diff --git a/exercise-my-solution.ipynb b/exercise-my-solution.ipynb
new file mode 100644
index 0000000..bb98c7e
--- /dev/null
+++ b/exercise-my-solution.ipynb
@@ -0,0 +1,172 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-03-04T09:40:28.904Z",
+     "iopub.status.busy": "2024-03-04T09:40:28.896Z",
+     "iopub.status.idle": "2024-03-04T09:40:28.978Z",
+     "shell.execute_reply": "2024-03-04T09:40:28.967Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-03-04T10:02:39.062Z",
+     "iopub.status.busy": "2024-03-04T10:02:39.057Z",
+     "iopub.status.idle": "2024-03-04T10:02:39.068Z",
+     "shell.execute_reply": "2024-03-04T10:02:39.071Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "n_series = 32\n",
+    "len_one_series = 5*2**20\n",
+    "time_series = np.random.rand(n_series, len_one_series)\n",
+    "gap = 16*2**10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-03-04T10:02:41.027Z",
+     "iopub.status.busy": "2024-03-04T10:02:41.020Z",
+     "iopub.status.idle": "2024-03-04T10:02:41.036Z",
+     "shell.execute_reply": "2024-03-04T10:02:41.040Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "print(f'Size of one time series: {int(time_series[0].nbytes/2**20)} M')\n",
+    "print(f'Size of collection: {int(time_series.nbytes/2**20)} M')\n",
+    "print(f'Gap size: {int(gap*8/2**10)} K')\n",
+    "print(f'Gapped series size: {int(time_series[0, ::gap].nbytes/2**10)} K')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-03-04T10:06:08.461Z",
+     "iopub.status.busy": "2024-03-04T10:06:08.459Z",
+     "iopub.status.idle": "2024-03-04T10:06:08.466Z",
+     "shell.execute_reply": "2024-03-04T10:06:08.468Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# compute a Taylor-like series\n",
+    "def taylor(time_series, mean, gap):\n",
+    "    for row, ts in enumerate(time_series):\n",
+    "        for pwr in range(1,20):\n",
+    "            mean[row] += (ts[::gap]**pwr).sum()\n",
+    "    return mean\n",
+    "       "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-03-04T10:06:08.461Z",
+     "iopub.status.busy": "2024-03-04T10:06:08.459Z",
+     "iopub.status.idle": "2024-03-04T10:06:08.466Z",
+     "shell.execute_reply": "2024-03-04T10:06:08.468Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def taylor_improved(time_series, mean, gap):\n",
+    "    y = time_series[:,::gap].copy()\n",
+    "    for row, ts in enumerate(y):\n",
+    "        for pwr in range(1,20):\n",
+    "            mean[row] += (ts**pwr).sum()\n",
+    "    return mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# verify that they yield the same results\n",
+    "out1 = taylor(time_series, np.zeros(n_series), gap)\n",
+    "out2 = taylor_improved(time_series, np.zeros(n_series), gap)\n",
+    "np.testing.assert_allclose(out1, out2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-03-04T10:06:14.959Z",
+     "iopub.status.busy": "2024-03-04T10:06:14.956Z",
+     "iopub.status.idle": "2024-03-04T10:06:17.437Z",
+     "shell.execute_reply": "2024-03-04T10:06:17.443Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "mean = np.zeros(n_series, dtype='float64')\n",
+    "%timeit taylor(time_series, mean, gap)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-03-04T10:06:20.056Z",
+     "iopub.status.busy": "2024-03-04T10:06:20.053Z",
+     "iopub.status.idle": "2024-03-04T10:06:21.695Z",
+     "shell.execute_reply": "2024-03-04T10:06:21.700Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "mean = np.zeros(n_series, dtype='float64')\n",
+    "%timeit taylor_improved(time_series, mean, gap)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.5"
+  },
+  "nteract": {
+   "version": "0.28.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}