Cybersecurity_june2024.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNko5lA90so+lHbiQ9Vgbx+",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vishymails/Cybersecurity-june24/blob/main/Cybersecurity_june2024.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "sNq2YXj_v5Ib"
      },
      "outputs": [],
      "source": [
        "from random import random"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "time_series = [2 * x + random() for x in range(1, 100)]\n"
      ],
      "metadata": {
        "id": "2Jx4lPgXzCyv"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(time_series)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "VoeSK21UzMuJ",
        "outputId": "eec26b01-a378-48e6-b1b6-db194dd0e918"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.arima.model import ARIMA\n",
        "\n",
        "model = ARIMA(time_series, order=(0, 1, 2))\n",
        "model_fit = model.fit()\n",
        "\n",
        "y = model_fit.predict(len(time_series), len(time_series))\n",
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pAq9gZDFzepK",
        "outputId": "6028a798-a672-4335-fb25-74ec4151c154"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[199.25776731]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.ar_model import AutoReg\n",
        "\n",
        "model = AutoReg(time_series, lags=2)\n",
        "model_fit = model.fit()\n",
        "\n",
        "y = model_fit.predict(len(time_series), len(time_series))\n",
        "print(y)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eM_fGKM80Dq9",
        "outputId": "d41730b9-fe68-42e3-84c6-fd122681cfbb"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[200.59113312]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_fit.summary\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "tWHOZ7sO220n",
        "outputId": "f0c6b792-6557-46e8-ee35-097f1dc06272"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method AutoRegResults.summary of <statsmodels.tsa.ar_model.AutoRegResults object at 0x7e4f93607640>>"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>statsmodels.tsa.ar_model.AutoRegResults.summary</b><br/>def summary(alpha=0.05)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/ar_model.py</a>Summarize the Model\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "alpha : float, optional\n",
              "    Significance level for the confidence intervals.\n",
              "\n",
              "Returns\n",
              "-------\n",
              "smry : Summary instance\n",
              "    This holds the summary table and text, which can be printed or\n",
              "    converted to various output formats.\n",
              "\n",
              "See Also\n",
              "--------\n",
              "statsmodels.iolib.summary.Summary</pre>\n",
              "      <script>\n",
              "      if (google.colab.kernel.accessAllowed && google.colab.files && google.colab.files.view) {\n",
              "        for (const element of document.querySelectorAll('.filepath')) {\n",
              "          element.style.display = 'block'\n",
              "          element.onclick = (event) => {\n",
              "            event.preventDefault();\n",
              "            event.stopPropagation();\n",
              "            google.colab.files.view(element.textContent, 1652);\n",
              "          };\n",
              "        }\n",
              "      }\n",
              "      for (const element of document.querySelectorAll('.function-repr-contents')) {\n",
              "        element.onclick = (event) => {\n",
              "          event.preventDefault();\n",
              "          event.stopPropagation();\n",
              "          element.classList.toggle('function-repr-contents-collapsed');\n",
              "        };\n",
              "      }\n",
              "      </script>\n",
              "      </div>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_fit.params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X6iNSVyB3C7-",
        "outputId": "02207047-714e-46ad-dd1c-b8398436f4b8"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2.67830625, 0.65050609, 0.34965503])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_fit.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "Ub5c1eiw3JJ8",
        "outputId": "f25fa1f6-5985-4933-9aa0-05073977cdfc"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            AutoReg Model Results                             \n",
              "==============================================================================\n",
              "Dep. Variable:                      y   No. Observations:                   99\n",
              "Model:                     AutoReg(2)   Log Likelihood                 -41.198\n",
              "Method:               Conditional MLE   S.D. of innovations              0.370\n",
              "Date:                Mon, 17 Jun 2024   AIC                             90.396\n",
              "Time:                        11:02:33   BIC                            100.695\n",
              "Sample:                             2   HQIC                            94.561\n",
              "                                   99                                         \n",
              "==============================================================================\n",
              "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "const          2.6783      0.205     13.071      0.000       2.277       3.080\n",
              "y.L1           0.6505      0.096      6.802      0.000       0.463       0.838\n",
              "y.L2           0.3497      0.096      3.655      0.000       0.162       0.537\n",
              "                                    Roots                                    \n",
              "=============================================================================\n",
              "                  Real          Imaginary           Modulus         Frequency\n",
              "-----------------------------------------------------------------------------\n",
              "AR.1            0.9999           +0.0000j            0.9999            0.0000\n",
              "AR.2           -2.8603           +0.0000j            2.8603            0.5000\n",
              "-----------------------------------------------------------------------------\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>AutoReg Model Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>         <td>y</td>        <th>  No. Observations:  </th>   <td>99</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>            <td>AutoReg(2)</td>    <th>  Log Likelihood     </th> <td>-41.198</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>         <td>Conditional MLE</td> <th>  S.D. of innovations</th>  <td>0.370</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>          <td>Mon, 17 Jun 2024</td> <th>  AIC                </th> <td>90.396</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>              <td>11:02:33</td>     <th>  BIC                </th> <td>100.695</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>                <td>2</td>        <th>  HQIC               </th> <td>94.561</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                      <td>99</td>        <th>                     </th>    <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th> <td>    2.6783</td> <td>    0.205</td> <td>   13.071</td> <td> 0.000</td> <td>    2.277</td> <td>    3.080</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>y.L1</th>  <td>    0.6505</td> <td>    0.096</td> <td>    6.802</td> <td> 0.000</td> <td>    0.463</td> <td>    0.838</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>y.L2</th>  <td>    0.3497</td> <td>    0.096</td> <td>    3.655</td> <td> 0.000</td> <td>    0.162</td> <td>    0.537</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<caption>Roots</caption>\n",
              "<tr>\n",
              "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.1</th> <td>           0.9999</td> <td>          +0.0000j</td> <td>           0.9999</td> <td>           0.0000</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.2</th> <td>          -2.8603</td> <td>          +0.0000j</td> <td>           2.8603</td> <td>           0.5000</td>\n",
              "</tr>\n",
              "</table>"
            ],
            "text/latex": "\\begin{center}\n\\begin{tabular}{lclc}\n\\toprule\n\\textbf{Dep. Variable:} &             y             & \\textbf{  No. Observations:  } &             99             \\\\\n\\textbf{Model:}         &         AutoReg(2)        & \\textbf{  Log Likelihood     } &          -41.198           \\\\\n\\textbf{Method:}        &      Conditional MLE      & \\textbf{  S.D. of innovations} &           0.370            \\\\\n\\textbf{Date:}          &      Mon, 17 Jun 2024     & \\textbf{  AIC                } &           90.396           \\\\\n\\textbf{Time:}          &          11:02:33         & \\textbf{  BIC                } &          100.695           \\\\\n\\textbf{Sample:}        &             2             & \\textbf{  HQIC               } &           94.561           \\\\\n\\textbf{}               &             99            & \\textbf{                     } &                            \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lcccccc}\n               & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n\\midrule\n\\textbf{const} &       2.6783  &        0.205     &    13.071  &         0.000        &        2.277    &        3.080     \\\\\n\\textbf{y.L1}  &       0.6505  &        0.096     &     6.802  &         0.000        &        0.463    &        0.838     \\\\\n\\textbf{y.L2}  &       0.3497  &        0.096     &     3.655  &         0.000        &        0.162    &        0.537     \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lcccc}\n              & \\textbf{            Real} & \\textbf{         Imaginary} & \\textbf{         Modulus} & \\textbf{        Frequency}  \\\\\n\\midrule\n\\textbf{AR.1} &                0.9999     &                +0.0000j     &                0.9999     &                0.0000       \\\\\n\\textbf{AR.2} &               -2.8603     &                +0.0000j     &                2.8603     &                0.5000       \\\\\n\\bottomrule\n\\end{tabular}\n%\\caption{AutoReg Model Results}\n\\end{center}"
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "b4Pitgbc3OMS"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}