Publication notebooks (#216)

* table setup * parameter table * names * published notebook
time-series-machine-learning · Apr 23, 2024 · eafb6de · eafb6de
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diff --git a/tsml_eval/publications/y2023/tsc_bakeoff/tsc_bakeoff_2023.ipynb b/tsml_eval/publications/y2023/tsc_bakeoff/tsc_bakeoff_2023.ipynb
@@ -10,7 +10,8 @@
     "# Bake off redux: a review and experimental evaluation of recent time series classification algorithms\n",
     "\n",
     "This is the webpage and repo package to support the paper \"Bake off redux: a review\n",
-    "and experimental evaluation of recent time series classification algorithms\" submitted to [Data Mining and Knowledge Discovery](https://link.springer.com/journal/10618).\n",
+    "and experimental evaluation of recent time series classification algorithms\" published in \n",
+    "[Data Mining and Knowledge Discovery](https://link.springer.com/article/10.1007/s10618-024-01022-1).\n",
     "\n",
     "Our results files are stored [here](https://github.com/time-series-machine-learning/tsml-eval/tree/main/tsml_eval/publications/y2023/tsc_bakeoff/results).\n",
     "\n",

diff --git a/tsml_eval/publications/y2023/tser_archive_expansion/tser_archive_expansion.ipynb b/tsml_eval/publications/y2023/tser_archive_expansion/tser_archive_expansion.ipynb
@@ -1,15 +1,13 @@
 {
  "cells": [
   {
-   "attachments": {},
+   "metadata": {},
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
    "source": [
     "# Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression\n",
     "\n",
-    "This is the webpage and repo package to support the paper \"Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression\" submitted to IEEE Transactions on Knowledge and Data Engineering (TKDE).\n",
+    "This is the webpage and repo package to support the paper \"Unsupervised Feature Based Algorithms \n",
+    "for Time Series Extrinsic Regression\" published in Data Mining and Knowledge Discovery.\n",
     "\n",
     "Our results files are stored [here](https://github.com/time-series-machine-learning/tsml-eval/tree/main/tsml_eval/publications/y2023/tser_archive_expansion/results).\n",
     "\n",
@@ -64,17 +62,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "D:\\CMP_Machine_Learning\\Repositories\\tsml-eval\\venv\\lib\\site-packages\\dask\\dataframe\\_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
    "source": [
     "from aeon.regression.convolution_based import RocketRegressor\n",
     "from sklearn.metrics import mean_squared_error\n",
@@ -87,10 +74,13 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-12-19T13:14:35.468135700Z",
-     "start_time": "2023-12-19T13:14:00.609300200Z"
+     "end_time": "2024-04-23T11:49:31.813513Z",
+     "start_time": "2024-04-23T11:49:28.312416Z"
     }
-   }
+   },
+   "outputs": [
+   ],
+   "execution_count": 1
   },
   {
    "cell_type": "markdown",
@@ -105,26 +95,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'numpy.ndarray'> <class 'numpy.ndarray'>\n",
-      "(20, 1, 20) (20,)\n",
-      "(20, 1, 20) (20,)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "array([[[2.39, 2.36, 2.45, 2.45, 2.54, 2.69, 2.63, 2.73, 2.7 , 2.73,\n         2.68, 2.76, 2.76, 2.68, 2.39, 2.4 , 2.36, 2.6 , 2.77, 2.76]],\n\n       [[3.02, 3.09, 3.01, 3.01, 2.98, 3.11, 3.13, 3.13, 3.23, 3.36,\n         3.31, 3.25, 3.24, 3.23, 3.15, 3.21, 3.36, 3.3 , 3.4 , 3.62]],\n\n       [[3.95, 4.03, 4.07, 4.35, 4.25, 4.33, 4.45, 4.65, 4.77, 4.71,\n         4.66, 4.97, 5.13, 5.21, 5.39, 5.66, 5.52, 5.32, 5.25, 4.96]],\n\n       [[2.62, 2.63, 2.63, 2.71, 2.71, 2.72, 2.58, 2.58, 2.58, 2.62,\n         2.61, 2.67, 2.57, 2.59, 2.63, 2.59, 2.59, 2.5 , 2.55, 2.55]],\n\n       [[2.44, 2.43, 2.47, 2.48, 2.5 , 2.57, 2.65, 2.62, 2.63, 2.75,\n         2.76, 2.72, 2.77, 2.79, 2.73, 2.91, 2.98, 2.91, 2.86, 2.96]]])"
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "# load example TSER dataset\n",
     "X_train, y_train = load_minimal_gas_prices(\"TRAIN\")\n",
@@ -142,10 +112,45 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-12-19T13:14:35.509031300Z",
-     "start_time": "2023-12-19T13:14:35.470130900Z"
+     "end_time": "2024-04-23T11:49:31.825482Z",
+     "start_time": "2024-04-23T11:49:31.814510Z"
     }
-   }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'numpy.ndarray'> <class 'numpy.ndarray'>\n",
+      "(20, 1, 20) (20,)\n",
+      "(20, 1, 20) (20,)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[[2.39, 2.36, 2.45, 2.45, 2.54, 2.69, 2.63, 2.73, 2.7 , 2.73,\n",
+       "         2.68, 2.76, 2.76, 2.68, 2.39, 2.4 , 2.36, 2.6 , 2.77, 2.76]],\n",
+       "\n",
+       "       [[3.02, 3.09, 3.01, 3.01, 2.98, 3.11, 3.13, 3.13, 3.23, 3.36,\n",
+       "         3.31, 3.25, 3.24, 3.23, 3.15, 3.21, 3.36, 3.3 , 3.4 , 3.62]],\n",
+       "\n",
+       "       [[3.95, 4.03, 4.07, 4.35, 4.25, 4.33, 4.45, 4.65, 4.77, 4.71,\n",
+       "         4.66, 4.97, 5.13, 5.21, 5.39, 5.66, 5.52, 5.32, 5.25, 4.96]],\n",
+       "\n",
+       "       [[2.62, 2.63, 2.63, 2.71, 2.71, 2.72, 2.58, 2.58, 2.58, 2.62,\n",
+       "         2.61, 2.67, 2.57, 2.59, 2.63, 2.59, 2.59, 2.5 , 2.55, 2.55]],\n",
+       "\n",
+       "       [[2.44, 2.43, 2.47, 2.48, 2.5 , 2.57, 2.65, 2.62, 2.63, 2.75,\n",
+       "         2.76, 2.72, 2.77, 2.79, 2.73, 2.91, 2.98, 2.91, 2.86, 2.96]]])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 2
   },
   {
    "cell_type": "markdown",
@@ -158,17 +163,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "array([-0.39051194, -0.32556358, -0.26716222, -0.29462747, -0.38713609,\n       -0.35210252, -0.35538663, -0.34188738, -0.386536  , -0.38444336,\n       -0.28417025, -0.34912216, -0.3575563 , -0.29534897, -0.3730631 ,\n       -0.40209279, -0.31955412, -0.34667809, -0.37519843, -0.38101579])"
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "# build a ROCKET regressor and make predictions\n",
     "rocket = RocketRegressor(num_kernels=1000, random_state=0)\n",
@@ -178,10 +172,26 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-12-19T13:14:35.774708300Z",
-     "start_time": "2023-12-19T13:14:35.504064Z"
+     "end_time": "2024-04-23T11:49:31.848420Z",
+     "start_time": "2024-04-23T11:49:31.826488Z"
     }
-   }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.39051194, -0.32556358, -0.26716222, -0.29462747, -0.38713609,\n",
+       "       -0.35210252, -0.35538663, -0.34188738, -0.386536  , -0.38444336,\n",
+       "       -0.28417025, -0.34912216, -0.3575563 , -0.29534897, -0.3730631 ,\n",
+       "       -0.40209279, -0.31955412, -0.34667809, -0.37519843, -0.38101579])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 3
   },
   {
    "cell_type": "markdown",
@@ -194,17 +204,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "[0.0807299822065842,\n 0.08058022963859234,\n 1.3423714791418515,\n 0.0742857643859832,\n 0.10120786111499722]"
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "regressors = [\n",
     "    \"1NN-DTW\",\n",
@@ -235,10 +234,27 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-12-19T13:14:44.721449Z",
-     "start_time": "2023-12-19T13:14:35.766729400Z"
+     "end_time": "2024-04-23T11:49:33.045134Z",
+     "start_time": "2024-04-23T11:49:31.849418Z"
     }
-   }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.0807299822065842,\n",
+       " 0.08058022963859234,\n",
+       " 1.3423714791418515,\n",
+       " 0.0742857643859832,\n",
+       " 0.10120786111499722]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 4
   },
   {
    "cell_type": "markdown",