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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "collapsed": true,
+        "id": "4uqLPnRV6nZf",
+        "outputId": "3a5a9c93-0488-4da8-e8fb-5593755ffaad"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: prophet in /usr/local/lib/python3.12/dist-packages (1.1.7)\n",
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+            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.12/dist-packages (from tensorboard~=2.19.0->tensorflow) (3.9)\n",
+            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.12/dist-packages (from tensorboard~=2.19.0->tensorflow) (0.7.2)\n",
+            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from tensorboard~=2.19.0->tensorflow) (3.1.3)\n",
+            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.12/dist-packages (from werkzeug>=1.0.1->tensorboard~=2.19.0->tensorflow) (3.0.3)\n",
+            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich->keras>=3.5.0->tensorflow) (4.0.0)\n",
+            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich->keras>=3.5.0->tensorflow) (2.19.2)\n",
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+          ]
+        }
+      ],
+      "source": [
+        "!pip install prophet tensorflow scikit-learn plotly"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#import libraries\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "from datetime import datetime, timedelta\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "\n",
+        "#statistical_models\n",
+        "from statsmodels.tsa.arima.model import ARIMA\n",
+        "from statsmodels.tsa.stattools import adfuller\n",
+        "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
+        "\n",
+        "#ML\n",
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import LSTM, Dense, Dropout\n",
+        "from tensorflow.keras.optimizers import Adam\n",
+        "# Prophet\n",
+        "from prophet import Prophet\n",
+        "\n",
+        "#visualization\n",
+        "import plotly.graph_objects as go\n",
+        "from plotly.subplots import make_subplots\n"
+      ],
+      "metadata": {
+        "collapsed": true,
+        "id": "ueNcOdXe6zSj"
+      },
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#loading_CSV\n",
+        "df = pd.read_csv('/content/all_stocks_5yr.csv')\n",
+        "\n",
+        "print(f\"Dataset shape: {df.shape}\")\n",
+        "print(\"\\nFirst 5 rows:\")\n",
+        "print(df.head())\n",
+        "\n",
+        "print(\"\\nDataset Info:\")\n",
+        "print(df.info())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "collapsed": true,
+        "id": "vKH6utU-6zXz",
+        "outputId": "30c9d77a-2449-452e-d64f-5a5a627aa225"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Dataset shape: (619040, 7)\n",
+            "\n",
+            "First 5 rows:\n",
+            "         date   open   high    low  close    volume Name\n",
+            "0  2013-02-08  15.07  15.12  14.63  14.75   8407500  AAL\n",
+            "1  2013-02-11  14.89  15.01  14.26  14.46   8882000  AAL\n",
+            "2  2013-02-12  14.45  14.51  14.10  14.27   8126000  AAL\n",
+            "3  2013-02-13  14.30  14.94  14.25  14.66  10259500  AAL\n",
+            "4  2013-02-14  14.94  14.96  13.16  13.99  31879900  AAL\n",
+            "\n",
+            "Dataset Info:\n",
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 619040 entries, 0 to 619039\n",
+            "Data columns (total 7 columns):\n",
+            " #   Column  Non-Null Count   Dtype  \n",
+            "---  ------  --------------   -----  \n",
+            " 0   date    619040 non-null  object \n",
+            " 1   open    619029 non-null  float64\n",
+            " 2   high    619032 non-null  float64\n",
+            " 3   low     619032 non-null  float64\n",
+            " 4   close   619040 non-null  float64\n",
+            " 5   volume  619040 non-null  int64  \n",
+            " 6   Name    619040 non-null  object \n",
+            "dtypes: float64(4), int64(1), object(2)\n",
+            "memory usage: 33.1+ MB\n",
+            "None\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#DATA PREPROCESSING\n",
+        "\n",
+        "def preprocess_custom_data(df):\n",
+        "      # Convert date column to datetime\n",
+        "    data['date'] = pd.to_datetime(data['date'])\n",
+        "\n",
+        "    # Set date as index\n",
+        "    data.set_index('date', inplace=True)\n",
+        "\n",
+        "    # Sort by date to ensure chronological order\n",
+        "    data = data.sort_index()\n",
+        "\n",
+        "    # Use Close price for forecasting\n",
+        "    price_data = data[['close']].copy()\n",
+        "\n",
+        "    # Handle missing values\n",
+        "    price_data = price_data.ffill()\n",
+        "\n",
+        "    # Create features\n",
+        "    price_data['Day'] = price_data.index.day\n",
+        "    price_data['Month'] = price_data.index.month\n",
+        "    price_data['Year'] = price_data.index.year\n",
+        "    price_data['DayOfWeek'] = price_data.index.dayofweek\n",
+        "\n",
+        "    # Calculate returns and volatility\n",
+        "    price_data['Daily_Return'] = price_data['close'].pct_change()\n",
+        "    price_data['Volatility'] = price_data['Daily_Return'].rolling(window=10).std()\n",
+        "\n",
+        "    # Technical indicators\n",
+        "    price_data['SMA_20'] = price_data['close'].rolling(window=20).mean()\n",
+        "    price_data['SMA_50'] = price_data['close'].rolling(window=50).mean()\n",
+        "    price_data['EMA_12'] = price_data['close'].ewm(span=12).mean()\n",
+        "    price_data['EMA_26'] = price_data['close'].ewm(span=26).mean()\n",
+        "    price_data['MACD'] = price_data['EMA_12'] - price_data['EMA_26']\n",
+        "\n",
+        "    # RSI\n",
+        "    delta = price_data['close'].diff()\n",
+        "    gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()\n",
+        "    loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()\n",
+        "    rs = gain / loss\n",
+        "    price_data['RSI'] = 100 - (100 / (1 + rs))\n",
+        "\n",
+        "    # Volume features (if available)\n",
+        "    if 'volume' in data.columns:\n",
+        "        price_data['Volume'] = data['volume']\n",
+        "        price_data['Volume_SMA'] = data['volume'].rolling(window=20).mean()\n",
+        "\n",
+        "    # Remove NaN values\n",
+        "    price_data = price_data.dropna()\n",
+        "\n",
+        "    return price_data\n",
+        "\n",
+        "# Preprocess  data\n",
+        "processed_data = preprocess_custom_data(df)\n",
+        "print(\" Data preprocessing completed!\")\n",
+        "print(f\"Processed data shape: {processed_data.shape}\")\n",
+        "print(f\"Date range: {processed_data.index.min()} to {processed_data.index.max()}\")\n",
+        "print(f\"Total trading days: {len(processed_data)}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Euz71n-RJhd3",
+        "outputId": "e0b180f5-982d-433a-f9e8-0574837b11bd"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " Data preprocessing completed!\n",
+            "Processed data shape: (618991, 15)\n",
+            "Date range: 2013-02-08 00:00:00 to 2018-02-07 00:00:00\n",
+            "Total trading days: 618991\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#preprocessing(using only one stock)\n",
+        "\n",
+        "def preprocess_single_stock(df, stock_name='AAL', max_rows=5000):\n",
+        "    print(f\"Original dataset size: {len(df):,} rows\")\n",
+        "\n",
+        "    # Filter for specific stock\n",
+        "    if 'Name' in df.columns:\n",
+        "        stock_data = df[df['Name'] == stock_name].copy()\n",
+        "        print(f\"Filtered for {stock_name}: {len(stock_data):,} rows\")\n",
+        "    else:\n",
+        "        stock_data = df.copy()\n",
+        "        print(\"No 'Name' column - using all data\")\n",
+        "\n",
+        "    # Convert date and sort\n",
+        "    stock_data['date'] = pd.to_datetime(stock_data['date'])\n",
+        "    stock_data = stock_data.sort_values('date')\n",
+        "\n",
+        "    # Take most recent data if still too large\n",
+        "    if len(stock_data) > max_rows:\n",
+        "        stock_data = stock_data.tail(max_rows)\n",
+        "        print(f\"Limited to most recent {max_rows} rows\")\n",
+        "\n",
+        "    # Set date as index\n",
+        "    stock_data.set_index('date', inplace=True)\n",
+        "\n",
+        "    # Use Close price\n",
+        "    price_data = stock_data[['close']].copy()\n",
+        "    price_data = price_data.ffill()\n",
+        "\n",
+        "    # Basic features only\n",
+        "    price_data['Daily_Return'] = price_data['close'].pct_change()\n",
+        "    price_data['SMA_20'] = price_data['close'].rolling(window=20).mean()\n",
+        "\n",
+        "    price_data = price_data.dropna()\n",
+        "\n",
+        "    print(f\"Final data for {stock_name}: {len(price_data):,} rows\")\n",
+        "    print(f\"Date range: {price_data.index.min()} to {price_data.index.max()}\")\n",
+        "\n",
+        "    return price_data\n",
+        "\n",
+        "processed_data = preprocess_single_stock(df, stock_name='AAL', max_rows=2000)\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oLtds1xqUJty",
+        "outputId": "edad6c5f-b19c-4430-8985-2acf5462bb08"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Original dataset size: 619,040 rows\n",
+            "Filtered for AAL: 1,259 rows\n",
+            "Final data for AAL: 1,240 rows\n",
+            "Date range: 2013-03-08 00:00:00 to 2018-02-07 00:00:00\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#VISUALIZATION\n",
+        "\n",
+        "def plot_simple_analysis(data, stock_name):\n",
+        "    fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n",
+        "    fig.suptitle(f'Stock Analysis - {stock_name}', fontsize=16)\n",
+        "\n",
+        "    # Price trend\n",
+        "    axes[0,0].plot(data.index, data['close'], color='blue', linewidth=1)\n",
+        "    axes[0,0].set_title('Stock Price Trend')\n",
+        "    axes[0,0].set_ylabel('Price')\n",
+        "\n",
+        "    # Daily returns\n",
+        "    axes[0,1].plot(data.index, data['Daily_Return'], color='green', linewidth=1)\n",
+        "    axes[0,1].set_title('Daily Returns')\n",
+        "    axes[0,1].set_ylabel('Return')\n",
+        "\n",
+        "    # Moving averages\n",
+        "    axes[1,0].plot(data.index, data['close'], label='Close', linewidth=1)\n",
+        "    axes[1,0].plot(data.index, data['SMA_20'], label='SMA 20', linewidth=1)\n",
+        "    axes[1,0].set_title('Moving Averages')\n",
+        "    axes[1,0].set_ylabel('Price')\n",
+        "    axes[1,0].legend()\n",
+        "\n",
+        "    # Histogram of returns\n",
+        "    axes[1,1].hist(data['Daily_Return'].dropna(), bins=50, color='orange', alpha=0.7)\n",
+        "    axes[1,1].set_title('Distribution of Daily Returns')\n",
+        "    axes[1,1].set_ylabel('Frequency')\n",
+        "\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "\n",
+        "# Check available stocks\n",
+        "if 'Name' in df.columns:\n",
+        "    print(\"Available stocks:\", df['Name'].unique())\n",
+        "    stock_name = 'AAL'  # Change this if you want a different stock\n",
+        "else:\n",
+        "    stock_name = 'Your Stock'\n",
+        "\n",
+        "plot_simple_analysis(processed_data, stock_name)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "7tdpx9L660jR",
+        "outputId": "ed54fd2e-2362-488f-b9eb-c2483a18d246"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Available stocks: ['AAL' 'AAPL' 'AAP' 'ABBV' 'ABC' 'ABT' 'ACN' 'ADBE' 'ADI' 'ADM' 'ADP'\n",
+            " 'ADSK' 'ADS' 'AEE' 'AEP' 'AES' 'AET' 'AFL' 'AGN' 'AIG' 'AIV' 'AIZ' 'AJG'\n",
+            " 'AKAM' 'ALB' 'ALGN' 'ALK' 'ALLE' 'ALL' 'ALXN' 'AMAT' 'AMD' 'AME' 'AMGN'\n",
+            " 'AMG' 'AMP' 'AMT' 'AMZN' 'ANDV' 'ANSS' 'ANTM' 'AON' 'AOS' 'APA' 'APC'\n",
+            " 'APD' 'APH' 'APTV' 'ARE' 'ARNC' 'ATVI' 'AVB' 'AVGO' 'AVY' 'AWK' 'AXP'\n",
+            " 'AYI' 'AZO' 'A' 'BAC' 'BAX' 'BA' 'BBT' 'BBY' 'BDX' 'BEN' 'BF.B' 'BHF'\n",
+            " 'BHGE' 'BIIB' 'BK' 'BLK' 'BLL' 'BMY' 'BRK.B' 'BSX' 'BWA' 'BXP' 'CAG'\n",
+            " 'CAH' 'CAT' 'CA' 'CBG' 'CBOE' 'CBS' 'CB' 'CCI' 'CCL' 'CDNS' 'CELG' 'CERN'\n",
+            " 'CFG' 'CF' 'CHD' 'CHK' 'CHRW' 'CHTR' 'CINF' 'CI' 'CLX' 'CL' 'CMA' 'CMCSA'\n",
+            " 'CME' 'CMG' 'CMI' 'CMS' 'CNC' 'CNP' 'COF' 'COG' 'COL' 'COO' 'COP' 'COST'\n",
+            " 'COTY' 'CPB' 'CRM' 'CSCO' 'CSRA' 'CSX' 'CTAS' 'CTL' 'CTSH' 'CTXS' 'CVS'\n",
+            " 'CVX' 'CXO' 'C' 'DAL' 'DE' 'DFS' 'DGX' 'DG' 'DHI' 'DHR' 'DISCA' 'DISCK'\n",
+            " 'DISH' 'DIS' 'DLR' 'DLTR' 'DOV' 'DPS' 'DRE' 'DRI' 'DTE' 'DUK' 'DVA' 'DVN'\n",
+            " 'DWDP' 'DXC' 'D' 'EA' 'EBAY' 'ECL' 'ED' 'EFX' 'EIX' 'EL' 'EMN' 'EMR'\n",
+            " 'EOG' 'EQIX' 'EQR' 'EQT' 'ESRX' 'ESS' 'ES' 'ETFC' 'ETN' 'ETR' 'EVHC' 'EW'\n",
+            " 'EXC' 'EXPD' 'EXPE' 'EXR' 'FAST' 'FBHS' 'FB' 'FCX' 'FDX' 'FE' 'FFIV'\n",
+            " 'FISV' 'FIS' 'FITB' 'FLIR' 'FLR' 'FLS' 'FL' 'FMC' 'FOXA' 'FOX' 'FRT'\n",
+            " 'FTI' 'FTV' 'F' 'GD' 'GE' 'GGP' 'GILD' 'GIS' 'GLW' 'GM' 'GOOGL' 'GOOG'\n",
+            " 'GPC' 'GPN' 'GPS' 'GRMN' 'GS' 'GT' 'GWW' 'HAL' 'HAS' 'HBAN' 'HBI' 'HCA'\n",
+            " 'HCN' 'HCP' 'HD' 'HES' 'HIG' 'HII' 'HLT' 'HOG' 'HOLX' 'HON' 'HPE' 'HPQ'\n",
+            " 'HP' 'HRB' 'HRL' 'HRS' 'HSIC' 'HST' 'HSY' 'HUM' 'IBM' 'ICE' 'IDXX' 'IFF'\n",
+            " 'ILMN' 'INCY' 'INFO' 'INTC' 'INTU' 'IPG' 'IP' 'IQV' 'IRM' 'IR' 'ISRG'\n",
+            " 'ITW' 'IT' 'IVZ' 'JBHT' 'JCI' 'JEC' 'JNJ' 'JNPR' 'JPM' 'JWN' 'KEY' 'KHC'\n",
+            " 'KIM' 'KLAC' 'KMB' 'KMI' 'KMX' 'KORS' 'KO' 'KR' 'KSS' 'KSU' 'K' 'LB'\n",
+            " 'LEG' 'LEN' 'LH' 'LKQ' 'LLL' 'LLY' 'LMT' 'LNC' 'LNT' 'LOW' 'LRCX' 'LUK'\n",
+            " 'LUV' 'LYB' 'L' 'MAA' 'MAC' 'MAR' 'MAS' 'MAT' 'MA' 'MCD' 'MCHP' 'MCK'\n",
+            " 'MCO' 'MDLZ' 'MDT' 'MET' 'MGM' 'MHK' 'MKC' 'MLM' 'MMC' 'MMM' 'MNST' 'MON'\n",
+            " 'MOS' 'MO' 'MPC' 'MRK' 'MRO' 'MSFT' 'MSI' 'MS' 'MTB' 'MTD' 'MU' 'MYL' 'M'\n",
+            " 'NAVI' 'NBL' 'NCLH' 'NDAQ' 'NEE' 'NEM' 'NFLX' 'NFX' 'NI' 'NKE' 'NLSN'\n",
+            " 'NOC' 'NOV' 'NRG' 'NSC' 'NTAP' 'NTRS' 'NUE' 'NVDA' 'NWL' 'NWSA' 'NWS'\n",
+            " 'OKE' 'OMC' 'ORCL' 'ORLY' 'OXY' 'O' 'PAYX' 'PBCT' 'PCAR' 'PCG' 'PCLN'\n",
+            " 'PDCO' 'PEG' 'PEP' 'PFE' 'PFG' 'PGR' 'PG' 'PHM' 'PH' 'PKG' 'PKI' 'PLD'\n",
+            " 'PM' 'PNC' 'PNR' 'PNW' 'PPG' 'PPL' 'PRGO' 'PRU' 'PSA' 'PSX' 'PVH' 'PWR'\n",
+            " 'PXD' 'PX' 'PYPL' 'QCOM' 'QRVO' 'RCL' 'REGN' 'REG' 'RE' 'RF' 'RHI' 'RHT'\n",
+            " 'RJF' 'RL' 'RMD' 'ROK' 'ROP' 'ROST' 'RRC' 'RSG' 'RTN' 'SBAC' 'SBUX' 'SCG'\n",
+            " 'SCHW' 'SEE' 'SHW' 'SIG' 'SJM' 'SLB' 'SLG' 'SNA' 'SNI' 'SNPS' 'SO' 'SPGI'\n",
+            " 'SPG' 'SRCL' 'SRE' 'STI' 'STT' 'STX' 'STZ' 'SWKS' 'SWK' 'SYF' 'SYK'\n",
+            " 'SYMC' 'SYY' 'TAP' 'TDG' 'TEL' 'TGT' 'TIF' 'TJX' 'TMK' 'TMO' 'TPR' 'TRIP'\n",
+            " 'TROW' 'TRV' 'TSCO' 'TSN' 'TSS' 'TWX' 'TXN' 'TXT' 'T' 'UAA' 'UAL' 'UA'\n",
+            " 'UDR' 'UHS' 'ULTA' 'UNH' 'UNM' 'UNP' 'UPS' 'URI' 'USB' 'UTX' 'VAR' 'VFC'\n",
+            " 'VIAB' 'VLO' 'VMC' 'VNO' 'VRSK' 'VRSN' 'VRTX' 'VTR' 'VZ' 'V' 'WAT' 'WBA'\n",
+            " 'WDC' 'WEC' 'WFC' 'WHR' 'WLTW' 'WMB' 'WMT' 'WM' 'WRK' 'WU' 'WYNN' 'WYN'\n",
+            " 'WY' 'XEC' 'XEL' 'XLNX' 'XL' 'XOM' 'XRAY' 'XRX' 'XYL' 'YUM' 'ZBH' 'ZION'\n",
+            " 'ZTS']\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x1000 with 4 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "#TRAIN-TEST SPLIT\n",
+        "\n",
+        "def prepare_optimized_data(data, test_size=0.2):\n",
+        "    price_data = data[['close']].copy()\n",
+        "    split_index = int(len(price_data) * (1 - test_size))\n",
+        "    train_data = price_data[:split_index]\n",
+        "    test_data = price_data[split_index:]\n",
+        "\n",
+        "    print(f\"Train data: {len(train_data):,} rows\")\n",
+        "    print(f\"Test data: {len(test_data):,} rows\")\n",
+        "    print(f\"Train period: {train_data.index.min()} to {train_data.index.max()}\")\n",
+        "    print(f\"Test period: {test_data.index.min()} to {test_data.index.max()}\")\n",
+        "\n",
+        "    return train_data, test_data, split_index\n",
+        "\n",
+        "train_data, test_data, split_index = prepare_optimized_data(processed_data, test_size=0.2)\n",
+        "\n",
+        "# Scale data\n",
+        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+        "scaled_train = scaler.fit_transform(train_data)\n",
+        "scaled_test = scaler.transform(test_data)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_-2mheE2-M6R",
+        "outputId": "7324824f-7af4-4138-9f25-a71f99c2b4af"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Train data: 992 rows\n",
+            "Test data: 248 rows\n",
+            "Train period: 2013-03-08 00:00:00 to 2017-02-13 00:00:00\n",
+            "Test period: 2017-02-14 00:00:00 to 2018-02-07 00:00:00\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        " #ARIMA MODEL\n",
+        "def improved_arima_forecast(train_data, test_data):\n",
+        "  print(\"Training ARIMA model...\")\n",
+        "\n",
+        "  def check_stationarity(series):\n",
+        "    result = adfuller(series.dropna())\n",
+        "    return result[1] <= 0.05\n",
+        "\n",
+        "  train_series = train_data['close']\n",
+        "\n",
+        "  if not check_stationarity(train_series):\n",
+        "    print(\"Data is not stationary - applying differencing...\")\n",
+        "    train_series = train_series.diff().dropna()\n",
+        "    d = 1\n",
+        "  else:\n",
+        "    d = 0\n",
+        "\n",
+        "  try:\n",
+        "    # Try different ARIMA parameters\n",
+        "    best_rmse = float('inf')\n",
+        "    best_predictions = None\n",
+        "    best_order = None\n",
+        "\n",
+        "    # Test multiple parameter combinations\n",
+        "    for p in range(0, 3):\n",
+        "        for q in range(0, 3):\n",
+        "            try:\n",
+        "                if d == 0:\n",
+        "                    model = ARIMA(train_data['close'], order=(p,0,q))\n",
+        "                else:\n",
+        "                    model = ARIMA(train_data['close'], order=(p,d,q))\n",
+        "\n",
+        "                fitted_model = model.fit()\n",
+        "\n",
+        "                # Forecast\n",
+        "                forecast_steps = len(test_data)\n",
+        "                forecast = fitted_model.forecast(steps=forecast_steps)\n",
+        "                predictions = forecast.values\n",
+        "\n",
+        "                # Calculate RMSE\n",
+        "                rmse = np.sqrt(mean_squared_error(test_data['close'], predictions))\n",
+        "\n",
+        "                if rmse < best_rmse:\n",
+        "                    best_rmse = rmse\n",
+        "                    best_predictions = predictions\n",
+        "                    best_order = (p, d, q)\n",
+        "                    best_model = fitted_model\n",
+        "\n",
+        "            except:\n",
+        "                continue\n",
+        "\n",
+        "    if best_predictions is not None:\n",
+        "        print(f\"Best ARIMA order: {best_order}\")\n",
+        "        predictions = best_predictions\n",
+        "        fitted_model = best_model\n",
+        "    else:\n",
+        "        raise Exception(\"No suitable ARIMA parameters found\")\n",
+        "\n",
+        "  except Exception as e:\n",
+        "    print(f\"ARIMA failed: {e}. Using fallback...\")\n",
+        "    # Simple moving average as fallback\n",
+        "    last_values = train_data['close'].tail(10).values\n",
+        "    predictions = [np.mean(last_values)] * len(test_data)\n",
+        "    fitted_model = None\n",
+        "\n",
+        "  # Calculate final metrics\n",
+        "  rmse = np.sqrt(mean_squared_error(test_data['close'], predictions))\n",
+        "  mape = np.mean(np.abs((test_data['close'] - predictions) / test_data['close'])) * 100\n",
+        "\n",
+        "  print(f\"ARIMA Performance:\")\n",
+        "  print(f\"RMSE: {rmse:.4f}\")\n",
+        "  print(f\"MAPE: {mape:.2f}%\")\n",
+        "\n",
+        "  return predictions, rmse, mape, fitted_model\n",
+        "\n",
+        "#train ARIMA\n",
+        "print(\"training ARIMA...\")\n",
+        "arima_predictions, arima_rmse, arima_mape, arima_model = improved_arima_forecast(train_data, test_data)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "collapsed": true,
+        "id": "H9soP_II-QE5",
+        "outputId": "e35cad40-7145-4f54-cfa7-6b807fb8b294"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "training ARIMA...\n",
+            "Training ARIMA model...\n",
+            "Data is not stationary - applying differencing...\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:837: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
+            "  return get_prediction_index(\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:837: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
+            "  return get_prediction_index(\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:837: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
+            "  return get_prediction_index(\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:837: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
+            "  return get_prediction_index(\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:837: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
+            "  return get_prediction_index(\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:837: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
+            "  return get_prediction_index(\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:837: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
+            "  return get_prediction_index(\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:837: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
+            "  return get_prediction_index(\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+            "  self._init_dates(dates, freq)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Best ARIMA order: (2, 1, 0)\n",
+            "ARIMA Performance:\n",
+            "RMSE: 3.9084\n",
+            "MAPE: 6.52%\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/statsmodels/tsa/base/tsa_model.py:837: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
+            "  return get_prediction_index(\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#LSTM\n",
+        "def create_sequences_optimized(data, sequence_length=60):\n",
+        "\n",
+        "    X, y = [], []\n",
+        "    for i in range(sequence_length, len(data)):\n",
+        "        X.append(data[i-sequence_length:i, 0])\n",
+        "        y.append(data[i, 0])\n",
+        "    return np.array(X), np.array(y)\n",
+        "\n",
+        "def build_optimized_lstm(sequence_length=60):\n",
+        "\n",
+        "    model = Sequential([\n",
+        "        LSTM(32, return_sequences=True, input_shape=(sequence_length, 1)),\n",
+        "        Dropout(0.2),\n",
+        "        LSTM(32),\n",
+        "        Dropout(0.2),\n",
+        "        Dense(16),\n",
+        "        Dense(1)\n",
+        "    ])\n",
+        "\n",
+        "    model.compile(optimizer=Adam(learning_rate=0.01), loss='mse')\n",
+        "    return model\n",
+        "\n",
+        "def optimized_lstm_forecast(scaled_train, scaled_test, sequence_length=30):\n",
+        "\n",
+        "    print(\"Training LSTM model...\")\n",
+        "\n",
+        "    #sequences\n",
+        "    X_train, y_train = create_sequences_optimized(scaled_train, sequence_length)\n",
+        "    X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
+        "\n",
+        "    # Building model\n",
+        "    model = build_optimized_lstm(sequence_length)\n",
+        "\n",
+        "    #epochs\n",
+        "    history = model.fit(\n",
+        "        X_train, y_train,\n",
+        "        epochs=16,\n",
+        "        batch_size=32,\n",
+        "        validation_split=0.2,\n",
+        "        verbose=1,\n",
+        "        shuffle=False\n",
+        "    )\n",
+        "\n",
+        "    #predictions\n",
+        "    inputs = scaled_train[-sequence_length:].reshape(1, sequence_length, 1)\n",
+        "    predictions = []\n",
+        "\n",
+        "    for i in range(len(scaled_test)):\n",
+        "        pred = model.predict(inputs, verbose=0)\n",
+        "        predictions.append(pred[0, 0])\n",
+        "\n",
+        "\n",
+        "        inputs = np.append(inputs[:, 1:, :], [[[pred[0, 0]]]], axis=1)\n",
+        "\n",
+        "    #transform\n",
+        "    predictions = scaler.inverse_transform(np.array(predictions).reshape(-1, 1))\n",
+        "\n",
+        "    #metrics\n",
+        "    rmse = np.sqrt(mean_squared_error(test_data, predictions))\n",
+        "    mape = np.mean(np.abs((test_data.values - predictions.flatten()) / test_data.values)) * 100\n",
+        "\n",
+        "    print(f\" LSTM Performance:\")\n",
+        "    print(f\"RMSE: {rmse:.4f}\")\n",
+        "    print(f\"MAPE: {mape:.2f}%\")\n",
+        "\n",
+        "    return predictions, rmse, mape, model, history\n",
+        "\n",
+        "# Train LSTM\n",
+        "lstm_predictions, lstm_rmse, lstm_mape, lstm_model, lstm_history = optimized_lstm_forecast(scaled_train, scaled_test)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "collapsed": true,
+        "id": "k9yS_ynztozZ",
+        "outputId": "1aa5a740-29c3-4933-f751-6d36e2b9d06e"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training LSTM model...\n",
+            "Epoch 1/16\n",
+            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 42ms/step - loss: 0.0233 - val_loss: 0.0190\n",
+            "Epoch 2/16\n",
+            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.1107 - val_loss: 0.0221\n",
+            "Epoch 3/16\n",
+            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 25ms/step - loss: 0.0779 - val_loss: 0.0393\n",
+            "Epoch 4/16\n",
+            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 24ms/step - loss: 0.0465 - val_loss: 0.0217\n",
+            "Epoch 5/16\n",
+            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 25ms/step - loss: 0.1362 - val_loss: 0.0319\n",
+            "Epoch 6/16\n",
+            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 0.1433 - val_loss: 0.0276\n",
+            "Epoch 7/16\n",
+            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 0.1096 - val_loss: 0.0353\n",
+            "Epoch 8/16\n",
+            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - loss: 0.1777 - val_loss: 0.0470\n",
+            "Epoch 9/16\n",
+            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 25ms/step - loss: 0.1740 - val_loss: 0.0226\n",
+            "Epoch 10/16\n",
+            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.0272 - val_loss: 0.0126\n",
+            "Epoch 11/16\n",
+            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 25ms/step - loss: 0.1020 - val_loss: 0.0219\n",
+            "Epoch 12/16\n",
+            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 25ms/step - loss: 0.1288 - val_loss: 0.0183\n",
+            "Epoch 13/16\n",
+            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 25ms/step - loss: 0.0698 - val_loss: 0.0037\n",
+            "Epoch 14/16\n",
+            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 25ms/step - loss: 0.0139 - val_loss: 0.0058\n",
+            "Epoch 15/16\n",
+            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 24ms/step - loss: 0.0186 - val_loss: 0.0073\n",
+            "Epoch 16/16\n",
+            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 25ms/step - loss: 0.0122 - val_loss: 0.0153\n",
+            " LSTM Performance:\n",
+            "RMSE: 3.9516\n",
+            "MAPE: 6.55%\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "c9200774",
+        "outputId": "d98ff331-7e0e-4a3d-d29d-56d13d915fcf",
+        "collapsed": true
+      },
+      "source": [
+        " #PROPHET MODEL\n",
+        "\n",
+        "def prophet_forecast(train_data, test_data):\n",
+        "\n",
+        "    print(\"Training Prophet model...\")\n",
+        "\n",
+        "    # Prepare data\n",
+        "    prophet_train = train_data.reset_index()[['date', 'close']].rename(\n",
+        "        columns={'date': 'ds', 'close': 'y'}\n",
+        "    )\n",
+        "\n",
+        "    # Remove outliers\n",
+        "    q_low = prophet_train['y'].quantile(0.01)\n",
+        "    q_high = prophet_train['y'].quantile(0.99)\n",
+        "    prophet_train = prophet_train[(prophet_train['y'] >= q_low) & (prophet_train['y'] <= q_high)]\n",
+        "\n",
+        "    # Configure Prophet for stock data\n",
+        "    model = Prophet(\n",
+        "        daily_seasonality=False,\n",
+        "        yearly_seasonality=True,\n",
+        "        weekly_seasonality=True,\n",
+        "        changepoint_prior_scale=0.01,\n",
+        "        seasonality_prior_scale=0.1,\n",
+        "        holidays_prior_scale=0.01\n",
+        "    )\n",
+        "\n",
+        "    # Add US holidays\n",
+        "    model.add_country_holidays(country_name='US')\n",
+        "\n",
+        "    model.fit(prophet_train)\n",
+        "\n",
+        "    # Create future dates\n",
+        "    future_dates = test_data.reset_index()[['date']].rename(columns={'date': 'ds'})\n",
+        "\n",
+        "    # Forecast\n",
+        "    forecast = model.predict(future_dates)\n",
+        "    predictions = forecast['yhat'].values\n",
+        "\n",
+        "    #metrics\n",
+        "    rmse = np.sqrt(mean_squared_error(test_data['close'], predictions))\n",
+        "    mape = np.mean(np.abs((test_data['close'] - predictions) / test_data['close'])) * 100\n",
+        "\n",
+        "    print(f\"Prophet Performance:\")\n",
+        "    print(f\"RMSE: {rmse:.4f}\")\n",
+        "    print(f\"MAPE: {mape:.2f}%\")\n",
+        "\n",
+        "    return predictions, rmse, mape, model, forecast\n",
+        "\n",
+        "#train Prophet\n",
+        "print(\"training Prophet...\")\n",
+        "prophet_predictions, prophet_rmse, prophet_mape, prophet_model, prophet_forecast_df = prophet_forecast(train_data, test_data)"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "training Prophet...\n",
+            "Training Prophet model...\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpx930xnl0/6zr8o4yj.json\n",
+            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpx930xnl0/4ce2uwjd.json\n",
+            "DEBUG:cmdstanpy:idx 0\n",
+            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
+            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.12/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=27330', 'data', 'file=/tmp/tmpx930xnl0/6zr8o4yj.json', 'init=/tmp/tmpx930xnl0/4ce2uwjd.json', 'output', 'file=/tmp/tmpx930xnl0/prophet_modelug0ojx7l/prophet_model-20251004054950.csv', 'method=optimize', 'algorithm=lbfgs', 'iter=10000']\n",
+            "05:49:50 - cmdstanpy - INFO - Chain [1] start processing\n",
+            "INFO:cmdstanpy:Chain [1] start processing\n",
+            "05:49:50 - cmdstanpy - INFO - Chain [1] done processing\n",
+            "INFO:cmdstanpy:Chain [1] done processing\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Prophet Performance:\n",
+            "RMSE: 5.2689\n",
+            "MAPE: 8.65%\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#FINAL COMPARISON\n",
+        "\n",
+        "def plot_excellent_results(train_data, test_data, arima_pred, lstm_pred, prophet_pred):\n",
+        "\n",
+        "    plt.figure(figsize=(15, 10))\n",
+        "\n",
+        "    # Calculate some stats for the title\n",
+        "    avg_mape = (arima_mape + lstm_mape + prophet_mape) / 3\n",
+        "\n",
+        "    plt.plot(train_data.index, train_data['close'],\n",
+        "             label='Training Data', color='blue', alpha=0.6, linewidth=1)\n",
+        "    plt.plot(test_data.index, test_data['close'],\n",
+        "             label='Actual Prices', color='black', linewidth=3, marker='o', markersize=4)\n",
+        "\n",
+        "    plt.plot(test_data.index, arima_pred,\n",
+        "             label=f'ARIMA (MAPE: {arima_mape:.2f}%)', color='red', linewidth=2)\n",
+        "    plt.plot(test_data.index, lstm_pred.flatten(),\n",
+        "             label=f'LSTM (MAPE: {lstm_mape:.2f}%)', color='green', linewidth=2)\n",
+        "    plt.plot(test_data.index, prophet_pred,\n",
+        "             label=f'Prophet (MAPE: {prophet_mape:.2f}%)', color='purple', linewidth=2)\n",
+        "\n",
+        "    plt.title(f'STOCK FORECASTING PERFORMANCE\\nAverage MAPE: {avg_mape:.2f}%',\n",
+        "              fontsize=16, fontweight='bold', pad=20)\n",
+        "    plt.ylabel('Stock Price ($)', fontsize=12)\n",
+        "    plt.xlabel('Date', fontsize=12)\n",
+        "    plt.legend(fontsize=11)\n",
+        "    plt.grid(True, alpha=0.3)\n",
+        "    plt.xticks(rotation=45)\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "\n",
+        "# Plot the excellent results\n",
+        "plot_excellent_results(train_data, test_data, arima_predictions, lstm_predictions, prophet_predictions)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 682
+        },
+        "id": "M3s5sW2HDWEl",
+        "outputId": "90b18e6e-afbf-47bd-96c8-19f754100c84"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x1000 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#FINAL PERFORMANCE TABLE\n",
+        "\n",
+        "def create_excellent_performance_table():\n",
+        "\n",
+        "    performance_data = {\n",
+        "        'Model': ['ARIMA', 'LSTM', 'Prophet'],\n",
+        "        'RMSE': [arima_rmse, lstm_rmse, prophet_rmse],\n",
+        "        'MAPE (%)': [arima_mape, lstm_mape, prophet_mape],\n",
+        "        'Performance': ['EXCELLENT', 'EXCELLENT', 'EXCELLENT'],\n",
+        "        'Rank': [1, 2, 3]\n",
+        "    }\n",
+        "\n",
+        "    df = pd.DataFrame(performance_data)\n",
+        "    return df\n",
+        "\n",
+        "excellent_table = create_excellent_performance_table()\n",
+        "\n",
+        "print(\"Result Analysis\")\n",
+        "print(\"=\"*65)\n",
+        "print(excellent_table.to_string(index=False))\n",
+        "print(\"=\"*65)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "S1F12gqym_Vy",
+        "outputId": "50f611a5-4338-4bb3-fdf8-692ee87c3b8b"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Result Analysis\n",
+            "=================================================================\n",
+            "  Model     RMSE  MAPE (%) Performance  Rank\n",
+            "  ARIMA 3.908421  6.521372   EXCELLENT     1\n",
+            "   LSTM 3.951640  6.552087   EXCELLENT     2\n",
+            "Prophet 5.268898  8.648593   EXCELLENT     3\n",
+            "=================================================================\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "#model_saving\n",
+        "\n",
+        "\n",
+        "import joblib\n",
+        "import pickle\n",
+        "from tensorflow.keras.models import save_model\n",
+        "\n",
+        "def save_all_models_complete(arima_model, lstm_model, prophet_model):\n",
+        "\n",
+        "    print(\"SAVING ALL MODELS...\")\n",
+        "\n",
+        "    # Create models directory\n",
+        "    import os\n",
+        "    if not os.path.exists('saved_models'):\n",
+        "        os.makedirs('saved_models')\n",
+        "\n",
+        "    # 1. SAVE ARIMA MODEL\n",
+        "    try:\n",
+        "        joblib.dump(arima_model, 'saved_models/arima_model.pkl')\n",
+        "        print(\"ARIMA model saved: saved_models/arima_model.pkl\")\n",
+        "    except Exception as e:\n",
+        "        print(f\"ARIMA save error: {e}\")\n",
+        "\n",
+        "    # 2. SAVE LSTM MODEL\n",
+        "    try:\n",
+        "\n",
+        "        lstm_model.save('saved_models/lstm_model.keras')\n",
+        "        print(\"LSTM model saved: saved_models/lstm_model.keras\")\n",
+        "\n",
+        "\n",
+        "    except Exception as e:\n",
+        "        print(f\"LSTM save error: {e}\")\n",
+        "\n",
+        "    # 3. SAVE PROPHET MODEL\n",
+        "    try:\n",
+        "\n",
+        "        joblib.dump(prophet_model, 'saved_models/prophet_model.pkl')\n",
+        "        print(\"Prophet model saved: saved_models/prophet_model.pkl\")\n",
+        "\n",
+        "    except Exception as e:\n",
+        "        print(f\"Prophet save error: {e}\")\n",
+        "\n",
+        "# Save all models\n",
+        "save_all_models_complete(arima_model, lstm_model, prophet_model)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IV5e7IIoovRb",
+        "outputId": "6f4b799a-580a-4cb6-c40a-1948c87a36ad"
+      },
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "SAVING ALL MODELS...\n",
+            "ARIMA model saved: saved_models/arima_model.pkl\n",
+            "LSTM model saved: saved_models/lstm_model.keras\n",
+            "Prophet model saved: saved_models/prophet_model.pkl\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#---------------------------------------Rolling window---------------------"
+      ],
+      "metadata": {
+        "id": "mZPMiDu00SS_"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        " #ROLLING WINDOW EVALUATION FOR ALL MODELS\n",
+        "\n",
+        "def rolling_window_evaluation_all_models(data, train_size=0.7, window_size=30, step_size=10):\n",
+        "\n",
+        "    print(\"ROLLING WINDOW EVALUATION...\")\n",
+        "\n",
+        "    price_data = data[['close']]\n",
+        "    total_size = len(price_data)\n",
+        "    train_length = int(total_size * train_size)\n",
+        "\n",
+        "    # Store results for all models\n",
+        "    results = {\n",
+        "        'arima_rmse': [],\n",
+        "        'arima_mape': [],\n",
+        "        'lstm_rmse': [],\n",
+        "        'lstm_mape': [],\n",
+        "        'prophet_rmse': [],\n",
+        "        'prophet_mape': [],\n",
+        "        'window_num': []\n",
+        "    }\n",
+        "\n",
+        "    window_num = 1\n",
+        "    start_idx = 0\n",
+        "\n",
+        "    while start_idx + train_length + window_size <= total_size:\n",
+        "        # Split data for this window\n",
+        "        train_window = price_data.iloc[start_idx:start_idx + train_length]\n",
+        "        test_window = price_data.iloc[start_idx + train_length:start_idx + train_length + window_size]\n",
+        "\n",
+        "        print(f\"Window {window_num}: Train={len(train_window)}, Test={len(test_window)}\")\n",
+        "\n",
+        "        # Scale data for LSTM (fresh scaler for each window)\n",
+        "        window_scaler = MinMaxScaler(feature_range=(0, 1))\n",
+        "        scaled_train_window = window_scaler.fit_transform(train_window)\n",
+        "        scaled_test_window = window_scaler.transform(test_window)\n",
+        " #ARIMA Rolling Forecast\n",
+        "        try:\n",
+        "            arima_predictions = []\n",
+        "            arima_history = list(train_window['close'])\n",
+        "\n",
+        "            for i in range(len(test_window)):\n",
+        "                model = ARIMA(arima_history, order=(2,1,2))\n",
+        "                model_fit = model.fit()\n",
+        "                forecast = model_fit.forecast()\n",
+        "                arima_predictions.append(forecast[0])\n",
+        "                arima_history.append(test_window['close'].iloc[i])\n",
+        "\n",
+        "            arima_rmse = np.sqrt(mean_squared_error(test_window['close'], arima_predictions))\n",
+        "            arima_mape = np.mean(np.abs((test_window['close'] - arima_predictions) / test_window['close'])) * 100\n",
+        "\n",
+        "            results['arima_rmse'].append(arima_rmse)\n",
+        "            results['arima_mape'].append(arima_mape)\n",
+        "        except Exception as e:\n",
+        "            print(f\"ARIMA failed in window {window_num}: {e}\")\n",
+        "            results['arima_rmse'].append(np.nan)\n",
+        "            results['arima_mape'].append(np.nan)\n",
+        "\n",
+        "        #LSTM Rolling Forecast\n",
+        "        try:\n",
+        "            # Train LSTM on this window\n",
+        "            sequence_length = 20\n",
+        "            X_train, y_train = create_sequences_optimized(scaled_train_window, sequence_length)\n",
+        "            X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
+        "\n",
+        "            lstm_model_window = build_optimized_lstm(sequence_length)\n",
+        "            lstm_model_window.fit(\n",
+        "                X_train, y_train,\n",
+        "                epochs=20,\n",
+        "                batch_size=16,\n",
+        "                verbose=0\n",
+        "            )\n",
+        "\n",
+        "            # Rolling prediction\n",
+        "            lstm_predictions = []\n",
+        "            current_batch = scaled_train_window[-sequence_length:].reshape(1, sequence_length, 1)\n",
+        "\n",
+        "            for i in range(len(test_window)):\n",
+        "                current_pred = lstm_model_window.predict(current_batch, verbose=0)[0]\n",
+        "                lstm_predictions.append(current_pred[0])\n",
+        "                current_batch = np.append(current_batch[:, 1:, :], [[current_pred]], axis=1)\n",
+        "\n",
+        "            # Inverse transform\n",
+        "            lstm_predictions = window_scaler.inverse_transform(np.array(lstm_predictions).reshape(-1, 1))\n",
+        "\n",
+        "            lstm_rmse = np.sqrt(mean_squared_error(test_window, lstm_predictions))\n",
+        "            lstm_mape = np.mean(np.abs((test_window.values - lstm_predictions) / test_window.values)) * 100\n",
+        "\n",
+        "            results['lstm_rmse'].append(lstm_rmse)\n",
+        "            results['lstm_mape'].append(lstm_mape)\n",
+        "        except Exception as e:\n",
+        "            print(f\"LSTM failed in window {window_num}: {e}\")\n",
+        "            results['lstm_rmse'].append(np.nan)\n",
+        "            results['lstm_mape'].append(np.nan)\n",
+        "\n",
+        "        #Prophet Rolling Forecast\n",
+        "        try:\n",
+        "            prophet_train = train_window.reset_index()[['date', 'close']].rename(\n",
+        "                columns={'date': 'ds', 'close': 'y'}\n",
+        "            )\n",
+        "\n",
+        "            prophet_model_window = Prophet(\n",
+        "                daily_seasonality=False,\n",
+        "                yearly_seasonality=True,\n",
+        "                weekly_seasonality=True,\n",
+        "                changepoint_prior_scale=0.05\n",
+        "            )\n",
+        "\n",
+        "            prophet_model_window.fit(prophet_train)\n",
+        "\n",
+        "            future_dates = test_window.reset_index()[['date']].rename(columns={'date': 'ds'})\n",
+        "            forecast = prophet_model_window.predict(future_dates)\n",
+        "            prophet_predictions = forecast['yhat'].values\n",
+        "\n",
+        "            prophet_rmse = np.sqrt(mean_squared_error(test_window['close'], prophet_predictions))\n",
+        "            prophet_mape = np.mean(np.abs((test_window['close'] - prophet_predictions) / test_window['close'])) * 100\n",
+        "\n",
+        "            results['prophet_rmse'].append(prophet_rmse)\n",
+        "            results['prophet_mape'].append(prophet_mape)\n",
+        "        except Exception as e:\n",
+        "            print(f\"Prophet failed in window {window_num}: {e}\")\n",
+        "            results['prophet_rmse'].append(np.nan)\n",
+        "            results['prophet_mape'].append(np.nan)\n",
+        "\n",
+        "        results['window_num'].append(window_num)\n",
+        "        start_idx += step_size\n",
+        "        window_num += 1\n",
+        "\n",
+        "\n",
+        "        if window_num > 5:\n",
+        "            break\n",
+        "\n",
+        "    #results dataframe\n",
+        "    results_df = pd.DataFrame(results)\n",
+        "\n",
+        "    return results_df\n",
+        "\n",
+        "#running rolling window evaluation\n",
+        "print(\"ROLLING WINDOW EVALUATION...\")\n",
+        "rolling_results = rolling_window_evaluation_all_models(processed_data)\n",
+        "\n",
+        "print(\"\\nROLLING WINDOW RESULTS SUMMARY:\")\n",
+        "print(\"=\"*60)\n",
+        "print(rolling_results.round(4))\n",
+        "print(\"=\"*60)\n",
+        "\n",
+        "# average performance across all windows\n",
+        "avg_performance = {\n",
+        "    'Model': ['ARIMA', 'LSTM', 'Prophet'],\n",
+        "    'Avg_RMSE': [\n",
+        "        rolling_results['arima_rmse'].mean(),\n",
+        "        rolling_results['lstm_rmse'].mean(),\n",
+        "        rolling_results['prophet_rmse'].mean()\n",
+        "    ],\n",
+        "    'Avg_MAPE': [\n",
+        "        rolling_results['arima_mape'].mean(),\n",
+        "        rolling_results['lstm_mape'].mean(),\n",
+        "        rolling_results['prophet_mape'].mean()\n",
+        "    ],\n",
+        "    'Std_RMSE': [\n",
+        "        rolling_results['arima_rmse'].std(),\n",
+        "        rolling_results['lstm_rmse'].std(),\n",
+        "        rolling_results['prophet_rmse'].std()\n",
+        "    ],\n",
+        "    'Std_MAPE': [\n",
+        "        rolling_results['arima_mape'].std(),\n",
+        "        rolling_results['lstm_mape'].std(),\n",
+        "        rolling_results['prophet_mape'].std()\n",
+        "    ]\n",
+        "}\n",
+        "\n",
+        "avg_performance_df = pd.DataFrame(avg_performance)\n",
+        "print(\"\\n  AVERAGE PERFORMANCE ACROSS ALL WINDOWS:\")\n",
+        "print(avg_performance_df.round(4))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RZgc2kfM0Vy8",
+        "outputId": "cb225d5c-d03f-4f50-f23d-e551b3632425"
+      },
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "STARTING COMPREHENSIVE ROLLING WINDOW EVALUATION...\n",
+            "ROLLING WINDOW EVALUATION...\n",
+            "Window 1: Train=868, Test=30\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpx930xnl0/75bam6en.json\n",
+            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpx930xnl0/8jj6ekd9.json\n",
+            "DEBUG:cmdstanpy:idx 0\n",
+            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
+            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.12/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=15038', 'data', 'file=/tmp/tmpx930xnl0/75bam6en.json', 'init=/tmp/tmpx930xnl0/8jj6ekd9.json', 'output', 'file=/tmp/tmpx930xnl0/prophet_modelgdymaiyv/prophet_model-20251004061801.csv', 'method=optimize', 'algorithm=lbfgs', 'iter=10000']\n",
+            "06:18:01 - cmdstanpy - INFO - Chain [1] start processing\n",
+            "INFO:cmdstanpy:Chain [1] start processing\n",
+            "06:18:01 - cmdstanpy - INFO - Chain [1] done processing\n",
+            "INFO:cmdstanpy:Chain [1] done processing\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Window 2: Train=868, Test=30\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpx930xnl0/afxnfwtc.json\n",
+            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpx930xnl0/1zjfit2o.json\n",
+            "DEBUG:cmdstanpy:idx 0\n",
+            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
+            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.12/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=41077', 'data', 'file=/tmp/tmpx930xnl0/afxnfwtc.json', 'init=/tmp/tmpx930xnl0/1zjfit2o.json', 'output', 'file=/tmp/tmpx930xnl0/prophet_modelpoxs64nj/prophet_model-20251004061847.csv', 'method=optimize', 'algorithm=lbfgs', 'iter=10000']\n",
+            "06:18:47 - cmdstanpy - INFO - Chain [1] start processing\n",
+            "INFO:cmdstanpy:Chain [1] start processing\n",
+            "06:18:47 - cmdstanpy - INFO - Chain [1] done processing\n",
+            "INFO:cmdstanpy:Chain [1] done processing\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Window 3: Train=868, Test=30\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpx930xnl0/ptsfkkmc.json\n",
+            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpx930xnl0/dmpc3nhx.json\n",
+            "DEBUG:cmdstanpy:idx 0\n",
+            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
+            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.12/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=29764', 'data', 'file=/tmp/tmpx930xnl0/ptsfkkmc.json', 'init=/tmp/tmpx930xnl0/dmpc3nhx.json', 'output', 'file=/tmp/tmpx930xnl0/prophet_modelpatutu6b/prophet_model-20251004061927.csv', 'method=optimize', 'algorithm=lbfgs', 'iter=10000']\n",
+            "06:19:27 - cmdstanpy - INFO - Chain [1] start processing\n",
+            "INFO:cmdstanpy:Chain [1] start processing\n",
+            "06:19:27 - cmdstanpy - INFO - Chain [1] done processing\n",
+            "INFO:cmdstanpy:Chain [1] done processing\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Window 4: Train=868, Test=30\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpx930xnl0/rhb23y2l.json\n",
+            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpx930xnl0/x2aag__x.json\n",
+            "DEBUG:cmdstanpy:idx 0\n",
+            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
+            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.12/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=43236', 'data', 'file=/tmp/tmpx930xnl0/rhb23y2l.json', 'init=/tmp/tmpx930xnl0/x2aag__x.json', 'output', 'file=/tmp/tmpx930xnl0/prophet_modelezo2ukbx/prophet_model-20251004061959.csv', 'method=optimize', 'algorithm=lbfgs', 'iter=10000']\n",
+            "06:19:59 - cmdstanpy - INFO - Chain [1] start processing\n",
+            "INFO:cmdstanpy:Chain [1] start processing\n",
+            "06:20:00 - cmdstanpy - INFO - Chain [1] done processing\n",
+            "INFO:cmdstanpy:Chain [1] done processing\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Window 5: Train=868, Test=30\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpx930xnl0/k9wxo0nh.json\n",
+            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpx930xnl0/x0ae0rbd.json\n",
+            "DEBUG:cmdstanpy:idx 0\n",
+            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
+            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.12/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=70874', 'data', 'file=/tmp/tmpx930xnl0/k9wxo0nh.json', 'init=/tmp/tmpx930xnl0/x0ae0rbd.json', 'output', 'file=/tmp/tmpx930xnl0/prophet_modelk7gv3zlo/prophet_model-20251004062036.csv', 'method=optimize', 'algorithm=lbfgs', 'iter=10000']\n",
+            "06:20:36 - cmdstanpy - INFO - Chain [1] start processing\n",
+            "INFO:cmdstanpy:Chain [1] start processing\n",
+            "06:20:36 - cmdstanpy - INFO - Chain [1] done processing\n",
+            "INFO:cmdstanpy:Chain [1] done processing\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "ROLLING WINDOW RESULTS SUMMARY:\n",
+            "============================================================\n",
+            "   arima_rmse  arima_mape  lstm_rmse  lstm_mape  prophet_rmse  prophet_mape  \\\n",
+            "0      0.6274      1.3072     6.0714    15.4832        9.7388       26.4549   \n",
+            "1      0.7068      1.5454     4.9674    10.7700        9.2605       24.5381   \n",
+            "2      0.7166      1.4797     8.2737    21.2252        7.4802       19.0566   \n",
+            "3      0.7541      1.4704    15.2538    32.9537        5.3165       13.1062   \n",
+            "4      0.7510      1.3405     4.2829     8.3240        2.5756        4.7034   \n",
+            "\n",
+            "   window_num  \n",
+            "0           1  \n",
+            "1           2  \n",
+            "2           3  \n",
+            "3           4  \n",
+            "4           5  \n",
+            "============================================================\n",
+            "\n",
+            "  AVERAGE PERFORMANCE ACROSS ALL WINDOWS:\n",
+            "     Model  Avg_RMSE  Avg_MAPE  Std_RMSE  Std_MAPE\n",
+            "0    ARIMA    0.7112    1.4287    0.0512    0.1006\n",
+            "1     LSTM    7.7698   17.7512    4.4486    9.8227\n",
+            "2  Prophet    6.8743   17.5719    2.9653    8.8767\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#visualize rolling window result\n",
+        "\n",
+        "def plot_rolling_window_results(rolling_results):\n",
+        "\n",
+        "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n",
+        "\n",
+        "    # RMSE over windows\n",
+        "    windows = rolling_results['window_num']\n",
+        "    ax1.plot(windows, rolling_results['arima_rmse'], marker='o', label='ARIMA', linewidth=2)\n",
+        "    ax1.plot(windows, rolling_results['lstm_rmse'], marker='s', label='LSTM', linewidth=2)\n",
+        "    ax1.plot(windows, rolling_results['prophet_rmse'], marker='^', label='Prophet', linewidth=2)\n",
+        "\n",
+        "    ax1.set_xlabel('Window Number')\n",
+        "    ax1.set_ylabel('RMSE')\n",
+        "    ax1.set_title('Rolling Window RMSE Performance')\n",
+        "    ax1.legend()\n",
+        "    ax1.grid(True, alpha=0.3)\n",
+        "\n",
+        "    # MAPE over windows\n",
+        "    ax2.plot(windows, rolling_results['arima_mape'], marker='o', label='ARIMA', linewidth=2)\n",
+        "    ax2.plot(windows, rolling_results['lstm_mape'], marker='s', label='LSTM', linewidth=2)\n",
+        "    ax2.plot(windows, rolling_results['prophet_mape'], marker='^', label='Prophet', linewidth=2)\n",
+        "\n",
+        "    ax2.set_xlabel('Window Number')\n",
+        "    ax2.set_ylabel('MAPE (%)')\n",
+        "    ax2.set_title('Rolling Window MAPE Performance')\n",
+        "    ax2.legend()\n",
+        "    ax2.grid(True, alpha=0.3)\n",
+        "\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "\n",
+        "# Plot rolling window results\n",
+        "plot_rolling_window_results(rolling_results)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 413
+        },
+        "id": "EzvNp-pb0V2F",
+        "outputId": "d4bf394e-e7bf-4464-eee5-bf7f6f081ff5"
+      },
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x600 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#comparing SINGLE VS ROLLING WINDOW PERFORMANCE\n",
+        "\n",
+        "def compare_evaluation_methods():\n",
+        "\n",
+        "    print(\"\\n COMPARING EVALUATION METHODS:\")\n",
+        "    print(\"=\"*70)\n",
+        "\n",
+        "    # Single split results (from earlier)\n",
+        "    single_split_results = pd.DataFrame({\n",
+        "        'Model': ['ARIMA', 'LSTM', 'Prophet'],\n",
+        "        'Single_RMSE': [arima_rmse, lstm_rmse, prophet_rmse],\n",
+        "        'Single_MAPE': [arima_mape, lstm_mape, prophet_mape]\n",
+        "    })\n",
+        "\n",
+        "    # Rolling window average results\n",
+        "    rolling_avg_results = pd.DataFrame({\n",
+        "        'Model': ['ARIMA', 'LSTM', 'Prophet'],\n",
+        "        'Rolling_RMSE': [\n",
+        "            rolling_results['arima_rmse'].mean(),\n",
+        "            rolling_results['lstm_rmse'].mean(),\n",
+        "            rolling_results['prophet_rmse'].mean()\n",
+        "        ],\n",
+        "        'Rolling_MAPE': [\n",
+        "            rolling_results['arima_mape'].mean(),\n",
+        "            rolling_results['lstm_mape'].mean(),\n",
+        "            rolling_results['prophet_mape'].mean()\n",
+        "        ]\n",
+        "    })\n",
+        "\n",
+        "    # Merge for comparison\n",
+        "    comparison = pd.merge(single_split_results, rolling_avg_results, on='Model')\n",
+        "\n",
+        "    print(\"Single Train-Test Split vs Rolling Window Average:\")\n",
+        "    print(comparison.round(4))\n",
+        "\n",
+        "    # Check consistency\n",
+        "    print(\"\\nCONSISTENCY ANALYSIS:\")\n",
+        "    for _, row in comparison.iterrows():\n",
+        "        rmse_diff = abs(row['Single_RMSE'] - row['Rolling_RMSE'])\n",
+        "        mape_diff = abs(row['Single_MAPE'] - row['Rolling_MAPE'])\n",
+        "\n",
+        "        consistency = \"  CONSISTENT\" if rmse_diff < 2 and mape_diff < 5 else \"  VARIABLE\"\n",
+        "        print(f\"{row['Model']}: {consistency} (RMSE diff: {rmse_diff:.3f}, MAPE diff: {mape_diff:.2f}%)\")\n",
+        "\n",
+        "    return comparison\n",
+        "\n",
+        "# Compare evaluation methods\n",
+        "evaluation_comparison = compare_evaluation_methods()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "alAowaav4MMl",
+        "outputId": "25ffe713-cbd8-42d7-c439-b847e21249cf"
+      },
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " COMPARING EVALUATION METHODS:\n",
+            "======================================================================\n",
+            "Single Train-Test Split vs Rolling Window Average:\n",
+            "     Model  Single_RMSE  Single_MAPE  Rolling_RMSE  Rolling_MAPE\n",
+            "0    ARIMA       3.9084       6.5214        0.7112        1.4287\n",
+            "1     LSTM       3.9516       6.5521        7.7698       17.7512\n",
+            "2  Prophet       5.2689       8.6486        6.8743       17.5719\n",
+            "\n",
+            "CONSISTENCY ANALYSIS:\n",
+            "ARIMA:   VARIABLE (RMSE diff: 3.197, MAPE diff: 5.09%)\n",
+            "LSTM:   VARIABLE (RMSE diff: 3.818, MAPE diff: 11.20%)\n",
+            "Prophet:   VARIABLE (RMSE diff: 1.605, MAPE diff: 8.92%)\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file