diff --git a/docs/examples.rst b/docs/examples.rst
index 08526ff614..3fed8b4195 100644
--- a/docs/examples.rst
+++ b/docs/examples.rst
@@ -12,4 +12,5 @@ Examples
    examples/set_and_get_examples
    examples/search_vector_similarity_examples
    examples/pipeline_examples
+   examples/timeseries_examples
    examples/redis-stream-example.ipynb
diff --git a/docs/examples/timeseries_examples.ipynb b/docs/examples/timeseries_examples.ipynb
new file mode 100644
index 0000000000..fefc0c8f37
--- /dev/null
+++ b/docs/examples/timeseries_examples.ipynb
@@ -0,0 +1,631 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Timeseries\n",
+    "\n",
+    "`redis-py` supports [RedisTimeSeries](https://github.com/RedisTimeSeries/RedisTimeSeries/) which is a time-series-database module for Redis.\n",
+    "\n",
+    "This example shows how to handle timeseries data with `redis-py`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Health check"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import redis \n",
+    "\n",
+    "r = redis.Redis(decode_responses=True)\n",
+    "ts = r.ts()\n",
+    "\n",
+    "r.ping()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simple example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a timeseries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ts.create(\"ts_key\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add samples to the timeseries\n",
+    "\n",
+    "We can either set the timestamp with an UNIX timestamp in milliseconds or use * to set the timestamp based en server's clock."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1657272304448"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ts.add(\"ts_key\", 1657265437756, 1)\n",
+    "ts.add(\"ts_key\", \"1657265437757\", 2)\n",
+    "ts.add(\"ts_key\", \"*\", 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get the last sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1657272304448, 3.0)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ts.get(\"ts_key\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get samples between two timestamps\n",
+    "\n",
+    "The minimum and maximum possible timestamps can be expressed with respectfully - and +."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(1657265437756, 1.0), (1657265437757, 2.0), (1657272304448, 3.0)]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ts.range(\"ts_key\", \"-\", \"+\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(1657265437756, 1.0), (1657265437757, 2.0)]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ts.range(\"ts_key\", 1657265437756, 1657265437757)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Delete samples between two timestamps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before deletion:  [(1657265437756, 1.0), (1657265437757, 2.0), (1657272304448, 3.0)]\n",
+      "After deletion:   [(1657272304448, 3.0)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Before deletion: \", ts.range(\"ts_key\", \"-\", \"+\"))\n",
+    "ts.delete(\"ts_key\", 1657265437756, 1657265437757)\n",
+    "print(\"After deletion:  \", ts.range(\"ts_key\", \"-\", \"+\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multiple timeseries with labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ts.create(\"ts_key1\")\n",
+    "ts.create(\"ts_key2\", labels={\"label1\": 1, \"label2\": 2})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add samples to multiple timeseries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1657272306147, 1657272306147]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ts.madd([(\"ts_key1\", \"*\", 1), (\"ts_key2\", \"*\", 2)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add samples with labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1657272306457"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ts.add(\"ts_key2\", \"*\", 2,  labels={\"label1\": 1, \"label2\": 2})\n",
+    "ts.add(\"ts_key2\", \"*\", 2,  labels={\"label1\": 3, \"label2\": 4})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get the last sample matching specific label\n",
+    "\n",
+    "Get the last sample that matches \"label1=1\", see [Redis documentation](https://redis.io/commands/ts.mget/) to see the posible filter values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'ts_key2': [{}, 1657272306457, 2.0]}]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ts.mget([\"label1=1\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get also the label-value pairs of the sample:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'ts_key2': [{'label1': '1', 'label2': '2'}, 1657272306457, 2.0]}]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ts.mget([\"label1=1\"], with_labels=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Retention period\n",
+    "\n",
+    "You can specify a retention period when creating timeseries objects or when adding a sample timeseries object. Once the retention period has elapsed, the sample is removed from the timeseries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "retention_time = 1000\n",
+    "ts.create(\"ts_key_ret\", retention_msecs=retention_time)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Base timeseries:                      [(1657272307670, 1.0)]\n",
+      "Timeseries after 1000 milliseconds:   [(1657272307670, 1.0)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "# this will be deleted in 1000 milliseconds\n",
+    "ts.add(\"ts_key_ret\", \"*\", 1, retention_msecs=retention_time)\n",
+    "print(\"Base timeseries:                     \", ts.range(\"ts_key_ret\", \"-\", \"+\"))\n",
+    "# sleeping for 1000 milliseconds (1 second)\n",
+    "time.sleep(1)\n",
+    "print(\"Timeseries after 1000 milliseconds:  \", ts.range(\"ts_key_ret\", \"-\", \"+\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The two lists are the same, this is because the oldest values are deleted when a new sample is added."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1657272308849"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ts.add(\"ts_key_ret\", \"*\", 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(1657272308849, 10.0)]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ts.range(\"ts_key_ret\", \"-\", \"+\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here the first sample has been deleted."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Specify duplicate policies\n",
+    "\n",
+    "By default, the policy for duplicates timestamp keys is set to \"BLOCK\", we cannot create two samples with the same timestamp:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TSDB: Error at upsert, update is not supported when DUPLICATE_POLICY is set to BLOCK mode\n"
+     ]
+    }
+   ],
+   "source": [
+    "ts.add(\"ts_key\", 123456789, 1)\n",
+    "try:\n",
+    "    ts.add(\"ts_key\", 123456789, 2)\n",
+    "except Exception as err:\n",
+    "    print(err)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can change this default behaviour using `duplicate_policy` parameter, for instance:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(123456789, 2.0), (1657272304448, 3.0)]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# using policy \"LAST\", we keep the last added sample\n",
+    "ts.add(\"ts_key\", 123456789, 2, duplicate_policy=\"LAST\")\n",
+    "ts.range(\"ts_key\", \"-\", \"+\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For more informations about duplicate policies, see [Redis documentation](https://redis.io/commands/ts.add/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using Redis TSDB to keep track of a value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1657272310241"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ts.add(\"ts_key_incr\", \"*\", 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Increment the value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for _ in range(10):\n",
+    "    ts.incrby(\"ts_key_incr\", 1)\n",
+    "    # sleeping a bit so the timestamp are not duplicates\n",
+    "    time.sleep(0.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(1657272310241, 0.0),\n",
+       " (1657272310533, 1.0),\n",
+       " (1657272310545, 2.0),\n",
+       " (1657272310556, 3.0),\n",
+       " (1657272310567, 4.0),\n",
+       " (1657272310578, 5.0),\n",
+       " (1657272310589, 6.0),\n",
+       " (1657272310600, 7.0),\n",
+       " (1657272310611, 8.0),\n",
+       " (1657272310622, 9.0),\n",
+       " (1657272310632, 10.0)]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ts.range(\"ts_key_incr\", \"-\", \"+\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.2 64-bit",
+   "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": "3.9.2"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}