Upload 142 files

Copper Google Trend Analysis Weekly /Copper Prices.csv

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+Date,Price
+6/28/20,2.7485
+7/5/20,2.8975
+7/12/20,2.9045
+7/19/20,2.8925
+7/26/20,2.868
+8/2/20,2.7925
+8/9/20,2.859
+8/16/20,2.9175
+8/23/20,3.0195
+8/30/20,3.062
+9/6/20,3.0395
+9/13/20,3.116
+9/20/20,2.971
+9/27/20,2.9775
+10/4/20,3.0825
+10/11/20,3.0675
+10/18/20,3.129
+10/25/20,3.0475
+11/1/20,3.154
+11/8/20,3.178
+11/15/20,3.291
+11/22/20,3.4175
+11/29/20,3.5245
+12/6/20,3.528
+12/13/20,3.6325
+12/20/20,3.5625
+12/27/20,3.519
+1/3/21,3.6735
+1/10/21,3.602
+1/17/21,3.626
+1/24/21,3.556
+1/31/21,3.626
+2/7/21,3.788
+2/14/21,4.074
+2/21/21,4.0925
+2/28/21,4.0755
+3/7/21,4.14
+3/14/21,4.113
+3/21/21,4.068
+3/28/21,3.9905
+4/4/21,4.04
+4/11/21,4.168
+4/18/21,4.336
+4/25/21,4.468
+5/2/21,4.7485
+5/9/21,4.6545
+5/16/21,4.481
+5/23/21,4.6775
+5/30/21,4.529
+6/6/21,4.5375
+6/13/21,4.157
+6/20/21,4.292
+6/27/21,4.276
+7/4/21,4.3455
+7/11/21,4.323
+7/18/21,4.4
+7/25/21,4.4825
+8/1/21,4.348
+8/8/21,4.3915
+8/15/21,4.137
+8/22/21,4.3185
+8/29/21,4.334
+9/5/21,4.452
+9/12/21,4.246
+9/19/21,4.2855
+9/26/21,4.1885
+10/3/21,4.2755
+10/10/21,4.7295
+10/17/21,4.498
+10/24/21,4.368
+10/31/21,4.343
+11/7/21,4.4495
+11/14/21,4.4075
+11/21/21,4.2835
+11/28/21,4.267
+12/5/21,4.2865
+12/12/21,4.295
+12/19/21,4.3925
+12/26/21,4.4635
+1/2/22,4.41
+1/9/22,4.4205
+1/16/22,4.524
+1/23/22,4.31
+1/30/22,4.4875
+2/6/22,4.506
+2/13/22,4.5195
+2/20/22,4.485
+2/27/22,4.9375
+3/6/22,4.6255
+3/13/22,4.7395
+3/20/22,4.6985
+3/27/22,4.6885
+4/3/22,4.725
+4/10/22,4.7235
+4/17/22,4.5815
+4/24/22,4.4085
+5/1/22,4.267
+5/8/22,4.175
+5/15/22,4.275
+5/22/22,4.3065
+5/29/22,4.472
+6/5/22,4.2945
+6/12/22,4.0135
+6/19/22,3.7405
+6/26/22,3.604
+7/3/22,3.522
+7/10/22,3.234
+7/17/22,3.3495
+7/24/22,3.5735
+7/31/22,3.552
+8/7/22,3.6685
+8/14/22,3.6645
+8/21/22,3.697
+8/28/22,3.4135
+9/4/22,3.568
+9/11/22,3.5165
+9/18/22,3.343
+9/25/22,3.4125
+10/2/22,3.3865
+10/9/22,3.4235
+10/16/22,3.4745
+10/23/22,3.429
+10/30/22,3.6865
+11/6/22,3.9135
+11/13/22,3.632
+11/20/22,3.6275
+11/27/22,3.8505
+12/4/22,3.8785
+12/11/22,3.7615
+12/18/22,3.809
+12/25/22,3.8105
+1/1/23,3.911
+1/8/23,4.216
+1/15/23,4.2515
+1/22/23,4.2225
+1/29/23,4.0565
+2/5/23,4.0165
+2/12/23,4.1085
+2/19/23,3.952
+2/26/23,4.067
+3/5/23,4.0305
+3/12/23,3.8925
+3/19/23,4.075
+3/26/23,4.0945
+4/2/23,4.0155
+4/9/23,4.1065
+4/16/23,3.98
+4/23/23,3.8905
+4/30/23,3.883
+5/7/23,3.7285
+5/14/23,3.732
+5/21/23,3.682
+5/28/23,3.7275
+6/4/23,3.789
+6/11/23,3.889
+6/18/23,3.8035
+6/25/23,3.7595
+7/2/23,3.782
+7/9/23,3.933
+7/16/23,3.818
+7/23/23,3.9265
+7/30/23,3.8675
+8/6/23,3.719
+8/13/23,3.706
+8/20/23,3.762
+8/27/23,3.852
+9/3/23,3.7165
+9/10/23,3.801
+9/17/23,3.696
+9/24/23,3.7375
+10/1/23,3.6275
+10/8/23,3.571
+10/15/23,3.563
+10/22/23,3.646
+10/29/23,3.6815
+11/5/23,3.587
+11/12/23,3.7385
+11/19/23,3.789
+11/26/23,3.9315
+12/3/23,3.8305
+12/10/23,3.8905
+12/17/23,3.905
+12/24/23,3.8905
+12/31/23,3.806
+1/7/24,3.7405
+1/14/24,3.7865
+1/21/24,3.852
+1/28/24,3.8215
+2/4/24,3.6815
+2/11/24,3.8385
+2/18/24,3.88
+2/25/24,3.8615
+3/3/24,3.8915
+3/10/24,4.1245
+3/17/24,4.0075
+3/24/24,4.007
+3/31/24,4.236
+4/7/24,4.2585
+4/14/24,4.4975
+4/21/24,4.564
+4/28/24,4.557
+5/5/24,4.6625
+5/12/24,5.05
+5/19/24,4.7535
+5/26/24,4.602
+6/2/24,4.4835
+6/9/24,4.495
+6/16/24,4.442
+6/23/24,4.3915
+6/30/24,4.652
+7/7/24,4.593
+7/14/24,4.2365
+7/21/24,4.124
+7/28/24,4.103
+8/4/24,3.993
+8/11/24,4.1435
+8/18/24,4.201
+8/25/24,4.2115
+9/1/24,4.0735
+9/8/24,4.236
+9/15/24,4.343
+9/22/24,4.5995
+9/29/24,4.574
+10/6/24,4.4935
+10/13/24,4.3845
+10/20/24,4.3705
+10/27/24,4.3715
+11/3/24,4.306
+11/10/24,4.0645
+11/17/24,4.086
+11/24/24,4.14
+12/1/24,4.1965
+12/8/24,4.197
+12/15/24,4.1005
+12/22/24,4.122
+12/29/24,4.0735
+1/5/25,4.304
+1/12/25,4.3685
+1/19/25,4.321
+1/26/25,4.279
+2/2/25,4.589
+2/9/25,4.6645
+2/16/25,4.56
+2/23/25,4.548
+3/2/25,4.71
+3/9/25,4.896
+3/16/25,5.1135
+3/23/25,5.13
+3/30/25,4.402
+4/6/25,4.523
+4/13/25,4.739
+4/20/25,4.84
+4/27/25,4.673
+5/4/25,4.653
+5/11/25,4.5935
+5/18/25,4.8365
+5/25/25,4.6775
+6/1/25,4.848
+6/8/25,4.8145
+6/15/25,4.8335
+6/22/25,5.123
+6/29/25,5.1415

Copper Google Trend Analysis Weekly /Data Processing.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f2fc4d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processed: Sudden Factors/Earthquake copper.csv\n",
+      "Processed: Sudden Factors/Brexit.csv\n",
+      "Processed: Sudden Factors/Copper trade war.csv\n",
+      "Processed: Sudden Factors/Trade war.csv\n",
+      "Processed: Sudden Factors/Mining sanctions.csv\n",
+      "Processed: Sudden Factors/Volatility index.csv\n",
+      "Processed: Sudden Factors/Extreme weather.csv\n",
+      "Processed: Sudden Factors/Energy crisis.csv\n",
+      "Processed: Sudden Factors/Hurricane copper.csv\n",
+      "Processed: Sudden Factors/Banking crisis.csv\n",
+      "Processed: Sudden Factors/Middle East conflicts.csv\n",
+      "Processed: Sudden Factors/Mining natural disaster.csv\n",
+      "Processed: Sudden Factors/Conflicts.csv\n",
+      "Processed: Sudden Factors/Iran Iraq War.csv\n",
+      "Processed: Sudden Factors/COVID.csv\n",
+      "Processed: Sudden Factors/Resource nationalism.csv\n",
+      "Processed: Sudden Factors/Pandemic copper.csv\n",
+      "Processed: Sudden Factors/COVID copper.csv\n",
+      "Processed: Sudden Factors/Transportation disruption.csv\n",
+      "Processed: Sudden Factors/Copper tariff.csv\n",
+      "Processed: Sudden Factors/2008 crisis.csv\n",
+      "Processed: Sudden Factors/Mining accidents.csv\n",
+      "Processed: Sudden Factors/Climate change mining.csv\n",
+      "Processed: Sudden Factors/Terrorist attack.csv\n",
+      "Processed: Sudden Factors/China US trade tensions.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "\n",
+    "folder_path = \"Sudden Factors\"\n",
+    "\n",
+    "for filename in os.listdir(folder_path):\n",
+    "    if filename.endswith('.csv'):\n",
+    "        file_path = os.path.join(folder_path, filename)\n",
+    "        \n",
+    "        # Read CSV, skip first two rows, use the third row as header\n",
+    "        df = pd.read_csv(file_path, skiprows=2)\n",
+    "        \n",
+    "        # Convert the first column (assumed to be 'Week') to datetime\n",
+    "        first_col = df.columns[0]\n",
+    "        df[first_col] = pd.to_datetime(df[first_col], errors='coerce')\n",
+    "        \n",
+    "        # Rename columns: first column to 'date', second to 'Interest'\n",
+    "        if len(df.columns) > 1:\n",
+    "            df.rename(columns={first_col: 'Date', df.columns[1]: 'Interest'}, inplace=True)\n",
+    "        \n",
+    "        # Save back to CSV\n",
+    "        df.to_csv(file_path, index=False)\n",
+    "        print(f\"Processed: {file_path}\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "269c0e76",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processed: ./Copper Prices.csv\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/6b/p92_dgbd07ldpbpq29vbpd_m0000gn/T/ipykernel_13497/813422496.py:15: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
+      "  df[first_col] = pd.to_datetime(df[first_col], errors='coerce')\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "\n",
+    "folder_path = \".\"\n",
+    "\n",
+    "for filename in os.listdir(folder_path):\n",
+    "    if filename.endswith('.csv'):\n",
+    "        file_path = os.path.join(folder_path, filename)\n",
+    "        \n",
+    "        # Read CSV, skip first two rows, use the third row as header\n",
+    "        df = pd.read_csv(file_path)\n",
+    "        \n",
+    "        # Convert the first column (assumed to be 'Week') to datetime\n",
+    "        first_col = df.columns[0]\n",
+    "        df[first_col] = pd.to_datetime(df[first_col], errors='coerce')\n",
+    "        \n",
+    "        # Save back to CSV\n",
+    "        df.to_csv(file_path, index=False)\n",
+    "        print(f\"Processed: {file_path}\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8f7d0600",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processed: Supply Factors/Copper mining.csv\n",
+      "Processed: Supply Factors/Peru copper mining.csv\n",
+      "Processed: Supply Factors/Copper ore production.csv\n",
+      "Processed: Supply Factors/Scrap copper prices.csv\n",
+      "Processed: Supply Factors/Copper project.csv\n",
+      "Processed: Supply Factors/Copper mine accident.csv\n",
+      "Processed: Supply Factors/Aluminum production.csv\n",
+      "Processed: Supply Factors/Recycled copper.csv\n",
+      "Processed: Supply Factors/Copper smelter.csv\n",
+      "Processed: Supply Factors/China copper production.csv\n",
+      "Processed: Supply Factors/Peru copper production.csv\n",
+      "Processed: Supply Factors/Copper refinery.csv\n",
+      "Processed: Supply Factors/Copper reserves by country.csv\n",
+      "Processed: Supply Factors/Chile copper production.csv\n",
+      "Processed: Supply Factors/Copper mine strike.csv\n",
+      "Processed: Supply Factors/Global copper production.csv\n",
+      "Processed: Supply Factors/Copper mining cost.csv\n",
+      "Processed: Supply Factors/US copper production.csv\n",
+      "Processed: Supply Factors/Congo copper.csv\n",
+      "Processed: Supply Factors/Copper mine production.csv\n",
+      "Processed: Supply Factors/Copper concentrate.csv\n",
+      "Processed: Supply Factors/Copper ore reserves.csv\n",
+      "Processed: Supply Factors/Copper production by country.csv\n",
+      "Processed: Supply Factors/Steel production.csv\n",
+      "Processed: Supply Factors/Copper mining companies.csv\n",
+      "Processed: Supply Factors/Copper cathode production.csv\n",
+      "Processed: Supply Factors/Copper resource.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "\n",
+    "folder_path = \"Supply Factors\"\n",
+    "\n",
+    "for filename in os.listdir(folder_path):\n",
+    "    if filename.endswith('.csv'):\n",
+    "        file_path = os.path.join(folder_path, filename)\n",
+    "        \n",
+    "        # Read CSV, skip first two rows, use the third row as header\n",
+    "        df = pd.read_csv(file_path)\n",
+    "        first_col = df.columns[0]\n",
+    "        \n",
+    "        # Rename columns: first column to 'date', second to 'Interest'\n",
+    "        if len(df.columns) > 1:\n",
+    "            df.rename(columns={first_col: 'Date', df.columns[1]: 'Interest'}, inplace=True)\n",
+    "        \n",
+    "        # Save back to CSV\n",
+    "        df.to_csv(file_path, index=False)\n",
+    "        print(f\"Processed: {file_path}\")\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Copper Google Trend Analysis Weekly /Demand Factors/5G infrastructure.csv

@@ -0,0 +1,263 @@
+Date,Interest
+2020-06-28,48
+2020-07-05,53
+2020-07-12,91
+2020-07-19,52
+2020-07-26,66
+2020-08-02,50
+2020-08-09,54
+2020-08-16,62
+2020-08-23,58
+2020-08-30,58
+2020-09-06,51
+2020-09-13,48
+2020-09-20,52
+2020-09-27,53
+2020-10-04,42
+2020-10-11,44
+2020-10-18,58
+2020-10-25,29
+2020-11-01,31
+2020-11-08,35
+2020-11-15,47
+2020-11-22,53
+2020-11-29,35
+2020-12-06,26
+2020-12-13,44
+2020-12-20,39
+2020-12-27,34
+2021-01-03,41
+2021-01-10,45
+2021-01-17,50
+2021-01-24,68
+2021-01-31,50
+2021-02-07,38
+2021-02-14,39
+2021-02-21,39
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Copper Google Trend Analysis Weekly /Demand Factors/5G technology.csv

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Copper Google Trend Analysis Weekly /Demand Factors/Building construction copper.csv

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Copper Google Trend Analysis Weekly /Demand Factors/China copper demand.csv

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Copper Google Trend Analysis Weekly /Demand Factors/Construction activity.csv

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Copper Google Trend Analysis Weekly /Demand Factors/Developing country GDP.csv

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Copper Google Trend Analysis Weekly /Demand Factors/Economy recession.csv

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Copper Google Trend Analysis Weekly /Demand Factors/Economy.csv

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Copper Google Trend Analysis Weekly /Demand Factors/Electric vehicle demand.csv

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Copper Google Trend Analysis Weekly /Demand Factors/Electronics manufacturing.csv

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Copper Google Trend Analysis Weekly /Demand Factors/Euro exchange rate.csv

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Copper Google Trend Analysis Weekly /Demand Factors/Foreign exchange rates.csv

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Copper Google Trend Analysis Weekly /Demand Factors/Global GDP growth.csv

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Copper Google Trend Analysis Weekly /Demand Factors/Income.csv

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Copper Google Trend Analysis Weekly /Demand Factors/Infrastructure spending.csv

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Copper Google Trend Analysis Weekly /Demand Factors/Manufacturing PMI.csv

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Copper Google Trend Analysis Weekly /Demand Factors/Power grid expansion.csv

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Copper Google Trend Analysis Weekly /Demand Factors/RMB exchange rate.csv

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Copper Google Trend Analysis Weekly /Demand Factors/Renewable energy.csv

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Copper Google Trend Analysis Weekly /Demand Factors/World GDP by country.csv

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Copper Google Trend Analysis Weekly /Direction Classification.ipynb

@@ -0,0 +1,1166 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ddec01d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Table 4 – Augmented Dickey-Fuller\n"
+     ]
+    },
+    {
+     "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>Level p</th>\n",
+       "      <th>Diff p</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Building construction copper</th>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>US copper production</th>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Copper mining cost</th>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Commodity speculation</th>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chile copper production</th>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COVID</th>\n",
+       "      <td>0.7920</td>\n",
+       "      <td>0.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Terrorist attack</th>\n",
+       "      <td>0.8097</td>\n",
+       "      <td>0.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Electronics manufacturing</th>\n",
+       "      <td>0.8776</td>\n",
+       "      <td>0.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>China US trade tensions</th>\n",
+       "      <td>0.9989</td>\n",
+       "      <td>0.0002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Copper tariff</th>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>98 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              Level p  Diff p\n",
+       "Building construction copper   0.0000  0.0000\n",
+       "US copper production           0.0000  0.0000\n",
+       "Copper mining cost             0.0000  0.0000\n",
+       "Commodity speculation          0.0000  0.0000\n",
+       "Chile copper production        0.0000  0.0000\n",
+       "...                               ...     ...\n",
+       "COVID                          0.7920  0.0000\n",
+       "Terrorist attack               0.8097  0.0000\n",
+       "Electronics manufacturing      0.8776  0.0000\n",
+       "China US trade tensions        0.9989  0.0002\n",
+       "Copper tariff                  1.0000  0.0000\n",
+       "\n",
+       "[98 rows x 2 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Table 5 – Cointegration\n"
+     ]
+    },
+    {
+     "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>t</th>\n",
+       "      <th>p</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Series</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Iran Iraq War</th>\n",
+       "      <td>-2.8943</td>\n",
+       "      <td>0.1375</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5G technology</th>\n",
+       "      <td>-2.8166</td>\n",
+       "      <td>0.1605</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Electronics manufacturing</th>\n",
+       "      <td>-2.7911</td>\n",
+       "      <td>0.1685</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Copper ore reserves</th>\n",
+       "      <td>-2.7675</td>\n",
+       "      <td>0.1762</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Currency exchange rates</th>\n",
+       "      <td>-2.7040</td>\n",
+       "      <td>0.1981</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Copper concentrate</th>\n",
+       "      <td>-2.6545</td>\n",
+       "      <td>0.2164</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Middle East conflicts</th>\n",
+       "      <td>-2.6240</td>\n",
+       "      <td>0.2281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mining sanctions</th>\n",
+       "      <td>-2.5874</td>\n",
+       "      <td>0.2420</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Yahoo finance</th>\n",
+       "      <td>-2.5775</td>\n",
+       "      <td>0.2460</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RMB exchange rate</th>\n",
+       "      <td>-2.5758</td>\n",
+       "      <td>0.2467</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Energy crisis</th>\n",
+       "      <td>-2.5618</td>\n",
+       "      <td>0.2525</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Scrap copper prices</th>\n",
+       "      <td>-2.5581</td>\n",
+       "      <td>0.2541</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Manufacturing PMI</th>\n",
+       "      <td>-2.5548</td>\n",
+       "      <td>0.2554</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COVID</th>\n",
+       "      <td>-2.5403</td>\n",
+       "      <td>0.2616</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Extreme weather</th>\n",
+       "      <td>-2.5326</td>\n",
+       "      <td>0.2649</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Euro exchange rate</th>\n",
+       "      <td>-2.5289</td>\n",
+       "      <td>0.2665</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Copper tariff</th>\n",
+       "      <td>-2.5263</td>\n",
+       "      <td>0.2676</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>China US trade tensions</th>\n",
+       "      <td>-2.5121</td>\n",
+       "      <td>0.2738</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COVID copper</th>\n",
+       "      <td>-2.5116</td>\n",
+       "      <td>0.2740</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Electric vehicle demand</th>\n",
+       "      <td>-2.4723</td>\n",
+       "      <td>0.2914</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bloomberg</th>\n",
+       "      <td>-2.3920</td>\n",
+       "      <td>0.3286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Copper price forecast</th>\n",
+       "      <td>-2.3662</td>\n",
+       "      <td>0.3410</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Terrorist attack</th>\n",
+       "      <td>-2.3257</td>\n",
+       "      <td>0.3608</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Trade war</th>\n",
+       "      <td>-2.3010</td>\n",
+       "      <td>0.3731</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Global GDP growth</th>\n",
+       "      <td>-2.2684</td>\n",
+       "      <td>0.3894</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>World GDP by country</th>\n",
+       "      <td>-2.2533</td>\n",
+       "      <td>0.3970</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Inflation expectations</th>\n",
+       "      <td>-2.2304</td>\n",
+       "      <td>0.4087</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                t      p\n",
+       "Series                                  \n",
+       "Iran Iraq War             -2.8943 0.1375\n",
+       "5G technology             -2.8166 0.1605\n",
+       "Electronics manufacturing -2.7911 0.1685\n",
+       "Copper ore reserves       -2.7675 0.1762\n",
+       "Currency exchange rates   -2.7040 0.1981\n",
+       "Copper concentrate        -2.6545 0.2164\n",
+       "Middle East conflicts     -2.6240 0.2281\n",
+       "Mining sanctions          -2.5874 0.2420\n",
+       "Yahoo finance             -2.5775 0.2460\n",
+       "RMB exchange rate         -2.5758 0.2467\n",
+       "Energy crisis             -2.5618 0.2525\n",
+       "Scrap copper prices       -2.5581 0.2541\n",
+       "Manufacturing PMI         -2.5548 0.2554\n",
+       "COVID                     -2.5403 0.2616\n",
+       "Extreme weather           -2.5326 0.2649\n",
+       "Euro exchange rate        -2.5289 0.2665\n",
+       "Copper tariff             -2.5263 0.2676\n",
+       "China US trade tensions   -2.5121 0.2738\n",
+       "COVID copper              -2.5116 0.2740\n",
+       "Electric vehicle demand   -2.4723 0.2914\n",
+       "Bloomberg                 -2.3920 0.3286\n",
+       "Copper price forecast     -2.3662 0.3410\n",
+       "Terrorist attack          -2.3257 0.3608\n",
+       "Trade war                 -2.3010 0.3731\n",
+       "Global GDP growth         -2.2684 0.3894\n",
+       "World GDP by country      -2.2533 0.3970\n",
+       "Inflation expectations    -2.2304 0.4087"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Table 6 – Granger causality (all lags)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
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+       "\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>Series</th>\n",
+       "      <th>Lag</th>\n",
+       "      <th>p</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>5G technology</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.9159</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>5G technology</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.4953</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>5G technology</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.4765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>5G technology</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.5444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>5G technology</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.3709</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>211</th>\n",
+       "      <td>Yahoo finance</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.1242</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>212</th>\n",
+       "      <td>Yahoo finance</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0.1664</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>213</th>\n",
+       "      <td>Yahoo finance</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0.2101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>214</th>\n",
+       "      <td>Yahoo finance</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.2764</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>215</th>\n",
+       "      <td>Yahoo finance</td>\n",
+       "      <td>24</td>\n",
+       "      <td>0.3186</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>648 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Series  Lag      p\n",
+       "24   5G technology    1 0.9159\n",
+       "25   5G technology    2 0.4953\n",
+       "26   5G technology    3 0.4765\n",
+       "27   5G technology    4 0.5444\n",
+       "28   5G technology    5 0.3709\n",
+       "..             ...  ...    ...\n",
+       "211  Yahoo finance   20 0.1242\n",
+       "212  Yahoo finance   21 0.1664\n",
+       "213  Yahoo finance   22 0.2101\n",
+       "214  Yahoo finance   23 0.2764\n",
+       "215  Yahoo finance   24 0.3186\n",
+       "\n",
+       "[648 rows x 3 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Feature Selection Results:\n",
+      "\n",
+      "p-value < 0.01: 6 series\n",
+      "['Copper ore reserves', 'Mining sanctions', 'Extreme weather', 'Euro exchange rate', 'Copper tariff', 'Terrorist attack']\n",
+      "\n",
+      "p-value < 0.05: 12 series\n",
+      "['Iran Iraq War', 'Electronics manufacturing', 'Copper ore reserves', 'Mining sanctions', 'Yahoo finance', 'Scrap copper prices', 'Extreme weather', 'Euro exchange rate', 'Copper tariff', 'China US trade tensions', 'Terrorist attack', 'Trade war']\n",
+      "\n",
+      "p-value < 0.10: 13 series\n",
+      "['Iran Iraq War', 'Electronics manufacturing', 'Copper ore reserves', 'Middle East conflicts', 'Mining sanctions', 'Yahoo finance', 'Scrap copper prices', 'Extreme weather', 'Euro exchange rate', 'Copper tariff', 'China US trade tensions', 'Terrorist attack', 'Trade war']\n",
+      "\n",
+      "Table 7 – Granger-causal summary\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Min p</th>\n",
+       "      <th>Best Lag</th>\n",
+       "      <th>p&lt;0.01</th>\n",
+       "      <th>p&lt;0.05</th>\n",
+       "      <th>p&lt;0.10</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Series</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Extreme weather</th>\n",
+       "      <td>0.0007</td>\n",
+       "      <td>7</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Terrorist attack</th>\n",
+       "      <td>0.0008</td>\n",
+       "      <td>15</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Euro exchange rate</th>\n",
+       "      <td>0.0024</td>\n",
+       "      <td>9</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Copper tariff</th>\n",
+       "      <td>0.0040</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Copper ore reserves</th>\n",
+       "      <td>0.0044</td>\n",
+       "      <td>21</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mining sanctions</th>\n",
+       "      <td>0.0057</td>\n",
+       "      <td>24</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Trade war</th>\n",
+       "      <td>0.0121</td>\n",
+       "      <td>9</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Scrap copper prices</th>\n",
+       "      <td>0.0196</td>\n",
+       "      <td>21</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>China US trade tensions</th>\n",
+       "      <td>0.0263</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Yahoo finance</th>\n",
+       "      <td>0.0356</td>\n",
+       "      <td>15</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Electronics manufacturing</th>\n",
+       "      <td>0.0371</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Iran Iraq War</th>\n",
+       "      <td>0.0394</td>\n",
+       "      <td>6</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Middle East conflicts</th>\n",
+       "      <td>0.0580</td>\n",
+       "      <td>8</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Copper concentrate</th>\n",
+       "      <td>0.1174</td>\n",
+       "      <td>15</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COVID</th>\n",
+       "      <td>0.1325</td>\n",
+       "      <td>23</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Currency exchange rates</th>\n",
+       "      <td>0.1420</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Global GDP growth</th>\n",
+       "      <td>0.1694</td>\n",
+       "      <td>16</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COVID copper</th>\n",
+       "      <td>0.1707</td>\n",
+       "      <td>23</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Inflation expectations</th>\n",
+       "      <td>0.2726</td>\n",
+       "      <td>6</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RMB exchange rate</th>\n",
+       "      <td>0.2776</td>\n",
+       "      <td>15</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Manufacturing PMI</th>\n",
+       "      <td>0.3599</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bloomberg</th>\n",
+       "      <td>0.3698</td>\n",
+       "      <td>17</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5G technology</th>\n",
+       "      <td>0.3709</td>\n",
+       "      <td>5</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>World GDP by country</th>\n",
+       "      <td>0.4347</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Energy crisis</th>\n",
+       "      <td>0.5680</td>\n",
+       "      <td>5</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Copper price forecast</th>\n",
+       "      <td>0.7506</td>\n",
+       "      <td>6</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Electric vehicle demand</th>\n",
+       "      <td>0.7906</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           Min p  Best Lag  p<0.01  p<0.05  p<0.10\n",
+       "Series                                                            \n",
+       "Extreme weather           0.0007         7    True    True    True\n",
+       "Terrorist attack          0.0008        15    True    True    True\n",
+       "Euro exchange rate        0.0024         9    True    True    True\n",
+       "Copper tariff             0.0040         6    True    True    True\n",
+       "Copper ore reserves       0.0044        21    True    True    True\n",
+       "Mining sanctions          0.0057        24    True    True    True\n",
+       "Trade war                 0.0121         9   False    True    True\n",
+       "Scrap copper prices       0.0196        21   False    True    True\n",
+       "China US trade tensions   0.0263         1   False    True    True\n",
+       "Yahoo finance             0.0356        15   False    True    True\n",
+       "Electronics manufacturing 0.0371         2   False    True    True\n",
+       "Iran Iraq War             0.0394         6   False    True    True\n",
+       "Middle East conflicts     0.0580         8   False   False    True\n",
+       "Copper concentrate        0.1174        15   False   False   False\n",
+       "COVID                     0.1325        23   False   False   False\n",
+       "Currency exchange rates   0.1420         1   False   False   False\n",
+       "Global GDP growth         0.1694        16   False   False   False\n",
+       "COVID copper              0.1707        23   False   False   False\n",
+       "Inflation expectations    0.2726         6   False   False   False\n",
+       "RMB exchange rate         0.2776        15   False   False   False\n",
+       "Manufacturing PMI         0.3599         1   False   False   False\n",
+       "Bloomberg                 0.3698        17   False   False   False\n",
+       "5G technology             0.3709         5   False   False   False\n",
+       "World GDP by country      0.4347         3   False   False   False\n",
+       "Energy crisis             0.5680         5   False   False   False\n",
+       "Copper price forecast     0.7506         6   False   False   False\n",
+       "Electric vehicle demand   0.7906         2   False   False   False"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Accuracy comparison – direction prediction\n"
+     ]
+    },
+    {
+     "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>Scenario</th>\n",
+       "      <th>0.05</th>\n",
+       "      <th>0.10</th>\n",
+       "      <th>without</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Model</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DecTree</th>\n",
+       "      <td>46.67%</td>\n",
+       "      <td>57.78%</td>\n",
+       "      <td>46.67%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HMM</th>\n",
+       "      <td>55.56%</td>\n",
+       "      <td>55.56%</td>\n",
+       "      <td>55.56%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LogReg</th>\n",
+       "      <td>44.44%</td>\n",
+       "      <td>48.89%</td>\n",
+       "      <td>51.11%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RandForest</th>\n",
+       "      <td>46.67%</td>\n",
+       "      <td>44.44%</td>\n",
+       "      <td>46.67%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SVM</th>\n",
+       "      <td>51.11%</td>\n",
+       "      <td>48.89%</td>\n",
+       "      <td>62.22%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Stack</th>\n",
+       "      <td>44.44%</td>\n",
+       "      <td>40.00%</td>\n",
+       "      <td>55.56%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Voting</th>\n",
+       "      <td>48.89%</td>\n",
+       "      <td>48.89%</td>\n",
+       "      <td>46.67%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Scenario      0.05    0.10 without\n",
+       "Model                             \n",
+       "DecTree     46.67%  57.78%  46.67%\n",
+       "HMM         55.56%  55.56%  55.56%\n",
+       "LogReg      44.44%  48.89%  51.11%\n",
+       "RandForest  46.67%  44.44%  46.67%\n",
+       "SVM         51.11%  48.89%  62.22%\n",
+       "Stack       44.44%  40.00%  55.56%\n",
+       "Voting      48.89%  48.89%  46.67%"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ================================================================\n",
+    "#  Copper-Price Direction-of-Move Classification\n",
+    "#  • Baseline (“without”), tier 0·05, tier 0·10\n",
+    "#  • Models: LogReg, DecisionTree, RandomForest, SVM, HMM\n",
+    "#    + Ensembles: Voting, Stacking\n",
+    "# ================================================================\n",
+    "import pathlib, warnings, numpy as np, pandas as pd\n",
+    "from statsmodels.tsa.stattools import adfuller, coint, grangercausalitytests\n",
+    "from sklearn.model_selection import TimeSeriesSplit, KFold\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import (RandomForestClassifier, VotingClassifier,\n",
+    "                              StackingClassifier)\n",
+    "from sklearn.svm import SVC\n",
+    "from hmmlearn.hmm import GaussianHMM\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "pd.set_option(\"display.float_format\", \"{:,.4f}\".format)\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "# ── 1. Load copper price and Google-Trends data ------------------\n",
+    "ROOT = pathlib.Path(\".\")\n",
+    "def load_copper(fp=ROOT / \"Copper Prices.csv\"):\n",
+    "    df = (pd.read_csv(fp)\n",
+    "            .assign(Date=lambda d: pd.to_datetime(d[\"Date\"]))\n",
+    "            .set_index(\"Date\")\n",
+    "            .asfreq(\"W-SUN\"))\n",
+    "    return df[\"Price\"].rename(\"Copper_Price\")\n",
+    "\n",
+    "def load_trends():\n",
+    "    def one_folder(folder):\n",
+    "        frames = []\n",
+    "        for fp in (ROOT / folder).glob(\"*.csv\"):\n",
+    "            key = fp.stem.replace(\",\", \"\")\n",
+    "            t   = pd.read_csv(fp); t.columns = [c.strip() for c in t.columns]\n",
+    "            val = t.columns[1]\n",
+    "            frames.append(t.assign(Date=lambda d: pd.to_datetime(d[t.columns[0]]))\n",
+    "                            .set_index(\"Date\")\n",
+    "                            .asfreq(\"W-SUN\")\n",
+    "                            .rename(columns={val: key}))\n",
+    "        return pd.concat(frames, axis=1) if frames else pd.DataFrame()\n",
+    "    cats = [\"Supply Factors\", \"Demand Factors\",\n",
+    "            \"Speculative Factors\", \"Sudden Factors\"]\n",
+    "    return pd.concat([one_folder(c) for c in cats], axis=1).sort_index()\n",
+    "\n",
+    "copper, trends = load_copper(), load_trends()\n",
+    "data_raw = pd.concat([copper, trends], axis=1).dropna()\n",
+    "\n",
+    "# ── 2. ADF to find I(1) series -----------------------------------\n",
+    "ALPHA_ADF, ALPHA_COINT, MAX_LAG_GC = 0.01, 0.50, 24\n",
+    "adf_p = lambda s: adfuller(s.dropna(), autolag=\"AIC\")[1]\n",
+    "i1 = [c for c in data_raw.columns\n",
+    "      if adf_p(data_raw[c])  > ALPHA_ADF and\n",
+    "         adf_p(data_raw[c].diff()) < ALPHA_ADF and\n",
+    "         c != \"Copper_Price\"]\n",
+    "\n",
+    "# ── 3. Cointegration filter --------------------------------------\n",
+    "cands = [s for s in i1\n",
+    "         if coint(data_raw[\"Copper_Price\"], data_raw[s])[1] < ALPHA_COINT]\n",
+    "\n",
+    "# ── 4. Granger causality tiers -----------------------------------\n",
+    "gc_rows = []\n",
+    "for s in cands:\n",
+    "    if data_raw[s].nunique() <= 1:  continue\n",
+    "    for lag in range(1, MAX_LAG_GC + 1):\n",
+    "        try:\n",
+    "            p = grangercausalitytests(\n",
+    "                    data_raw[[\"Copper_Price\", s]].dropna().values,\n",
+    "                    maxlag=lag, verbose=False)[lag][0][\"ssr_ftest\"][1]\n",
+    "            gc_rows.append({\"Series\": s, \"Lag\": lag, \"p\": p})\n",
+    "        except Exception:\n",
+    "            continue\n",
+    "df_gc = pd.DataFrame(gc_rows)\n",
+    "\n",
+    "TIERS = {0.05: [s for s in cands if df_gc.loc[df_gc[\"Series\"] == s, \"p\"].min() < 0.05],\n",
+    "         0.10: [s for s in cands if df_gc.loc[df_gc[\"Series\"] == s, \"p\"].min() < 0.10]}\n",
+    "\n",
+    "# ────────────────────────────────────────────────────────────────\n",
+    "#  Tables 4 → 7  +  Feature Selection Results   (no CSV, no .style)\n",
+    "# ────────────────────────────────────────────────────────────────\n",
+    "from IPython.display import display\n",
+    "from statsmodels.tools.sm_exceptions import InfeasibleTestError\n",
+    "\n",
+    "# ── Table 4 – Augmented Dickey-Fuller ----------------------------\n",
+    "tab4 = pd.DataFrame(\n",
+    "    {\n",
+    "        \"Level p\": [adf_p(data_raw[c]) for c in data_raw.columns],\n",
+    "        \"Diff p\":  [adf_p(data_raw[c].diff()) for c in data_raw.columns],\n",
+    "    },\n",
+    "    index=data_raw.columns,\n",
+    ").sort_values(\"Level p\")\n",
+    "\n",
+    "print(\"\\nTable 4 – Augmented Dickey-Fuller\")\n",
+    "display(tab4)\n",
+    "\n",
+    "# Identify I(1) candidates\n",
+    "i1 = tab4.index[\n",
+    "    (tab4[\"Level p\"] > ALPHA_ADF) & (tab4[\"Diff p\"] < ALPHA_ADF)\n",
+    "]\n",
+    "\n",
+    "# ── Table 5 – Engle-Granger cointegration ------------------------\n",
+    "rows = []\n",
+    "for s in i1:\n",
+    "    if s == \"Copper_Price\":\n",
+    "        continue\n",
+    "    t_stat, p_val, *_ = coint(data_raw[\"Copper_Price\"], data_raw[s])\n",
+    "    rows.append({\"Series\": s, \"t\": t_stat, \"p\": p_val})\n",
+    "\n",
+    "df_coint = pd.DataFrame(rows).set_index(\"Series\").sort_values(\"p\")\n",
+    "\n",
+    "print(\"\\nTable 5 – Cointegration\")\n",
+    "display(df_coint)\n",
+    "\n",
+    "cointegrated = df_coint.index[df_coint[\"p\"] < ALPHA_COINT]\n",
+    "\n",
+    "# ── Table 6 – full Granger matrix --------------------------------\n",
+    "gc_records = []\n",
+    "for s in cointegrated:\n",
+    "    if data_raw[s].nunique() <= 1:\n",
+    "        continue\n",
+    "    for lag in range(1, MAX_LAG_GC + 1):\n",
+    "        try:\n",
+    "            p = grangercausalitytests(\n",
+    "                data_raw[[\"Copper_Price\", s]].dropna().values,\n",
+    "                maxlag=lag,\n",
+    "                verbose=False,\n",
+    "            )[lag][0][\"ssr_ftest\"][1]\n",
+    "            gc_records.append({\"Series\": s, \"Lag\": lag, \"p\": p})\n",
+    "        except InfeasibleTestError:\n",
+    "            continue\n",
+    "\n",
+    "df_gc = pd.DataFrame(gc_records).sort_values([\"Series\", \"Lag\"])\n",
+    "\n",
+    "print(\"\\nTable 6 – Granger causality (all lags)\")\n",
+    "display(df_gc)\n",
+    "\n",
+    "# ── Feature Selection Results ------------------------------------\n",
+    "tiers = {\n",
+    "    a: [\n",
+    "        s\n",
+    "        for s in cointegrated\n",
+    "        if df_gc.loc[df_gc[\"Series\"] == s, \"p\"].min() < a\n",
+    "    ]\n",
+    "    for a in TIERS_TO_BUILD\n",
+    "}\n",
+    "\n",
+    "print(\"\\nFeature Selection Results:\")\n",
+    "for a in TIERS_TO_BUILD:\n",
+    "    print(f\"\\np-value < {a:.2f}: {len(tiers[a])} series\")\n",
+    "    print(tiers[a])\n",
+    "\n",
+    "# ── Table 7 – Granger-causal summary -----------------------------\n",
+    "summary_rows = []\n",
+    "for series in cointegrated:\n",
+    "    subset = df_gc.loc[df_gc[\"Series\"] == series, [\"Lag\", \"p\"]]\n",
+    "    best    = subset.loc[subset[\"p\"].idxmin()]\n",
+    "    summary_rows.append(\n",
+    "        {\n",
+    "            \"Series\":  series,\n",
+    "            \"Min p\":   float(best[\"p\"]),\n",
+    "            \"Best Lag\":int(best[\"Lag\"]),\n",
+    "            \"p<0.01\":  best[\"p\"] < 0.01,\n",
+    "            \"p<0.05\":  best[\"p\"] < 0.05,\n",
+    "            \"p<0.10\":  best[\"p\"] < 0.10,\n",
+    "        }\n",
+    "    )\n",
+    "\n",
+    "df_summary = pd.DataFrame(summary_rows).set_index(\"Series\").sort_values(\"Min p\")\n",
+    "\n",
+    "print(\"\\nTable 7 – Granger-causal summary\")\n",
+    "display(df_summary)\n",
+    "\n",
+    "\n",
+    "# ── 5. Helper: build lag-1 feature frame --------------------------\n",
+    "def lag_df(feats, lag=1):\n",
+    "    base = {\"Copper_Price\": data_raw[\"Copper_Price\"]}\n",
+    "    base.update({f\"{f}_lag{lag}\": data_raw[f].shift(lag) for f in feats})\n",
+    "    return pd.DataFrame(base).dropna()\n",
+    "\n",
+    "# ── 6. Scenarios --------------------------------------------------\n",
+    "SCENARIOS = {\n",
+    "    \"without\": [],          # copper-only baseline\n",
+    "    \"0.05\"  : TIERS[0.05],\n",
+    "    \"0.10\"  : TIERS[0.10],\n",
+    "}\n",
+    "\n",
+    "# ── 7. Model factories -------------------------------------------\n",
+    "MODEL_FNS = {\n",
+    "    \"LogReg\"   : lambda: make_pipeline(StandardScaler(),\n",
+    "                                       LogisticRegression(max_iter=1000,\n",
+    "                                                          random_state=42)),\n",
+    "    \"DecTree\"  : lambda: DecisionTreeClassifier(random_state=42),\n",
+    "    \"RandForest\":lambda: RandomForestClassifier(n_estimators=500,\n",
+    "                                                random_state=42),\n",
+    "    \"SVM\"      : lambda: make_pipeline(StandardScaler(),\n",
+    "                                       SVC(kernel=\"rbf\",\n",
+    "                                           probability=True,\n",
+    "                                           random_state=42)),\n",
+    "}\n",
+    "\n",
+    "records = []\n",
+    "\n",
+    "# ── 8. Run classification for each scenario ----------------------\n",
+    "for scen, feats in SCENARIOS.items():\n",
+    "    # A. dataset\n",
+    "    if scen == \"without\":\n",
+    "        df = (pd.concat([data_raw[\"Copper_Price\"],\n",
+    "                         data_raw[\"Copper_Price\"].shift(1)\n",
+    "                                 .rename(\"Copper_Price_lag1\")],\n",
+    "                        axis=1)\n",
+    "                .dropna())\n",
+    "    else:\n",
+    "        df = lag_df(feats, lag=1)\n",
+    "    # label\n",
+    "    df[\"y\"] = (df[\"Copper_Price\"].diff().shift(-1) > 0).astype(int)\n",
+    "    df.dropna(inplace=True)\n",
+    "\n",
+    "    X, y = df.drop(columns=[\"Copper_Price\", \"y\"]), df[\"y\"]\n",
+    "    cut  = int(len(X) * 0.83)\n",
+    "    X_tr, X_te, y_tr, y_te = X.iloc[:cut], X.iloc[cut:], y.iloc[:cut], y.iloc[cut:]\n",
+    "\n",
+    "    # B. supervised models\n",
+    "    fitted = {n: fn() for n, fn in MODEL_FNS.items()}\n",
+    "    for n, clf in fitted.items():\n",
+    "        clf.fit(X_tr, y_tr)\n",
+    "\n",
+    "    # C. HMM\n",
+    "    rets      = df[\"Copper_Price\"].pct_change().dropna().values.reshape(-1, 1)\n",
+    "    split_pt  = cut - 1\n",
+    "    hmm       = GaussianHMM(n_components=2, covariance_type=\"full\",\n",
+    "                            n_iter=100, random_state=42).fit(rets[:split_pt])\n",
+    "    states    = hmm.predict(rets[split_pt:])                 # length == len(y_te)\n",
+    "    state_map = {s: int(mu > 0) for s, mu in enumerate(hmm.means_.flatten())}\n",
+    "    y_pred_hmm = pd.Series([state_map[s] for s in states[:len(y_te)]],\n",
+    "                           index=y_te.index)\n",
+    "\n",
+    "    # D. ensembles (use KFold to satisfy cross_val_predict)\n",
+    "    kf = KFold(n_splits=5, shuffle=False)\n",
+    "    voting = VotingClassifier([(n, c) for n, c in fitted.items()],\n",
+    "                              voting=\"hard\").fit(X_tr, y_tr)\n",
+    "    stack  = StackingClassifier([(n, c) for n, c in fitted.items()],\n",
+    "                                final_estimator=LogisticRegression(max_iter=1000,\n",
+    "                                                                    random_state=42),\n",
+    "                                cv=kf, n_jobs=-1).fit(X_tr, y_tr)\n",
+    "\n",
+    "    # E. accuracy scores\n",
+    "    scores = {n: accuracy_score(y_te, c.predict(X_te)) for n, c in fitted.items()}\n",
+    "    scores[\"HMM\"]    = accuracy_score(y_te, y_pred_hmm)\n",
+    "    scores[\"Voting\"] = accuracy_score(y_te, voting.predict(X_te))\n",
+    "    scores[\"Stack\"]  = accuracy_score(y_te, stack.predict(X_te))\n",
+    "\n",
+    "    for mdl, acc in scores.items():\n",
+    "        records.append({\"Model\": mdl, \"Scenario\": scen, \"Accuracy\": acc})\n",
+    "\n",
+    "# ── 9. Show comparison table -------------------------------------\n",
+    "acc_df = (pd.DataFrame(records)\n",
+    "            .pivot_table(index=\"Model\", columns=\"Scenario\", values=\"Accuracy\")\n",
+    "            .applymap(\"{:.2%}\".format)\n",
+    "            .sort_index())\n",
+    "\n",
+    "print(\"\\nAccuracy comparison – direction prediction\")\n",
+    "display(acc_df)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Copper Google Trend Analysis Weekly /Model Results/Forecast_2025-07-13.csv

@@ -0,0 +1,2 @@
+,2025-07-13
+Forecast,5.462528850121129

Copper Google Trend Analysis Weekly /Model Results/Horizon_4w/Table_10_Dstat.csv

@@ -0,0 +1,17 @@
+,Without Google Trends,Without Google Trends,p-value ≤ 5%,p-value ≤ 5%,p-value ≤ 10%,p-value ≤ 10%
+,Dstat,No.,Dstat,No.,Dstat,No.
+Model,,,,,,
+RF,0.50,11,0.45,9,0.36,13
+ET,0.57,3,0.50,3,0.48,4
+AdaBoost,0.50,11,0.52,1,0.52,1
+GBDT,0.43,13,0.50,3,0.45,7
+XGB,0.34,14,0.02,14,0.02,14
+LR,0.57,3,0.45,9,0.48,4
+SVR,0.57,3,0.45,9,0.48,4
+MLP,0.57,3,0.50,3,0.52,1
+RF_LR,0.57,3,0.48,8,0.45,7
+ET_LR,0.59,1,0.50,3,0.45,7
+AdaBoost_LR,0.57,3,0.45,9,0.43,10
+GBDT_LR,0.59,1,0.52,1,0.52,1
+XGB_LR,0.57,3,0.45,9,0.43,10
+ET_AdaBoost_GBDT_Xgboost_RF_LR,0.57,3,0.50,3,0.43,10

Copper Google Trend Analysis Weekly /Model Results/Horizon_4w/Table_11_Comparison.csv

@@ -0,0 +1,43 @@
+Model,Criterion,0.01,0.05,0.10,without
+AdaBoost,Dstat,0.34,0.52,0.52,0.50
+AdaBoost,MAPE,8.56%,8.05%,8.46%,5.70%
+AdaBoost,RMSE,0.52,0.49,0.51,0.35
+AdaBoost_LR,Dstat,0.45,0.45,0.43,0.57
+AdaBoost_LR,MAPE,9.78%,9.83%,9.67%,5.39%
+AdaBoost_LR,RMSE,0.59,0.56,0.55,0.31
+ET,Dstat,0.41,0.50,0.48,0.57
+ET,MAPE,9.94%,10.63%,10.83%,5.62%
+ET,RMSE,0.58,0.61,0.62,0.35
+ET_AdaBoost_GBDT_Xgboost_RF_LR,Dstat,0.41,0.50,0.43,0.57
+ET_AdaBoost_GBDT_Xgboost_RF_LR,MAPE,8.64%,8.31%,7.63%,5.33%
+ET_AdaBoost_GBDT_Xgboost_RF_LR,RMSE,0.50,0.47,0.44,0.31
+ET_LR,Dstat,0.43,0.50,0.45,0.59
+ET_LR,MAPE,8.78%,8.25%,7.98%,5.40%
+ET_LR,RMSE,0.52,0.47,0.46,0.32
+GBDT,Dstat,0.45,0.50,0.45,0.43
+GBDT,MAPE,9.18%,8.92%,8.23%,5.74%
+GBDT,RMSE,0.54,0.52,0.48,0.35
+GBDT_LR,Dstat,0.34,0.52,0.52,0.59
+GBDT_LR,MAPE,10.42%,10.02%,13.96%,5.49%
+GBDT_LR,RMSE,0.62,0.57,0.81,0.32
+LR,Dstat,0.41,0.45,0.48,0.57
+LR,MAPE,8.72%,11.15%,12.12%,5.23%
+LR,RMSE,0.53,0.72,0.80,0.31
+MLP,Dstat,0.39,0.50,0.52,0.57
+MLP,MAPE,25.06%,43.10%,21.73%,5.37%
+MLP,RMSE,1.74,3.72,1.39,0.31
+RF,Dstat,0.45,0.45,0.36,0.50
+RF,MAPE,10.64%,9.06%,9.33%,5.61%
+RF,RMSE,0.61,0.53,0.55,0.34
+RF_LR,Dstat,0.45,0.48,0.45,0.57
+RF_LR,MAPE,9.74%,8.62%,8.41%,5.44%
+RF_LR,RMSE,0.57,0.49,0.48,0.32
+SVR,Dstat,0.34,0.45,0.48,0.57
+SVR,MAPE,8.65%,10.11%,10.02%,5.39%
+SVR,RMSE,0.53,0.60,0.59,0.33
+XGB,Dstat,0.36,0.02,0.02,0.34
+XGB,MAPE,8.69%,11.91%,11.91%,5.72%
+XGB,RMSE,0.52,0.66,0.66,0.35
+XGB_LR,Dstat,0.41,0.45,0.43,0.57
+XGB_LR,MAPE,10.08%,8.24%,8.19%,5.46%
+XGB_LR,RMSE,0.60,0.48,0.48,0.32

Copper Google Trend Analysis Weekly /Model Results/Horizon_4w/Table_12_ImprovementRates.csv

@@ -0,0 +1,15 @@
+Model,0.01,0.05,0.1
+AdaBoost,-31.82%,4.55%,4.55%
+AdaBoost_LR,-20.00%,-20.00%,-24.00%
+ET,-28.00%,-12.00%,-16.00%
+ET_AdaBoost_GBDT_Xgboost_RF_LR,-28.00%,-12.00%,-24.00%
+ET_LR,-26.92%,-15.38%,-23.08%
+GBDT,5.26%,15.79%,5.26%
+GBDT_LR,-42.31%,-11.54%,-11.54%
+LR,-28.00%,-20.00%,-16.00%
+MLP,-32.00%,-12.00%,-8.00%
+RF,-9.09%,-9.09%,-27.27%
+RF_LR,-20.00%,-16.00%,-20.00%
+SVR,-40.00%,-20.00%,-16.00%
+XGB,6.67%,-93.33%,-93.33%
+XGB_LR,-28.00%,-20.00%,-24.00%

Copper Google Trend Analysis Weekly /Model Results/Horizon_4w/Table_8_RMSE.csv

@@ -0,0 +1,17 @@
+,Without Google Trends,Without Google Trends,p-value ≤ 5%,p-value ≤ 5%,p-value ≤ 10%,p-value ≤ 10%
+,RMSE,No.,RMSE,No.,RMSE,No.
+Model,,,,,,
+RF,0.34,10,0.53,7,0.55,7
+ET,0.35,11,0.61,11,0.62,10
+AdaBoost,0.35,12,0.49,5,0.51,6
+GBDT,0.35,14,0.52,6,0.48,4
+XGB,0.35,13,0.66,12,0.66,11
+LR,0.31,1,0.72,13,0.80,12
+SVR,0.33,9,0.60,10,0.59,9
+MLP,0.31,2,3.72,14,1.39,14
+RF_LR,0.32,6,0.49,4,0.48,3
+ET_LR,0.32,5,0.47,1,0.46,2
+AdaBoost_LR,0.31,4,0.56,8,0.55,8
+GBDT_LR,0.32,8,0.57,9,0.81,13
+XGB_LR,0.32,7,0.48,3,0.48,5
+ET_AdaBoost_GBDT_Xgboost_RF_LR,0.31,3,0.47,2,0.44,1

Copper Google Trend Analysis Weekly /Model Results/Horizon_4w/Table_9_MAPE.csv

@@ -0,0 +1,17 @@
+,Without Google Trends,Without Google Trends,p-value ≤ 5%,p-value ≤ 5%,p-value ≤ 10%,p-value ≤ 10%
+,MAPE,No.,MAPE,No.,MAPE,No.
+Model,,,,,,
+RF,5.61%,10,9.06%,7,9.33%,7
+ET,5.62%,11,10.63%,11,10.83%,10
+AdaBoost,5.70%,12,8.05%,1,8.46%,6
+GBDT,5.74%,14,8.92%,6,8.23%,4
+XGB,5.72%,13,11.91%,13,11.91%,11
+LR,5.23%,1,11.15%,12,12.12%,12
+SVR,5.39%,4,10.11%,10,10.02%,9
+MLP,5.37%,3,43.10%,14,21.73%,14
+RF_LR,5.44%,7,8.62%,5,8.41%,5
+ET_LR,5.40%,6,8.25%,3,7.98%,2
+AdaBoost_LR,5.39%,5,9.83%,8,9.67%,8
+GBDT_LR,5.49%,9,10.02%,9,13.96%,13
+XGB_LR,5.46%,8,8.24%,2,8.19%,3
+ET_AdaBoost_GBDT_Xgboost_RF_LR,5.33%,2,8.31%,4,7.63%,1

Copper Google Trend Analysis Weekly /Model Results/Horizon_52w/Table_10_Dstat.csv

@@ -0,0 +1,17 @@
+,Without Google Trends,Without Google Trends,p-value ≤ 5%,p-value ≤ 5%,p-value ≤ 10%,p-value ≤ 10%
+,Dstat,No.,Dstat,No.,Dstat,No.
+Model,,,,,,
+RF,0.49,5,0.57,3,0.57,1
+ET,0.46,7,0.51,5,0.57,1
+AdaBoost,0.40,13,0.60,2,0.57,1
+GBDT,0.43,10,0.54,4,0.51,6
+XGB,0.03,14,0.14,14,0.31,14
+LR,0.54,1,0.43,10,0.43,10
+SVR,0.54,1,0.63,1,0.57,1
+MLP,0.43,10,0.40,13,0.43,10
+RF_LR,0.49,5,0.43,10,0.43,10
+ET_LR,0.46,7,0.51,5,0.49,7
+AdaBoost_LR,0.51,3,0.49,7,0.40,13
+GBDT_LR,0.43,10,0.49,7,0.46,8
+XGB_LR,0.51,3,0.46,9,0.46,8
+ET_AdaBoost_GBDT_Xgboost_RF_LR,0.46,7,0.43,10,0.54,5

Copper Google Trend Analysis Weekly /Model Results/Horizon_52w/Table_11_Comparison.csv

@@ -0,0 +1,43 @@
+Model,Criterion,0.01,0.05,0.10,without
+AdaBoost,Dstat,0.57,0.60,0.57,0.40
+AdaBoost,MAPE,12.32%,8.24%,7.67%,11.29%
+AdaBoost,RMSE,0.69,0.50,0.48,0.68
+AdaBoost_LR,Dstat,0.40,0.49,0.40,0.51
+AdaBoost_LR,MAPE,9.93%,12.01%,15.34%,12.22%
+AdaBoost_LR,RMSE,0.59,0.70,0.89,0.72
+ET,Dstat,0.57,0.51,0.57,0.46
+ET,MAPE,10.95%,8.29%,7.10%,11.04%
+ET,RMSE,0.64,0.51,0.46,0.67
+ET_AdaBoost_GBDT_Xgboost_RF_LR,Dstat,0.43,0.43,0.54,0.46
+ET_AdaBoost_GBDT_Xgboost_RF_LR,MAPE,10.06%,17.04%,15.55%,10.04%
+ET_AdaBoost_GBDT_Xgboost_RF_LR,RMSE,0.59,0.91,0.89,0.61
+ET_LR,Dstat,0.40,0.51,0.49,0.46
+ET_LR,MAPE,9.75%,15.12%,16.63%,12.60%
+ET_LR,RMSE,0.58,0.85,0.97,0.72
+GBDT,Dstat,0.63,0.54,0.51,0.43
+GBDT,MAPE,11.84%,7.09%,6.57%,10.74%
+GBDT,RMSE,0.66,0.46,0.43,0.65
+GBDT_LR,Dstat,0.34,0.49,0.46,0.43
+GBDT_LR,MAPE,9.81%,11.57%,14.56%,12.45%
+GBDT_LR,RMSE,0.59,0.69,0.87,0.73
+LR,Dstat,0.51,0.43,0.43,0.54
+LR,MAPE,10.38%,9.15%,11.54%,11.82%
+LR,RMSE,0.61,0.57,0.69,0.71
+MLP,Dstat,0.54,0.40,0.43,0.43
+MLP,MAPE,11.35%,9.64%,11.14%,11.76%
+MLP,RMSE,0.67,0.61,0.63,0.70
+RF,Dstat,0.54,0.57,0.57,0.49
+RF,MAPE,11.33%,7.83%,7.15%,10.69%
+RF,RMSE,0.65,0.49,0.46,0.65
+RF_LR,Dstat,0.43,0.43,0.43,0.49
+RF_LR,MAPE,9.61%,13.78%,16.64%,12.27%
+RF_LR,RMSE,0.58,0.78,0.96,0.72
+SVR,Dstat,0.57,0.63,0.57,0.54
+SVR,MAPE,11.26%,10.32%,9.62%,12.00%
+SVR,RMSE,0.65,0.62,0.56,0.71
+XGB,Dstat,0.57,0.14,0.31,0.03
+XGB,MAPE,11.88%,9.44%,9.84%,10.55%
+XGB,RMSE,0.67,0.57,0.59,0.63
+XGB_LR,Dstat,0.43,0.46,0.46,0.51
+XGB_LR,MAPE,9.83%,16.03%,15.25%,11.10%
+XGB_LR,RMSE,0.58,0.87,0.84,0.66