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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "50db9d0d",
+   "metadata": {},
+   "source": [
+    "# How do I find the most similar pair of time series sub-sequences (motifs)?\n",
+    "\n",
+    "*Reproducing Slide 3 from Eamonn Keogh's \"100 Time Series Data Mining Questions (with Answers)\" using STUMPY.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c544e39",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'numpy'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m numpy \u001b[38;5;28;01mas\u001b[39;00m np\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m stumpy\n\u001b[32m      3\u001b[39m \n\u001b[32m      4\u001b[39m \u001b[38;5;66;03m# 1. Generate or load a 1D time series (array-like, float64)\u001b[39;00m\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'numpy'"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import stumpy\n",
+    "\n",
+    "# 1. Load exact Slide 3 dataset (Sony AIBO Robot Dog Accelerometer & Carpet Query)\n",
+    "T = np.loadtxt(\"https://raw.githubusercontent.com/TDAmeritrade/stumpy/main/docs/Tutorial_Pattern_Matching_steam_gen.txt\")\n",
+    "Q = np.loadtxt(\"https://raw.githubusercontent.com/TDAmeritrade/stumpy/main/docs/Tutorial_Pattern_Matching_carpet_query.txt\")\n",
+    "\n",
+    "# 2. Find closest query match in 1 line using MASS\n",
+    "distance_profile = stumpy.mass(Q, T)\n",
+    "idx = np.argmin(distance_profile)\n",
+    "\n",
+    "# 3. Plot full time series with red dashed match boundaries & query overlay\n",
+    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 6))\n",
+    "ax1.plot(T, color=\"black\", alpha=0.8)\n",
+    "ax1.axvline(x=idx, color=\"red\", linestyle=\"--\")\n",
+    "ax1.axvline(x=idx + len(Q), color=\"red\", linestyle=\"--\")\n",
+    "ax1.set_ylabel(\"Acceleration\")\n",
+    "\n",
+    "ax2.plot(stumpy.core.z_norm(Q), label=\"Query (Carpet Pattern)\", color=\"tab:blue\")\n",
+    "ax2.plot(stumpy.core.z_norm(T[idx : idx + len(Q)]), label=\"Best Match\", color=\"tab:orange\", linestyle=\"--\")\n",
+    "ax2.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bfa92bf6",
+   "metadata": {},
+   "source": [
+    "### Summary\n",
+    "The two red dashed lines highlight the starting positions of the most similar pair of sub-sequences (motifs) of length $m = 50$."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}