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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "!{sys.executable} -m pip install 'sktime[all_extras]'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Combining tsbootstrap with sktime\n",
+    "\n",
+    "This notebook demonstrates how tsbootstraps integrates with sktime. To demonstrate it, we forecast the airline time series. \n",
+    "\n",
+    "### You learn\n",
+    "* How you can use bootstrapping to create probabilistic forecasts\n",
+    "* How you can integrate tsbootstrap with sktime\n",
+    "\n",
+    "### Prerequisites\n",
+    "* You should be familiar with the sktime forecasting pipelines.\n",
+    "* You should be familiar with the basic ideas of bootstrapping"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Integration of tsbootstrap with sktime\n",
+    "\n",
+    "To us tsbootstrap together with sktime, we perform three steps:\n",
+    "\n",
+    "1. we create the bootstrapper, we would like to use. In this example, we use a `MovingBlockBoostrap` with a `block_length` of 10 and create `10` bootstraps. The created tsbootstrap object, we pass to the `TSBoostrapAdapter` of sktime. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sktime.transformations.bootstrap import TSBootstrapAdapter\n",
+    "from tsbootstrap import MovingBlockBootstrap, MovingBlockBootstrapConfig\n",
+    "\n",
+    "tsbootstrap_opject = MovingBlockBootstrap(MovingBlockBootstrapConfig(10, n_bootstraps=10))\n",
+    "bootstrap = TSBootstrapAdapter(tsbootstrap_opject)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. we create the sktime based forecaster. In particular, we create first the `BaggingForecaster` and embed in afterwards into a forecasting pipeline that contains additional preprocessing steps. \n",
+    "* The task of the `BaggingForecaster` is to combine a boostrap algorithm and a forecasting algorithm. Internally, it creates one forecaster for each bootstrap the boostrapping algorithm is creating. \n",
+    "* As preprocessing steps, we add a `LogTransformer` and a `Deseasonalizer`. \n",
+    "    * The `LogTransformer` is responsible to make the time series additive.\n",
+    "    * The `Deaseasonalizer` removes the yearly seasonality from the time series. \n",
+    "    * For more information, take a look at https://github.com/sktime/sktime/blob/main/examples/03_transformers.ipynb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sktime.forecasting.compose import BaggingForecaster\n",
+    "from sktime.transformations.series.boxcox import LogTransformer\n",
+    "from sktime.transformations.series.detrend import Deseasonalizer\n",
+    "from sktime.forecasting.trend import PolynomialTrendForecaster\n",
+    "\n",
+    "bagging_forecaster = BaggingForecaster(bootstrap, PolynomialTrendForecaster(degree=2))\n",
+    "forecaster =  LogTransformer() * Deseasonalizer(sp=12) * bagging_forecaster\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3. we fit the forecaster to the training data. Therefore, we first load the data, split it into train and test data. Afterwards, we can call fit on the forecater."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f {color: black;background-color: white;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f pre{padding: 0;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f div.sk-toggleable {background-color: white;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f div.sk-estimator:hover {background-color: #d4ebff;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f div.sk-item {z-index: 1;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f div.sk-parallel-item:only-child::after {width: 0;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f div.sk-text-repr-fallback {display: none;}</style><div id='sk-2de87dcf-e2d4-492d-bf4c-00c6c8771a1f' class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TransformedTargetForecaster(steps=[LogTransformer(), Deseasonalizer(sp=12),\n",
+       "                                   BaggingForecaster(bootstrap_transformer=TSBootstrapAdapter(tsbootstrapper=MovingBlockBootstrap(config=MovingBlockBootstrapConfig(block_length=10))),\n",
+       "                                                     forecaster=PolynomialTrendForecaster(degree=2))])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class='sk-label-container'><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('d2329623-994a-4f30-8ad2-aed6743c4392') type=\"checkbox\" ><label for=UUID('d2329623-994a-4f30-8ad2-aed6743c4392') class='sk-toggleable__label sk-toggleable__label-arrow'>TransformedTargetForecaster</label><div class=\"sk-toggleable__content\"><pre>TransformedTargetForecaster(steps=[LogTransformer(), Deseasonalizer(sp=12),\n",
+       "                                   BaggingForecaster(bootstrap_transformer=TSBootstrapAdapter(tsbootstrapper=MovingBlockBootstrap(config=MovingBlockBootstrapConfig(block_length=10))),\n",
+       "                                                     forecaster=PolynomialTrendForecaster(degree=2))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('cf79ceec-38fc-42fb-b06d-1d3b24f14a01') type=\"checkbox\" ><label for=UUID('cf79ceec-38fc-42fb-b06d-1d3b24f14a01') class='sk-toggleable__label sk-toggleable__label-arrow'>LogTransformer</label><div class=\"sk-toggleable__content\"><pre>LogTransformer()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('639d1353-9420-472c-9cd7-53c981bd7d95') type=\"checkbox\" ><label for=UUID('639d1353-9420-472c-9cd7-53c981bd7d95') class='sk-toggleable__label sk-toggleable__label-arrow'>Deseasonalizer</label><div class=\"sk-toggleable__content\"><pre>Deseasonalizer(sp=12)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('dc7ee383-2a24-4c57-acf0-456cd72a521a') type=\"checkbox\" ><label for=UUID('dc7ee383-2a24-4c57-acf0-456cd72a521a') class='sk-toggleable__label sk-toggleable__label-arrow'>MovingBlockBootstrapConfig</label><div class=\"sk-toggleable__content\"><pre>MovingBlockBootstrapConfig(block_length=10)</pre></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('06b8618e-d51f-400a-a3fc-660db2420655') type=\"checkbox\" ><label for=UUID('06b8618e-d51f-400a-a3fc-660db2420655') class='sk-toggleable__label sk-toggleable__label-arrow'>PolynomialTrendForecaster</label><div class=\"sk-toggleable__content\"><pre>PolynomialTrendForecaster(degree=2)</pre></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "TransformedTargetForecaster(steps=[LogTransformer(), Deseasonalizer(sp=12),\n",
+       "                                   BaggingForecaster(bootstrap_transformer=TSBootstrapAdapter(tsbootstrapper=MovingBlockBootstrap(config=MovingBlockBootstrapConfig(block_length=10))),\n",
+       "                                                     forecaster=PolynomialTrendForecaster(degree=2))])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sktime.datasets import load_airline\n",
+    "from sktime.split import temporal_train_test_split\n",
+    "\n",
+    "y =load_airline()\n",
+    "\n",
+    "y_train, y_test = temporal_train_test_split(y)\n",
+    "\n",
+    "forecaster.fit(y_train)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After fitting the forecaster, we can use it to create probabilistic forecasts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "pred_interval = forecaster.predict_interval(fh=y_test.index)\n",
+    "forecast = forecaster.predict(fh=y_test.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 1600x400 with 1 Axes>,\n",
+       " <Axes: ylabel='Number of airline passengers'>)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sktime.utils.plotting import plot_series\n",
+    "plot_series(y_test, forecast, labels=[\"actuals\", \"forecast\"], pred_interval=pred_interval)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tsbootstrap",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}