diff --git a/prototype/experiment.ipynb b/prototype/experiment.ipynb
new file mode 100644
index 0000000..6fc7b0f
--- /dev/null
+++ b/prototype/experiment.ipynb
@@ -0,0 +1,259 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dataclasses import dataclass\n",
+    "from typing import Union, Optional\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@dataclass\n",
+    "class TimeConfig:\n",
+    "    \"\"\"Container for storing and accessing time configuration.\"\"\"\n",
+    "    start_year: str\n",
+    "    end_year: str\n",
+    "    target_start: str = '11-01'\n",
+    "    target_end: str = '12-01'\n",
+    "    freq: Optional[str] = '2M' # daily/monthly\n",
+    "    # tfreq: Union[int, str] = 7\n",
+    "    \n",
+    "    def get_dates_load(self):\n",
+    "        \"\"\"Return index which lines up with the bins\n",
+    "        Leap days are removed.\n",
+    "\n",
+    "        (see the function \"timeseries_tofit_bins\" in line 628 of functions_pp)\n",
+    "\n",
+    "        return pd.DateTimeIndex\n",
+    "        \"\"\"\n",
+    "\n",
+    "        return\n",
+    "\n",
+    "    def get_resample_bins(self):\n",
+    "        \"\"\"Return bins to aggregate to n day/ n month means\n",
+    "        This function could work with the precursor and target variables.\n",
+    "        \"\"\"\n",
+    "\n",
+    "        return\n",
+    "\n",
+    "    def split_train_test_groups(self):\n",
+    "        \"\"\"\n",
+    "        \"\"\"\n",
+    "        return"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tc = TimeConfig(start_year, end_year, target_start, target_end, freq)\n",
+    "tc.dates2bin # time passed to data loader\n",
+    "tc.labelbins # same axis as dates2bin, but has labels to group by n day/ n month means\n",
+    "tc.traintestgroups # smart splitting of train/test data and avoid overlapping\n",
+    "tc.df_split # aggregated time axis pandas dataframe with columns train=true and RV_mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@dataclass\n",
+    "class TrainTestSet:\n",
+    "    \"\"\"Base class for various traintest methods.\"\"\"\n",
+    "    df: pd.DataFrame\n",
+    "   \n",
+    "    def get_test_labels(self):\n",
+    "        return self.df.query('traintest==1')\n",
+    "       \n",
+    "    def get_training_labels(self):\n",
+    "        return self.df.query('traintest==2')\n",
+    "\n",
+    "class LeaveOutN(TrainTestSet):\n",
+    "    \"\"\"TrainTestSet based on a leave-n-out sampling method.\"\"\"\n",
+    "    def __init__(self, timeconfig, n, max_lag=None):\n",
+    "        times = timeconfig.datetimes\n",
+    "        df = pd.DataFrame(index=times, columns=[\"traintest\"])\n",
+    "        df['traintest'] = np.random.randint(0, 3, len(df))\n",
+    "        self.df = df\n",
+    "\n",
+    "class Random(TrainTestSet):\n",
+    "    \"\"\"TrainTestSet based on a random sampling method.\"\"\"\n",
+    "    n: int\n",
+    "\n",
+    "class Ranstrat(TrainTestSet):\n",
+    "    \"\"\"TrainTestSet based on a random stratified sampling method.\"\"\"\n",
+    "    n: int\n",
+    "\n",
+    "class split(TrainTestSet):\n",
+    "    \"\"\"TrainTestSet based on a simple split method.\"\"\"\n",
+    "    n: int\n",
+    "\n",
+    "class TimeSeriesSplit(TrainTestSet):\n",
+    "    \"\"\"TrainTestSet based on a \"one-step-ahead\" method.\"\"\"\n",
+    "    n: int\n",
+    "\n",
+    "class RepeatedKfold(TrainTestSet):\n",
+    "    \"\"\"TrainTestSet based on a repeated k-fold with different randomizations.\"\"\"\n",
+    "    n_repeats: int\n",
+    "    n_folds: int"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "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>traintest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1979-01-03</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1979-01-09</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1979-01-16</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1979-01-18</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1979-01-19</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-12-08</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-12-14</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-12-24</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-12-29</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-12-30</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5082 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            traintest\n",
+       "1979-01-03          2\n",
+       "1979-01-09          2\n",
+       "1979-01-16          2\n",
+       "1979-01-18          2\n",
+       "1979-01-19          2\n",
+       "...               ...\n",
+       "2020-12-08          2\n",
+       "2020-12-14          2\n",
+       "2020-12-24          2\n",
+       "2020-12-29          2\n",
+       "2020-12-30          2\n",
+       "\n",
+       "[5082 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tc = TimeConfig(start_year = '1979', end_year = '2021', freq='D')\n",
+    "ttset = LeaveOutN(tc, n=5)\n",
+    "ttset.get_training_labels()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class CrossValidator():\n",
+    "    \"\"\"Perform cross-validation of training/testing data\"\"\"\n",
+    "    def __init__(self):"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "08d8a5f855c7a1e3d1e204794a733da246b168cd162c0e6afd9e496498bf62ad"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.8.12 ('eucp')",
+   "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": "3.8.12"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/prototype/prototype.ipynb b/prototype/prototype.ipynb
new file mode 100644
index 0000000..dac06c3
--- /dev/null
+++ b/prototype/prototype.ipynb
@@ -0,0 +1,89 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import S2Sinit\n",
+    "import climpp\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initialize the workflow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define parameters for time config\n",
+    "start_year= 1979\n",
+    "end_year = 2020\n",
+    "target_start  = '11-01'\n",
+    "target_end  = '12-01'\n",
+    "freq = '2M' # two month means, with pandas\n",
+    "# define parameters for cross-validator\n",
+    "max_lag = 5\n",
+    "k = 10\n",
+    "\n",
+    "# this function only allows single target period\n",
+    "# data on the same coordinate\n",
+    "initializer = S2Sinit(start_year, end_year, target_start,\n",
+    "                        target_end, freq)\n",
+    "                \n",
+    "# ------------------ optional procedure\n",
+    "# call cross validator\n",
+    "initializer.cv.kfold(max_lag, k) # only specify parameters for cross-validation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Preprocessing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define parameters for preprocessing\n",
+    "path2target = 'path/target_var'\n",
+    "path2sst = 'path/sst'\n",
+    "path2z500 = 'path/z500'\n",
+    "# preprocess recipe for target variable\n",
+    "preprocess_steps_target = ['detrend', 'deseasonalize', 'standardize']\n",
+    "# preprocess recipe for precursors\n",
+    "preprocess_steps_sst = ['detrend', 'deseasonalize', 'standardize']\n",
+    "preprocess_steps_z500 = ['detrend', 'deseasonalize']\n",
+    "\n",
+    "# preprocess target\n",
+    "target_instance = climpp(initializer, path2target, preprocess_steps_target)\n",
+    "target_instance = climpp(initializer, path2target, preprocess_steps_target)\n",
+    "# preprocess precursor sst\n",
+    "sst_instance = climpp(initializer, path2sst, preprocess_steps_sst)\n",
+    "z500_instance = climpp(initializer, path2z500, preprocess_steps_z500)\n",
+    "\n",
+    "# execute preprocessing steps\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}