{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "PRONOSTICO RESULTADOS DE EBITDA"
      ],
      "metadata": {
        "id": "2r1OIJ_bv3R6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Primero, asegurémonos de que las librerías necesarias estén instaladas\n",
        "!pip install statsmodels pandas matplotlib\n",
        "\n",
        "# Importar librerías\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from statsmodels.tsa.holtwinters import ExponentialSmoothing\n",
        "\n",
        "# Datos de ejemplo\n",
        "data = {\n",
        "    'Fecha': ['2023-01-01', '2024-01-01', '2024-06-01', '2025-01-01', '2025-06-01', '2026-01-01', '2026-06-01'],\n",
        "    'EBITDA': [15102243.88, 48246446.82, 415328518.46, 557641400.56, 741763110.91, 894714432.72, 922319742.33]\n",
        "}\n",
        "\n",
        "\n",
        "# Crear DataFrame\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Convertir la columna 'Fecha' a tipo datetime y establecer como índice\n",
        "df['Fecha'] = pd.to_datetime(df['Fecha'])\n",
        "df.set_index('Fecha', inplace=True)\n",
        "\n",
        "# Aplicar el modelo Holt-Winters con estacionalidad semestral (2 períodos por año)\n",
        "model = ExponentialSmoothing(df['EBITDA'], trend='add', seasonal='add', seasonal_periods=2)\n",
        "model_fit = model.fit()\n",
        "\n",
        "# Realizar la predicción para los próximos 4 semestres (2 años)\n",
        "forecast = model_fit.forecast(steps=4)\n",
        "\n",
        "# Graficar los resultados\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(df.index, df['EBITDA'], label='EBITDA Real', marker='o')\n",
        "plt.plot(pd.date_range(df.index[-1], periods=5, freq='6M')[1:], forecast, label='Pronóstico EBITDA', marker='x', color='red')\n",
        "plt.title('Proyección de EBITDA')\n",
        "plt.xlabel('Fecha')\n",
        "plt.ylabel('EBITDA')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "\n",
        "# Imprimir los valores pronosticados\n",
        "print(f\"Pronóstico para los próximos 4 semestres: {forecast}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "XMMOKu4tTZ2M",
        "outputId": "3e901f32-7570-4afd-d99a-228372dbb4fd"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.10/dist-packages (0.14.4)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.2.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.8.0)\n",
            "Requirement already satisfied: numpy<3,>=1.22.3 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (1.26.4)\n",
            "Requirement already satisfied: scipy!=1.9.2,>=1.8 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (1.13.1)\n",
            "Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (1.0.1)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (24.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.55.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.7)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (11.0.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.2.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/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/python3.10/dist-packages/statsmodels/tsa/holtwinters/model.py:918: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/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/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:837: FutureWarning: No supported index is available. In the next version, calling this method in a model without a supported index will result in an exception.\n",
            "  return get_prediction_index(\n",
            "<ipython-input-2-ba7249805977>:33: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
            "  plt.plot(pd.date_range(df.index[-1], periods=5, freq='6M')[1:], forecast, label='Pronóstico EBITDA', marker='x', color='red')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pronóstico para los próximos 4 semestres: 7     1.140733e+09\n",
            "8     1.265047e+09\n",
            "9     1.455584e+09\n",
            "10    1.579899e+09\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "PRONOSTICO RESULTADOS DE EVA"
      ],
      "metadata": {
        "id": "TsCtn1Y_v_7n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Primero, asegurémonos de que las librerías necesarias estén instaladas\n",
        "!pip install statsmodels pandas matplotlib\n",
        "\n",
        "# Importar librerías\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from statsmodels.tsa.holtwinters import ExponentialSmoothing\n",
        "\n",
        "# Datos de ejemplo\n",
        "data = {\n",
        "    'Fecha': ['2023-01-01', '2024-01-01', '2024-06-01', '2025-01-01', '2025-06-01', '2026-01-01', '2026-06-01'],\n",
        "    'EVA': [-124628842.32, -122335740.07, 154586283.36, 247984667.35, 269312694.19, 364676529.26, 227232473.61]\n",
        "}\n",
        "\n",
        "# Crear DataFrame\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Convertir la columna 'Fecha' a tipo datetime y establecer como índice\n",
        "df['Fecha'] = pd.to_datetime(df['Fecha'])\n",
        "df.set_index('Fecha', inplace=True)\n",
        "\n",
        "# Aplicar el modelo Holt-Winters con estacionalidad semestral (2 períodos por año)\n",
        "model = ExponentialSmoothing(df['EVA'], trend='add', seasonal='add', seasonal_periods=2)\n",
        "model_fit = model.fit()\n",
        "\n",
        "# Realizar la predicción para los próximos 4 semestres (2 años)\n",
        "forecast = model_fit.forecast(steps=4)\n",
        "\n",
        "# Graficar los resultados\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(df.index, df['EVA'], label='EVA Real', marker='o')\n",
        "plt.plot(pd.date_range(df.index[-1], periods=5, freq='6M')[1:], forecast, label='Pronóstico EVA', marker='x', color='red')\n",
        "plt.title('Proyección de EVA')\n",
        "plt.xlabel('Fecha')\n",
        "plt.ylabel('EVA')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "\n",
        "# Imprimir los valores pronosticados\n",
        "print(f\"Pronóstico para los próximos 4 semestres: {forecast}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "CS0mFrBuwFj-",
        "outputId": "47e30c09-2f23-44e0-b0c2-c25471e77396"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.10/dist-packages (0.14.4)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.2.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.8.0)\n",
            "Requirement already satisfied: numpy<3,>=1.22.3 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (1.26.4)\n",
            "Requirement already satisfied: scipy!=1.9.2,>=1.8 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (1.13.1)\n",
            "Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (1.0.1)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (24.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.55.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.7)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (11.0.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.2.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/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/python3.10/dist-packages/statsmodels/tsa/holtwinters/model.py:918: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/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/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:837: FutureWarning: No supported index is available. In the next version, calling this method in a model without a supported index will result in an exception.\n",
            "  return get_prediction_index(\n",
            "<ipython-input-3-a2767a9aea5d>:32: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
            "  plt.plot(pd.date_range(df.index[-1], periods=5, freq='6M')[1:], forecast, label='Pronóstico EVA', marker='x', color='red')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pronóstico para los próximos 4 semestres: 7     3.745661e+08\n",
            "8     3.988609e+08\n",
            "9     4.277279e+08\n",
            "10    4.520227e+08\n",
            "dtype: float64\n"
          ]
        }
      ]
    }
  ]
}