Tesla_Stock_Price_prediction_using_Facebook_Prophet.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "#TESLA (TSLA) Stock Price prediction using Facebook Prophet"
      ],
      "metadata": {
        "id": "WNsVzEWCZM3Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aQM-5NwRc0ob",
        "outputId": "e4946a6b-49c2-4adc-8e80-30fd85277e10"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Importing necessary packages/libraries"
      ],
      "metadata": {
        "id": "lzDUUYcHZYw_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "##%%bash\n",
        "##python -m pip install prophet"
      ],
      "metadata": {
        "id": "v00lJyoSZmay"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "wim345nTW3kT"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import plotly.express as px\n",
        "from prophet import Prophet"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.io as pio\n",
        "pio.renderers.default='colab'"
      ],
      "metadata": {
        "id": "088VlePaaWRx"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "           "
      ],
      "metadata": {
        "id": "5jn8PzAUbpFB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Importing and Understanding the Data"
      ],
      "metadata": {
        "id": "xVIbuD-BbqyK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"/gdrive/My Drive/Colab_Notebooks/TSLA Stock Price Prediction/TSLA.csv\")"
      ],
      "metadata": {
        "id": "EBtNLNDwbjwZ"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(n = 20)"
      ],
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          "height": 669
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        "id": "408bAPf4cwJ9",
        "outputId": "169a0f83-5202-4ab5-b427-68abf991a214"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Date        Open        High         Low       Close   Adj Close  \\\n",
              "0   2022-08-24  297.563324  303.646667  296.500000  297.096680  297.096680   \n",
              "1   2022-08-25  302.359985  302.959991  291.600006  296.070007  296.070007   \n",
              "2   2022-08-26  297.429993  302.000000  287.470001  288.089996  288.089996   \n",
              "3   2022-08-29  282.829987  287.739990  280.700012  284.820007  284.820007   \n",
              "4   2022-08-30  287.869995  288.480011  272.649994  277.700012  277.700012   \n",
              "5   2022-08-31  280.619995  281.250000  271.809998  275.609985  275.609985   \n",
              "6   2022-09-01  272.579987  277.579987  266.149994  277.160004  277.160004   \n",
              "7   2022-09-02  281.070007  282.350006  269.079987  270.209991  270.209991   \n",
              "8   2022-09-06  272.679993  275.989990  265.739990  274.420013  274.420013   \n",
              "9   2022-09-07  273.100006  283.839996  272.269989  283.700012  283.700012   \n",
              "10  2022-09-08  281.299988  289.500000  279.760010  289.260010  289.260010   \n",
              "11  2022-09-09  291.670013  299.850006  291.250000  299.679993  299.679993   \n",
              "12  2022-09-12  300.720001  305.489990  300.399994  304.420013  304.420013   \n",
              "13  2022-09-13  292.899994  297.399994  290.399994  292.130005  292.130005   \n",
              "14  2022-09-14  292.239990  306.000000  291.640015  302.609985  302.609985   \n",
              "15  2022-09-15  301.829987  309.119995  300.720001  303.750000  303.750000   \n",
              "16  2022-09-16  299.609985  303.709991  295.600006  303.350006  303.350006   \n",
              "17  2022-09-19  300.089996  309.839996  297.799988  309.070007  309.070007   \n",
              "18  2022-09-20  306.910004  313.329987  305.579987  308.730011  308.730011   \n",
              "19  2022-09-21  308.290009  313.799988  300.630005  300.799988  300.799988   \n",
              "\n",
              "      Volume  \n",
              "0   57259800  \n",
              "1   53230000  \n",
              "2   57163900  \n",
              "3   41864700  \n",
              "4   50541800  \n",
              "5   52107300  \n",
              "6   54287000  \n",
              "7   50890100  \n",
              "8   55860000  \n",
              "9   50028900  \n",
              "10  53713100  \n",
              "11  54338100  \n",
              "12  48674600  \n",
              "13  68229600  \n",
              "14  72628700  \n",
              "15  64795500  \n",
              "16  87087800  \n",
              "17  60231200  \n",
              "18  61642800  \n",
              "19  62555700  "
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022-08-24</td>\n",
              "      <td>297.563324</td>\n",
              "      <td>303.646667</td>\n",
              "      <td>296.500000</td>\n",
              "      <td>297.096680</td>\n",
              "      <td>297.096680</td>\n",
              "      <td>57259800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-08-25</td>\n",
              "      <td>302.359985</td>\n",
              "      <td>302.959991</td>\n",
              "      <td>291.600006</td>\n",
              "      <td>296.070007</td>\n",
              "      <td>296.070007</td>\n",
              "      <td>53230000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-08-26</td>\n",
              "      <td>297.429993</td>\n",
              "      <td>302.000000</td>\n",
              "      <td>287.470001</td>\n",
              "      <td>288.089996</td>\n",
              "      <td>288.089996</td>\n",
              "      <td>57163900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-08-29</td>\n",
              "      <td>282.829987</td>\n",
              "      <td>287.739990</td>\n",
              "      <td>280.700012</td>\n",
              "      <td>284.820007</td>\n",
              "      <td>284.820007</td>\n",
              "      <td>41864700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-08-30</td>\n",
              "      <td>287.869995</td>\n",
              "      <td>288.480011</td>\n",
              "      <td>272.649994</td>\n",
              "      <td>277.700012</td>\n",
              "      <td>277.700012</td>\n",
              "      <td>50541800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2022-08-31</td>\n",
              "      <td>280.619995</td>\n",
              "      <td>281.250000</td>\n",
              "      <td>271.809998</td>\n",
              "      <td>275.609985</td>\n",
              "      <td>275.609985</td>\n",
              "      <td>52107300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2022-09-01</td>\n",
              "      <td>272.579987</td>\n",
              "      <td>277.579987</td>\n",
              "      <td>266.149994</td>\n",
              "      <td>277.160004</td>\n",
              "      <td>277.160004</td>\n",
              "      <td>54287000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2022-09-02</td>\n",
              "      <td>281.070007</td>\n",
              "      <td>282.350006</td>\n",
              "      <td>269.079987</td>\n",
              "      <td>270.209991</td>\n",
              "      <td>270.209991</td>\n",
              "      <td>50890100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2022-09-06</td>\n",
              "      <td>272.679993</td>\n",
              "      <td>275.989990</td>\n",
              "      <td>265.739990</td>\n",
              "      <td>274.420013</td>\n",
              "      <td>274.420013</td>\n",
              "      <td>55860000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2022-09-07</td>\n",
              "      <td>273.100006</td>\n",
              "      <td>283.839996</td>\n",
              "      <td>272.269989</td>\n",
              "      <td>283.700012</td>\n",
              "      <td>283.700012</td>\n",
              "      <td>50028900</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2022-09-08</td>\n",
              "      <td>281.299988</td>\n",
              "      <td>289.500000</td>\n",
              "      <td>279.760010</td>\n",
              "      <td>289.260010</td>\n",
              "      <td>289.260010</td>\n",
              "      <td>53713100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2022-09-09</td>\n",
              "      <td>291.670013</td>\n",
              "      <td>299.850006</td>\n",
              "      <td>291.250000</td>\n",
              "      <td>299.679993</td>\n",
              "      <td>299.679993</td>\n",
              "      <td>54338100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2022-09-12</td>\n",
              "      <td>300.720001</td>\n",
              "      <td>305.489990</td>\n",
              "      <td>300.399994</td>\n",
              "      <td>304.420013</td>\n",
              "      <td>304.420013</td>\n",
              "      <td>48674600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2022-09-13</td>\n",
              "      <td>292.899994</td>\n",
              "      <td>297.399994</td>\n",
              "      <td>290.399994</td>\n",
              "      <td>292.130005</td>\n",
              "      <td>292.130005</td>\n",
              "      <td>68229600</td>\n",
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              "      <th>14</th>\n",
              "      <td>2022-09-14</td>\n",
              "      <td>292.239990</td>\n",
              "      <td>306.000000</td>\n",
              "      <td>291.640015</td>\n",
              "      <td>302.609985</td>\n",
              "      <td>302.609985</td>\n",
              "      <td>72628700</td>\n",
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              "      <th>15</th>\n",
              "      <td>2022-09-15</td>\n",
              "      <td>301.829987</td>\n",
              "      <td>309.119995</td>\n",
              "      <td>300.720001</td>\n",
              "      <td>303.750000</td>\n",
              "      <td>303.750000</td>\n",
              "      <td>64795500</td>\n",
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              "      <th>16</th>\n",
              "      <td>2022-09-16</td>\n",
              "      <td>299.609985</td>\n",
              "      <td>303.709991</td>\n",
              "      <td>295.600006</td>\n",
              "      <td>303.350006</td>\n",
              "      <td>303.350006</td>\n",
              "      <td>87087800</td>\n",
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              "      <th>17</th>\n",
              "      <td>2022-09-19</td>\n",
              "      <td>300.089996</td>\n",
              "      <td>309.839996</td>\n",
              "      <td>297.799988</td>\n",
              "      <td>309.070007</td>\n",
              "      <td>309.070007</td>\n",
              "      <td>60231200</td>\n",
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              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2022-09-20</td>\n",
              "      <td>306.910004</td>\n",
              "      <td>313.329987</td>\n",
              "      <td>305.579987</td>\n",
              "      <td>308.730011</td>\n",
              "      <td>308.730011</td>\n",
              "      <td>61642800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2022-09-21</td>\n",
              "      <td>308.290009</td>\n",
              "      <td>313.799988</td>\n",
              "      <td>300.630005</td>\n",
              "      <td>300.799988</td>\n",
              "      <td>300.799988</td>\n",
              "      <td>62555700</td>\n",
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              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
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          "metadata": {},
          "execution_count": 18
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        "data.shape"
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      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(252, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
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      "cell_type": "code",
      "source": [
        "data.info()"
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        },
        "id": "BVzfLCgndpIX",
        "outputId": "9c8f0a08-413b-4491-b697-c319391a1683"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 252 entries, 0 to 251\n",
            "Data columns (total 7 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Date       252 non-null    object \n",
            " 1   Open       252 non-null    float64\n",
            " 2   High       252 non-null    float64\n",
            " 3   Low        252 non-null    float64\n",
            " 4   Close      252 non-null    float64\n",
            " 5   Adj Close  252 non-null    float64\n",
            " 6   Volume     252 non-null    int64  \n",
            "dtypes: float64(5), int64(1), object(1)\n",
            "memory usage: 13.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
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      "metadata": {
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          "height": 297
        },
        "id": "ULSnLolzdvY_",
        "outputId": "fbc2a6b5-412d-4a01-bd45-33207e63a25e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Open        High         Low       Close   Adj Close  \\\n",
              "count  252.000000  252.000000  252.000000  252.000000  252.000000   \n",
              "mean   209.393307  213.882288  204.466429  209.218201  209.218201   \n",
              "std     49.054679   49.423988   48.516284   48.874889   48.874889   \n",
              "min    103.000000  111.750000  101.809998  108.099998  108.099998   \n",
              "25%    176.902500  182.459999  172.507496  178.762501  178.762501   \n",
              "50%    198.919998  203.474998  193.599998  197.684998  197.684998   \n",
              "75%    250.582504  257.717499  244.694996  250.520004  250.520004   \n",
              "max    308.290009  313.799988  305.579987  309.070007  309.070007   \n",
              "\n",
              "             Volume  \n",
              "count  2.520000e+02  \n",
              "mean   1.266563e+08  \n",
              "std    4.788044e+07  \n",
              "min    4.186470e+07  \n",
              "25%    9.220765e+07  \n",
              "50%    1.215680e+08  \n",
              "75%    1.598709e+08  \n",
              "max    3.065906e+08  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>252.000000</td>\n",
              "      <td>252.000000</td>\n",
              "      <td>252.000000</td>\n",
              "      <td>252.000000</td>\n",
              "      <td>252.000000</td>\n",
              "      <td>2.520000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>209.393307</td>\n",
              "      <td>213.882288</td>\n",
              "      <td>204.466429</td>\n",
              "      <td>209.218201</td>\n",
              "      <td>209.218201</td>\n",
              "      <td>1.266563e+08</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>49.054679</td>\n",
              "      <td>49.423988</td>\n",
              "      <td>48.516284</td>\n",
              "      <td>48.874889</td>\n",
              "      <td>48.874889</td>\n",
              "      <td>4.788044e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>103.000000</td>\n",
              "      <td>111.750000</td>\n",
              "      <td>101.809998</td>\n",
              "      <td>108.099998</td>\n",
              "      <td>108.099998</td>\n",
              "      <td>4.186470e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>176.902500</td>\n",
              "      <td>182.459999</td>\n",
              "      <td>172.507496</td>\n",
              "      <td>178.762501</td>\n",
              "      <td>178.762501</td>\n",
              "      <td>9.220765e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>198.919998</td>\n",
              "      <td>203.474998</td>\n",
              "      <td>193.599998</td>\n",
              "      <td>197.684998</td>\n",
              "      <td>197.684998</td>\n",
              "      <td>1.215680e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>250.582504</td>\n",
              "      <td>257.717499</td>\n",
              "      <td>244.694996</td>\n",
              "      <td>250.520004</td>\n",
              "      <td>250.520004</td>\n",
              "      <td>1.598709e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>308.290009</td>\n",
              "      <td>313.799988</td>\n",
              "      <td>305.579987</td>\n",
              "      <td>309.070007</td>\n",
              "      <td>309.070007</td>\n",
              "      <td>3.065906e+08</td>\n",
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          },
          "metadata": {},
          "execution_count": 23
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      ]
    },
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      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "Man8iMDRe1ly"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "###Data Visulization - Visualizing the Historical Performance of Tesla"
      ],
      "metadata": {
        "id": "Ua6D2m6ge35b"
      }
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    {
      "cell_type": "code",
      "source": [
        "px.area(data, x = 'Date', y= 'Close')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "hrcGH6tBfAf8",
        "outputId": "1affa626-c4ac-4606-e133-88cc9436cce9"
      },
      "execution_count": 24,
      "outputs": [
        {
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        "data['co_diff'] = data['Close'] - data['Open']\n",
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      "execution_count": 33,
      "outputs": []
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    {
      "cell_type": "markdown",
      "source": [
        "###Data Preparation"
      ],
      "metadata": {
        "id": "H3SSJHAPhGR2"
      }
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    {
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        "data"
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          "height": 419
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        "id": "exNgnoUShIiz",
        "outputId": "a36129d0-426f-4f61-a571-5a6259ca508e"
      },
      "execution_count": 35,
      "outputs": [
        {
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              "           Date        Open        High         Low       Close   Adj Close  \\\n",
              "0    2022-08-24  297.563324  303.646667  296.500000  297.096680  297.096680   \n",
              "1    2022-08-25  302.359985  302.959991  291.600006  296.070007  296.070007   \n",
              "2    2022-08-26  297.429993  302.000000  287.470001  288.089996  288.089996   \n",
              "3    2022-08-29  282.829987  287.739990  280.700012  284.820007  284.820007   \n",
              "4    2022-08-30  287.869995  288.480011  272.649994  277.700012  277.700012   \n",
              "..          ...         ...         ...         ...         ...         ...   \n",
              "247  2023-08-18  214.119995  217.580002  212.360001  215.490005  215.490005   \n",
              "248  2023-08-21  221.550003  232.130005  220.580002  231.279999  231.279999   \n",
              "249  2023-08-22  240.250000  240.820007  229.550003  233.190002  233.190002   \n",
              "250  2023-08-23  229.339996  238.979996  229.289993  236.860001  236.860001   \n",
              "251  2023-08-24  238.660004  238.899994  228.180099  234.130005  234.130005   \n",
              "\n",
              "        Volume    co_diff  \n",
              "0     57259800  -0.466644  \n",
              "1     53230000  -6.289978  \n",
              "2     57163900  -9.339997  \n",
              "3     41864700   1.990020  \n",
              "4     50541800 -10.169983  \n",
              "..         ...        ...  \n",
              "247  135813700   1.370010  \n",
              "248  135702700   9.729996  \n",
              "249  130597900  -7.059998  \n",
              "250  100479700   7.520005  \n",
              "251   65432658  -4.529999  \n",
              "\n",
              "[252 rows x 8 columns]"
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              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>2022-08-24</td>\n",
              "      <td>297.563324</td>\n",
              "      <td>303.646667</td>\n",
              "      <td>296.500000</td>\n",
              "      <td>297.096680</td>\n",
              "      <td>297.096680</td>\n",
              "      <td>57259800</td>\n",
              "      <td>-0.466644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-08-25</td>\n",
              "      <td>302.359985</td>\n",
              "      <td>302.959991</td>\n",
              "      <td>291.600006</td>\n",
              "      <td>296.070007</td>\n",
              "      <td>296.070007</td>\n",
              "      <td>53230000</td>\n",
              "      <td>-6.289978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-08-26</td>\n",
              "      <td>297.429993</td>\n",
              "      <td>302.000000</td>\n",
              "      <td>287.470001</td>\n",
              "      <td>288.089996</td>\n",
              "      <td>288.089996</td>\n",
              "      <td>57163900</td>\n",
              "      <td>-9.339997</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-08-29</td>\n",
              "      <td>282.829987</td>\n",
              "      <td>287.739990</td>\n",
              "      <td>280.700012</td>\n",
              "      <td>284.820007</td>\n",
              "      <td>284.820007</td>\n",
              "      <td>41864700</td>\n",
              "      <td>1.990020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-08-30</td>\n",
              "      <td>287.869995</td>\n",
              "      <td>288.480011</td>\n",
              "      <td>272.649994</td>\n",
              "      <td>277.700012</td>\n",
              "      <td>277.700012</td>\n",
              "      <td>50541800</td>\n",
              "      <td>-10.169983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>247</th>\n",
              "      <td>2023-08-18</td>\n",
              "      <td>214.119995</td>\n",
              "      <td>217.580002</td>\n",
              "      <td>212.360001</td>\n",
              "      <td>215.490005</td>\n",
              "      <td>215.490005</td>\n",
              "      <td>135813700</td>\n",
              "      <td>1.370010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>2023-08-21</td>\n",
              "      <td>221.550003</td>\n",
              "      <td>232.130005</td>\n",
              "      <td>220.580002</td>\n",
              "      <td>231.279999</td>\n",
              "      <td>231.279999</td>\n",
              "      <td>135702700</td>\n",
              "      <td>9.729996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>2023-08-22</td>\n",
              "      <td>240.250000</td>\n",
              "      <td>240.820007</td>\n",
              "      <td>229.550003</td>\n",
              "      <td>233.190002</td>\n",
              "      <td>233.190002</td>\n",
              "      <td>130597900</td>\n",
              "      <td>-7.059998</td>\n",
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              "    <tr>\n",
              "      <th>250</th>\n",
              "      <td>2023-08-23</td>\n",
              "      <td>229.339996</td>\n",
              "      <td>238.979996</td>\n",
              "      <td>229.289993</td>\n",
              "      <td>236.860001</td>\n",
              "      <td>236.860001</td>\n",
              "      <td>100479700</td>\n",
              "      <td>7.520005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>251</th>\n",
              "      <td>2023-08-24</td>\n",
              "      <td>238.660004</td>\n",
              "      <td>238.899994</td>\n",
              "      <td>228.180099</td>\n",
              "      <td>234.130005</td>\n",
              "      <td>234.130005</td>\n",
              "      <td>65432658</td>\n",
              "      <td>-4.529999</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
          "execution_count": 35
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      "cell_type": "code",
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        "prophet_data = pd.DataFrame(data, columns = [\"Date\", \"Close\"])"
      ],
      "metadata": {
        "id": "jAunhinyhdxs"
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      "execution_count": 41,
      "outputs": []
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      "cell_type": "code",
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        "prophet_data = prophet_data.rename(columns =  {'Date':'ds', \"Close\":'y'})"
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        "id": "YaqwAuJuhkCk"
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      "execution_count": 42,
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        "prophet_data.head(n = 10)"
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          "height": 359
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        "id": "ExFSodouiJ5T",
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              "           ds           y\n",
              "0  2022-08-24  297.096680\n",
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              "2  2022-08-26  288.089996\n",
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              "5  2022-08-31  275.609985\n",
              "6  2022-09-01  277.160004\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bf326e1e-2050-443b-874a-f62c1dd4e462 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bf326e1e-2050-443b-874a-f62c1dd4e462');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4e854901-e311-4da2-a9f1-aff93b65861a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4e854901-e311-4da2-a9f1-aff93b65861a')\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
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              "    display: none;\n",
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              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4e854901-e311-4da2-a9f1-aff93b65861a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MMjqh4h0iLpb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Creation"
      ],
      "metadata": {
        "id": "xh3qftCliSP-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Prophet()"
      ],
      "metadata": {
        "id": "qLi0JPUOiT-d"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(prophet_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uc82oAWtie3p",
        "outputId": "04f3e71d-29f5-46f8-c3c2-c152172b0f32"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpy591d237/r9smmugp.json\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpy591d237/3q54ar4m.json\n",
            "DEBUG:cmdstanpy:idx 0\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=55688', 'data', 'file=/tmp/tmpy591d237/r9smmugp.json', 'init=/tmp/tmpy591d237/3q54ar4m.json', 'output', 'file=/tmp/tmpy591d237/prophet_modelcf_7poly/prophet_model-20230824171411.csv', 'method=optimize', 'algorithm=lbfgs', 'iter=10000']\n",
            "17:14:11 - cmdstanpy - INFO - Chain [1] start processing\n",
            "INFO:cmdstanpy:Chain [1] start processing\n",
            "17:14:11 - cmdstanpy - INFO - Chain [1] done processing\n",
            "INFO:cmdstanpy:Chain [1] done processing\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<prophet.forecaster.Prophet at 0x7ae8e6c46f80>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dWf1lBR3ihAu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Forecasting"
      ],
      "metadata": {
        "id": "DFvDIKXMip7s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "future = model.make_future_dataframe(periods = 200)"
      ],
      "metadata": {
        "id": "tfy9uEwUirmS"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "future"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "s4Z80ETIjT91",
        "outputId": "510f75e8-5b78-4a60-b71e-c8b19f91c390"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            ds\n",
              "0   2022-08-24\n",
              "1   2022-08-25\n",
              "2   2022-08-26\n",
              "3   2022-08-29\n",
              "4   2022-08-30\n",
              "..         ...\n",
              "612 2024-08-19\n",
              "613 2024-08-20\n",
              "614 2024-08-21\n",
              "615 2024-08-22\n",
              "616 2024-08-23\n",
              "\n",
              "[617 rows x 1 columns]"
            ],
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              "      <td>2022-08-29</td>\n",
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              "      <td>2022-08-30</td>\n",
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              "      <td>...</td>\n",
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              "      <th>612</th>\n",
              "      <td>2024-08-19</td>\n",
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              "      <td>2024-08-20</td>\n",
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              "      <td>2024-08-21</td>\n",
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              "      <td>2024-08-22</td>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2fe5efe6-c4c9-4026-8b36-0503d27e4ddd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2fe5efe6-c4c9-4026-8b36-0503d27e4ddd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </g>\n",
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              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
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              "    fill: #174EA6;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a1d16f64-e433-45ea-a87d-7f889aeaeba2 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "forecast = model.predict(future)"
      ],
      "metadata": {
        "id": "8ckXN1uyjWmS"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "forecast"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "m0rSgDNBjda4",
        "outputId": "ad58bbce-cc6a-435b-8f5c-e471df36af06"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            ds       trend  yhat_lower  yhat_upper  trend_lower  trend_upper  \\\n",
              "0   2022-08-24  298.610460  268.593366  329.620993   298.610460   298.610460   \n",
              "1   2022-08-25  297.367143  266.337348  331.339049   297.367143   297.367143   \n",
              "2   2022-08-26  296.123826  265.499104  326.924744   296.123826   296.123826   \n",
              "3   2022-08-29  292.393877  261.891925  324.061309   292.393877   292.393877   \n",
              "4   2022-08-30  291.150560  260.064948  321.552450   291.150560   291.150560   \n",
              "..         ...         ...         ...         ...          ...          ...   \n",
              "612 2024-08-19  492.920048  361.677606  635.682326   362.115783   637.558364   \n",
              "613 2024-08-20  493.542012  359.285348  641.875349   362.131910   638.414878   \n",
              "614 2024-08-21  494.163977  354.754457  644.203783   361.935926   639.739433   \n",
              "615 2024-08-22  494.785941  355.165280  646.968622   361.921873   641.127631   \n",
              "616 2024-08-23  495.407906  361.953008  649.732907   361.959324   642.515828   \n",
              "\n",
              "     additive_terms  additive_terms_lower  additive_terms_upper    weekly  \\\n",
              "0          0.327194              0.327194              0.327194  0.327194   \n",
              "1         -0.474218             -0.474218             -0.474218 -0.474218   \n",
              "2          0.635383              0.635383              0.635383  0.635383   \n",
              "3          1.934878              1.934878              1.934878  1.934878   \n",
              "4         -0.009734             -0.009734             -0.009734 -0.009734   \n",
              "..              ...                   ...                   ...       ...   \n",
              "612        1.934878              1.934878              1.934878  1.934878   \n",
              "613       -0.009734             -0.009734             -0.009734 -0.009734   \n",
              "614        0.327194              0.327194              0.327194  0.327194   \n",
              "615       -0.474218             -0.474218             -0.474218 -0.474218   \n",
              "616        0.635383              0.635383              0.635383  0.635383   \n",
              "\n",
              "     weekly_lower  weekly_upper  multiplicative_terms  \\\n",
              "0        0.327194      0.327194                   0.0   \n",
              "1       -0.474218     -0.474218                   0.0   \n",
              "2        0.635383      0.635383                   0.0   \n",
              "3        1.934878      1.934878                   0.0   \n",
              "4       -0.009734     -0.009734                   0.0   \n",
              "..            ...           ...                   ...   \n",
              "612      1.934878      1.934878                   0.0   \n",
              "613     -0.009734     -0.009734                   0.0   \n",
              "614      0.327194      0.327194                   0.0   \n",
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          "metadata": {},
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    {
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      "source": [
        "px.line(forecast, x = 'ds', y = 'yhat')"
      ],
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          "height": 542
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        "id": "IKLwgO2XjecM",
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              "                            \n",
              "var gd = document.getElementById('cfc8c402-4fd2-47af-a7a6-80b402131d71');\n",
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot = model.plot(forecast, xlabel = 'ds', ylabel = 'y')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "cgEjj0foj0gk",
        "outputId": "257e585d-d379-46c4-9272-aae8505138c6"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot2 = model.plot_components(forecast)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "4987uUOOkNw9",
        "outputId": "2f48baa7-e203-498b-d5a8-38b6515fb4f5"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "forecast.to_csv('/gdrive/My Drive/Colab_Notebooks/TSLA Stock Price Prediction/tesla_forecast.csv', index= False)"
      ],
      "metadata": {
        "id": "bDRgwlyEkbNZ"
      },
      "execution_count": 64,
      "outputs": []
    }
  ]
}