drop=True)\n",
+    "preds2 = fa.as_pandas(fcst2.predict(7, X_df=future)).sort_values(['unique_id', 'ds']).reset_index(drop=True)\n",
     "pd.testing.assert_frame_equal(preds, preds2)"
    ]
   },
@@ -1331,7 +1395,7 @@
    "outputs": [],
    "source": [
     "local_fcst = fcst.to_local()\n",
-    "local_preds = local_fcst.predict(10)\n",
+    "local_preds = local_fcst.predict(7, X_df=future)\n",
     "# we don't check the dtype because sometimes these are arrow dtypes\n",
     "# or different precisions of float\n",
     "pd.testing.assert_frame_equal(preds, local_preds, check_dtype=False)"
@@ -1356,6 +1420,7 @@
     "    spark_series,\n",
     "    n_windows=3,\n",
     "    h=14,\n",
+    "    static_features=['static_0', 'static_1'],\n",
     ").toPandas()"
    ]
   },
@@ -1397,48 +1462,48 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>id_15</td>\n",
-       "      <td>2001-04-04</td>\n",
-       "      <td>88.438691</td>\n",
-       "      <td>86.105463</td>\n",
-       "      <td>2001-04-02</td>\n",
-       "      <td>92.468763</td>\n",
+       "      <td>id_03</td>\n",
+       "      <td>2002-08-18</td>\n",
+       "      <td>3.272922</td>\n",
+       "      <td>3.348874</td>\n",
+       "      <td>2002-08-15</td>\n",
+       "      <td>3.060194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>id_25</td>\n",
-       "      <td>2001-04-12</td>\n",
-       "      <td>355.712493</td>\n",
-       "      <td>354.525400</td>\n",
-       "      <td>2001-04-02</td>\n",
-       "      <td>320.701359</td>\n",
+       "      <td>id_09</td>\n",
+       "      <td>2002-08-20</td>\n",
+       "      <td>402.718091</td>\n",
+       "      <td>402.622501</td>\n",
+       "      <td>2002-08-15</td>\n",
+       "      <td>398.784459</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>id_03</td>\n",
-       "      <td>2001-04-08</td>\n",
-       "      <td>257.243845</td>\n",
-       "      <td>253.834157</td>\n",
-       "      <td>2001-04-02</td>\n",
-       "      <td>274.420045</td>\n",
+       "      <td>id_25</td>\n",
+       "      <td>2002-08-22</td>\n",
+       "      <td>87.189811</td>\n",
+       "      <td>86.891632</td>\n",
+       "      <td>2002-08-15</td>\n",
+       "      <td>82.731377</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>id_14</td>\n",
-       "      <td>2001-04-07</td>\n",
-       "      <td>24.925278</td>\n",
-       "      <td>23.833504</td>\n",
-       "      <td>2001-04-02</td>\n",
-       "      <td>26.906679</td>\n",
+       "      <td>id_06</td>\n",
+       "      <td>2002-08-21</td>\n",
+       "      <td>20.416790</td>\n",
+       "      <td>20.478502</td>\n",
+       "      <td>2002-08-15</td>\n",
+       "      <td>19.196394</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>id_01</td>\n",
-       "      <td>2001-04-16</td>\n",
-       "      <td>89.180665</td>\n",
-       "      <td>90.743194</td>\n",
-       "      <td>2001-04-02</td>\n",
-       "      <td>93.807725</td>\n",
+       "      <td>id_22</td>\n",
+       "      <td>2002-08-23</td>\n",
+       "      <td>357.718513</td>\n",
+       "      <td>360.502024</td>\n",
+       "      <td>2002-08-15</td>\n",
+       "      <td>394.770699</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1446,18 +1511,18 @@
       ],
       "text/plain": [
        "  unique_id         ds  SparkLGBMForecast  SparkXGBForecast     cutoff  \\\n",
-       "0     id_15 2001-04-04          88.438691         86.105463 2001-04-02   \n",
-       "1     id_25 2001-04-12         355.712493        354.525400 2001-04-02   \n",
-       "2     id_03 2001-04-08         257.243845        253.834157 2001-04-02   \n",
-       "3     id_14 2001-04-07          24.925278         23.833504 2001-04-02   \n",
-       "4     id_01 2001-04-16          89.180665         90.743194 2001-04-02   \n",
+       "0     id_03 2002-08-18           3.272922          3.348874 2002-08-15   \n",
+       "1     id_09 2002-08-20         402.718091        402.622501 2002-08-15   \n",
+       "2     id_25 2002-08-22          87.189811         86.891632 2002-08-15   \n",
+       "3     id_06 2002-08-21          20.416790         20.478502 2002-08-15   \n",
+       "4     id_22 2002-08-23         357.718513        360.502024 2002-08-15   \n",
        "\n",
        "            y  \n",
-       "0   92.468763  \n",
-       "1  320.701359  \n",
-       "2  274.420045  \n",
-       "3   26.906679  \n",
-       "4   93.807725  "
+       "0    3.060194  \n",
+       "1  398.784459  \n",
+       "2   82.731377  \n",
+       "3   19.196394  \n",
+       "4  394.770699  "
       ]
      },
      "execution_count": null,
@@ -1540,10 +1605,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "series = generate_daily_series(100, n_static_features=2, equal_ends=True, static_as_categorical=False)\n",
-    "# we need noncategory unique_id\n",
-    "series['unique_id'] = series['unique_id'].astype(str)\n",
-    "ray_series = ray.data.from_pandas(series)"
+    "series = generate_daily_series(100, n_static_features=2, equal_ends=True, static_as_categorical=False, min_length=500, max_length=1_000)\n",
+    "series['unique_id'] = series['unique_id'].astype(str)  # can't handle categoricals atm\n",
+    "train, future = fourier(series, freq='d', season_length=7, k=2, h=7)\n",
+    "ray_series = ray.data.from_pandas(train)"
    ]
   },
   {
@@ -1573,7 +1638,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "models = [RayLGBMForecast(random_state=0), RayXGBForecast(random_state=0)]"
+    "models = [\n",
+    "    RayLGBMForecast(random_state=0, verbosity=-1),\n",
+    "    RayXGBForecast(random_state=0),\n",
+    "]"
    ]
   },
   {
@@ -1659,13 +1727,13 @@
     "    date_features=['dayofweek', 'month'],\n",
     "    num_threads=1,\n",
     ")\n",
-    "fcst_np.fit(ray_series)\n",
+    "fcst_np.fit(ray_series, static_features=['static_0', 'static_1'])\n",
     "# we dont use test_partition_results_size\n",
     "# since the number of objects is different \n",
     "# from the number of partitions\n",
     "test_eq(fa.count(fcst_np._partition_results), 100) # number of series\n",
-    "preds_np = fcst_np.predict(7).to_pandas().sort_values(['unique_id', 'ds']).reset_index(drop=True)\n",
-    "preds = fcst.predict(7).to_pandas().sort_values(['unique_id', 'ds']).reset_index(drop=True)\n",
+    "preds_np = fcst_np.predict(7, X_df=future).to_pandas().sort_values(['unique_id', 'ds']).reset_index(drop=True)\n",
+    "preds = fcst.predict(7, X_df=future).to_pandas().sort_values(['unique_id', 'ds']).reset_index(drop=True)\n",
     "pd.testing.assert_frame_equal(\n",
     "    preds[['unique_id', 'ds']], \n",
     "    preds_np[['unique_id', 'ds']], \n",
@@ -1687,7 +1755,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "preds = fcst.predict(14).to_pandas()"
+    "preds = fcst.predict(7, X_df=future).to_pandas()"
    ]
   },
   {
@@ -1726,38 +1794,38 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>id_01</td>\n",
-       "      <td>2001-05-15</td>\n",
-       "      <td>118.505341</td>\n",
-       "      <td>118.32222</td>\n",
+       "      <td>id_00</td>\n",
+       "      <td>2002-09-27</td>\n",
+       "      <td>15.232455</td>\n",
+       "      <td>10.38301</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>id_01</td>\n",
-       "      <td>2001-05-16</td>\n",
-       "      <td>152.321457</td>\n",
-       "      <td>152.265915</td>\n",
+       "      <td>id_00</td>\n",
+       "      <td>2002-09-28</td>\n",
+       "      <td>92.288994</td>\n",
+       "      <td>92.531502</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>id_01</td>\n",
-       "      <td>2001-05-17</td>\n",
-       "      <td>181.979599</td>\n",
-       "      <td>181.945618</td>\n",
+       "      <td>id_00</td>\n",
+       "      <td>2002-09-29</td>\n",
+       "      <td>160.043472</td>\n",
+       "      <td>160.722885</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>id_01</td>\n",
-       "      <td>2001-05-18</td>\n",
-       "      <td>9.530758</td>\n",
-       "      <td>9.543224</td>\n",
+       "      <td>id_00</td>\n",
+       "      <td>2002-09-30</td>\n",
+       "      <td>250.03212</td>\n",
+       "      <td>252.821899</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>id_01</td>\n",
-       "      <td>2001-05-19</td>\n",
-       "      <td>40.503441</td>\n",
-       "      <td>40.661186</td>\n",
+       "      <td>id_00</td>\n",
+       "      <td>2002-10-01</td>\n",
+       "      <td>322.905182</td>\n",
+       "      <td>324.387695</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1765,11 +1833,11 @@
       ],
       "text/plain": [
        "  unique_id         ds  RayLGBMForecast  RayXGBForecast\n",
-       "0     id_01 2001-05-15       118.505341       118.32222\n",
-       "1     id_01 2001-05-16       152.321457      152.265915\n",
-       "2     id_01 2001-05-17       181.979599      181.945618\n",
-       "3     id_01 2001-05-18         9.530758        9.543224\n",
-       "4     id_01 2001-05-19        40.503441       40.661186"
+       "0     id_00 2002-09-27        15.232455        10.38301\n",
+       "1     id_00 2002-09-28        92.288994       92.531502\n",
+       "2     id_00 2002-09-29       160.043472      160.722885\n",
+       "3     id_00 2002-09-30        250.03212      252.821899\n",
+       "4     id_00 2002-10-01       322.905182      324.387695"
       ]
      },
      "execution_count": null,
@@ -1850,8 +1918,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "preds = fa.as_pandas(fcst.predict(10)).sort_values(['unique_id', 'ds']).reset_index(drop=True)\n",
-    "preds2 = fa.as_pandas(fcst2.predict(10)).sort_values(['unique_id', 'ds']).reset_index(drop=True)\n",
+    "preds = fa.as_pandas(fcst.predict(7, X_df=future)).sort_values(['unique_id', 'ds']).reset_index(drop=True)\n",
+    "preds2 = fa.as_pandas(fcst2.predict(7, X_df=future)).sort_values(['unique_id', 'ds']).reset_index(drop=True)\n",
     "pd.testing.assert_frame_equal(preds, preds2)"
    ]
   },
@@ -1873,7 +1941,7 @@
    "outputs": [],
    "source": [
     "local_fcst = fcst.to_local()\n",
-    "local_preds = local_fcst.predict(10)\n",
+    "local_preds = local_fcst.predict(7, X_df=future)\n",
     "# we don't check the dtype because sometimes these are arrow dtypes\n",
     "# or different precisions of float\n",
     "pd.testing.assert_frame_equal(preds, local_preds, check_dtype=False)"
@@ -1898,6 +1966,7 @@
     "    ray_series,\n",
     "    n_windows=3,\n",
     "    h=14,\n",
+    "    static_features=['static_0', 'static_1'],\n",
     ").to_pandas()"
    ]
   },
@@ -1939,48 +2008,48 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>id_10</td>\n",
-       "      <td>2001-05-01</td>\n",
-       "      <td>24.767561</td>\n",
-       "      <td>24.528799</td>\n",
-       "      <td>2001-04-30</td>\n",
-       "      <td>31.878545</td>\n",
+       "      <td>id_04</td>\n",
+       "      <td>2002-09-20</td>\n",
+       "      <td>118.982094</td>\n",
+       "      <td>117.577477</td>\n",
+       "      <td>2002-09-12</td>\n",
+       "      <td>118.603489</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>id_10</td>\n",
-       "      <td>2001-05-07</td>\n",
-       "      <td>1.916985</td>\n",
-       "      <td>2.323445</td>\n",
-       "      <td>2001-04-30</td>\n",
-       "      <td>7.365955</td>\n",
+       "      <td>id_04</td>\n",
+       "      <td>2002-09-24</td>\n",
+       "      <td>51.461491</td>\n",
+       "      <td>50.120552</td>\n",
+       "      <td>2002-09-12</td>\n",
+       "      <td>52.668389</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>id_13</td>\n",
-       "      <td>2001-05-01</td>\n",
-       "      <td>210.900330</td>\n",
-       "      <td>212.959320</td>\n",
-       "      <td>2001-04-30</td>\n",
-       "      <td>190.485236</td>\n",
+       "      <td>id_05</td>\n",
+       "      <td>2002-09-20</td>\n",
+       "      <td>27.594826</td>\n",
+       "      <td>24.421537</td>\n",
+       "      <td>2002-09-12</td>\n",
+       "      <td>20.120710</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>id_14</td>\n",
-       "      <td>2001-05-01</td>\n",
-       "      <td>196.620819</td>\n",
-       "      <td>196.253036</td>\n",
-       "      <td>2001-04-30</td>\n",
-       "      <td>213.631212</td>\n",
+       "      <td>id_05</td>\n",
+       "      <td>2002-09-25</td>\n",
+       "      <td>411.615204</td>\n",
+       "      <td>412.093384</td>\n",
+       "      <td>2002-09-12</td>\n",
+       "      <td>419.621422</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>id_14</td>\n",
-       "      <td>2001-05-03</td>\n",
-       "      <td>323.323334</td>\n",
-       "      <td>322.372894</td>\n",
-       "      <td>2001-04-30</td>\n",
-       "      <td>338.234837</td>\n",
+       "      <td>id_08</td>\n",
+       "      <td>2002-09-25</td>\n",
+       "      <td>83.210945</td>\n",
+       "      <td>83.842705</td>\n",
+       "      <td>2002-09-12</td>\n",
+       "      <td>86.344885</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1988,11 +2057,11 @@
       ],
       "text/plain": [
        "  unique_id         ds  RayLGBMForecast  RayXGBForecast     cutoff           y\n",
-       "0     id_10 2001-05-01        24.767561       24.528799 2001-04-30   31.878545\n",
-       "1     id_10 2001-05-07         1.916985        2.323445 2001-04-30    7.365955\n",
-       "2     id_13 2001-05-01       210.900330      212.959320 2001-04-30  190.485236\n",
-       "3     id_14 2001-05-01       196.620819      196.253036 2001-04-30  213.631212\n",
-       "4     id_14 2001-05-03       323.323334      322.372894 2001-04-30  338.234837"
+       "0     id_04 2002-09-20       118.982094      117.577477 2002-09-12  118.603489\n",
+       "1     id_04 2002-09-24        51.461491       50.120552 2002-09-12   52.668389\n",
+       "2     id_05 2002-09-20        27.594826       24.421537 2002-09-12   20.120710\n",
+       "3     id_05 2002-09-25       411.615204      412.093384 2002-09-12  419.621422\n",
+       "4     id_08 2002-09-25        83.210945       83.842705 2002-09-12   86.344885"
       ]
      },
      "execution_count": null,