diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb
index c1e1bf60c917ad4803b9405f3d8a08f0dc9c78b7..f5abfa9771d60dc690ddaffeed9edb68ee0320f5 100644
--- a/module3/exo3/exercice.ipynb
+++ b/module3/exo3/exercice.ipynb
@@ -1098,13 +1098,6 @@
     "data['fit_global'] = pd.Series(season_fitted_curve_global(time), index=data.index)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": 53,
@@ -1158,745 +1151,6 @@
     "data['global_fit_global'] = pd.Series(global_fit_season(time), index=data.index) - 316.19"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>CO2</th>\n",
-       "      <th>trend_fit</th>\n",
-       "      <th>untrend_data</th>\n",
-       "      <th>global_fit</th>\n",
-       "      <th>fit_global</th>\n",
-       "      <th>global_fit_global</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>date</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1958-03-29</th>\n",
-       "      <td>316.19</td>\n",
-       "      <td>314.57</td>\n",
-       "      <td>317.81</td>\n",
-       "      <td>315.44</td>\n",
-       "      <td>316.20</td>\n",
-       "      <td>314.58</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-04-05</th>\n",
-       "      <td>317.31</td>\n",
-       "      <td>314.59</td>\n",
-       "      <td>318.91</td>\n",
-       "      <td>315.83</td>\n",
-       "      <td>316.50</td>\n",
-       "      <td>314.90</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-04-12</th>\n",
-       "      <td>317.69</td>\n",
-       "      <td>314.60</td>\n",
-       "      <td>319.28</td>\n",
-       "      <td>316.21</td>\n",
-       "      <td>316.80</td>\n",
-       "      <td>315.21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-04-19</th>\n",
-       "      <td>317.58</td>\n",
-       "      <td>314.61</td>\n",
-       "      <td>319.16</td>\n",
-       "      <td>316.59</td>\n",
-       "      <td>317.09</td>\n",
-       "      <td>315.51</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-04-26</th>\n",
-       "      <td>316.48</td>\n",
-       "      <td>314.63</td>\n",
-       "      <td>318.04</td>\n",
-       "      <td>316.94</td>\n",
-       "      <td>317.36</td>\n",
-       "      <td>315.80</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-05-03</th>\n",
-       "      <td>316.95</td>\n",
-       "      <td>314.64</td>\n",
-       "      <td>318.50</td>\n",
-       "      <td>317.27</td>\n",
-       "      <td>317.62</td>\n",
-       "      <td>316.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-05-17</th>\n",
-       "      <td>317.56</td>\n",
-       "      <td>314.67</td>\n",
-       "      <td>319.08</td>\n",
-       "      <td>317.83</td>\n",
-       "      <td>318.07</td>\n",
-       "      <td>316.56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-05-24</th>\n",
-       "      <td>317.99</td>\n",
-       "      <td>314.69</td>\n",
-       "      <td>319.49</td>\n",
-       "      <td>318.06</td>\n",
-       "      <td>318.26</td>\n",
-       "      <td>316.76</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-07-05</th>\n",
-       "      <td>315.85</td>\n",
-       "      <td>314.78</td>\n",
-       "      <td>317.26</td>\n",
-       "      <td>318.48</td>\n",
-       "      <td>318.68</td>\n",
-       "      <td>317.27</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-07-12</th>\n",
-       "      <td>315.85</td>\n",
-       "      <td>314.79</td>\n",
-       "      <td>317.25</td>\n",
-       "      <td>318.38</td>\n",
-       "      <td>318.63</td>\n",
-       "      <td>317.23</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-07-19</th>\n",
-       "      <td>315.46</td>\n",
-       "      <td>314.81</td>\n",
-       "      <td>316.84</td>\n",
-       "      <td>318.24</td>\n",
-       "      <td>318.54</td>\n",
-       "      <td>317.15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-07-26</th>\n",
-       "      <td>315.59</td>\n",
-       "      <td>314.82</td>\n",
-       "      <td>316.96</td>\n",
-       "      <td>318.05</td>\n",
-       "      <td>318.41</td>\n",
-       "      <td>317.04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-08-02</th>\n",
-       "      <td>315.64</td>\n",
-       "      <td>314.84</td>\n",
-       "      <td>316.99</td>\n",
-       "      <td>317.83</td>\n",
-       "      <td>318.26</td>\n",
-       "      <td>316.90</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-08-09</th>\n",
-       "      <td>315.10</td>\n",
-       "      <td>314.85</td>\n",
-       "      <td>316.44</td>\n",
-       "      <td>317.57</td>\n",
-       "      <td>318.07</td>\n",
-       "      <td>316.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-08-16</th>\n",
-       "      <td>315.09</td>\n",
-       "      <td>314.87</td>\n",
-       "      <td>316.41</td>\n",
-       "      <td>317.28</td>\n",
-       "      <td>317.85</td>\n",
-       "      <td>316.53</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-08-30</th>\n",
-       "      <td>314.14</td>\n",
-       "      <td>314.90</td>\n",
-       "      <td>315.43</td>\n",
-       "      <td>316.62</td>\n",
-       "      <td>317.36</td>\n",
-       "      <td>316.06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-09-06</th>\n",
-       "      <td>313.54</td>\n",
-       "      <td>314.91</td>\n",
-       "      <td>314.82</td>\n",
-       "      <td>316.27</td>\n",
-       "      <td>317.08</td>\n",
-       "      <td>315.80</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-11-08</th>\n",
-       "      <td>313.05</td>\n",
-       "      <td>315.05</td>\n",
-       "      <td>314.19</td>\n",
-       "      <td>313.47</td>\n",
-       "      <td>314.53</td>\n",
-       "      <td>313.39</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-11-15</th>\n",
-       "      <td>313.26</td>\n",
-       "      <td>315.06</td>\n",
-       "      <td>314.39</td>\n",
-       "      <td>313.30</td>\n",
-       "      <td>314.32</td>\n",
-       "      <td>313.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-11-22</th>\n",
-       "      <td>313.57</td>\n",
-       "      <td>315.08</td>\n",
-       "      <td>314.68</td>\n",
-       "      <td>313.18</td>\n",
-       "      <td>314.13</td>\n",
-       "      <td>313.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-11-29</th>\n",
-       "      <td>314.01</td>\n",
-       "      <td>315.09</td>\n",
-       "      <td>315.11</td>\n",
-       "      <td>313.10</td>\n",
-       "      <td>313.98</td>\n",
-       "      <td>312.88</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-12-06</th>\n",
-       "      <td>314.56</td>\n",
-       "      <td>315.11</td>\n",
-       "      <td>315.64</td>\n",
-       "      <td>313.07</td>\n",
-       "      <td>313.86</td>\n",
-       "      <td>312.77</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-12-13</th>\n",
-       "      <td>314.41</td>\n",
-       "      <td>315.12</td>\n",
-       "      <td>315.48</td>\n",
-       "      <td>313.08</td>\n",
-       "      <td>313.77</td>\n",
-       "      <td>312.70</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-12-20</th>\n",
-       "      <td>314.77</td>\n",
-       "      <td>315.14</td>\n",
-       "      <td>315.82</td>\n",
-       "      <td>313.15</td>\n",
-       "      <td>313.71</td>\n",
-       "      <td>312.66</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-12-27</th>\n",
-       "      <td>315.21</td>\n",
-       "      <td>315.15</td>\n",
-       "      <td>316.25</td>\n",
-       "      <td>313.26</td>\n",
-       "      <td>313.70</td>\n",
-       "      <td>312.66</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-01-03</th>\n",
-       "      <td>315.24</td>\n",
-       "      <td>315.17</td>\n",
-       "      <td>316.26</td>\n",
-       "      <td>313.42</td>\n",
-       "      <td>313.72</td>\n",
-       "      <td>312.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-01-10</th>\n",
-       "      <td>315.50</td>\n",
-       "      <td>315.18</td>\n",
-       "      <td>316.51</td>\n",
-       "      <td>313.63</td>\n",
-       "      <td>313.77</td>\n",
-       "      <td>312.76</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-01-17</th>\n",
-       "      <td>315.69</td>\n",
-       "      <td>315.20</td>\n",
-       "      <td>316.68</td>\n",
-       "      <td>313.87</td>\n",
-       "      <td>313.86</td>\n",
-       "      <td>312.87</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-01-24</th>\n",
-       "      <td>315.86</td>\n",
-       "      <td>315.21</td>\n",
-       "      <td>316.84</td>\n",
-       "      <td>314.15</td>\n",
-       "      <td>313.99</td>\n",
-       "      <td>313.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-01-31</th>\n",
-       "      <td>315.42</td>\n",
-       "      <td>315.23</td>\n",
-       "      <td>316.38</td>\n",
-       "      <td>314.46</td>\n",
-       "      <td>314.15</td>\n",
-       "      <td>313.19</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-07-06</th>\n",
-       "      <td>412.69</td>\n",
-       "      <td>410.33</td>\n",
-       "      <td>318.55</td>\n",
-       "      <td>408.31</td>\n",
-       "      <td>315.48</td>\n",
-       "      <td>409.63</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-07-13</th>\n",
-       "      <td>412.30</td>\n",
-       "      <td>410.38</td>\n",
-       "      <td>318.11</td>\n",
-       "      <td>408.42</td>\n",
-       "      <td>315.20</td>\n",
-       "      <td>409.39</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-07-20</th>\n",
-       "      <td>411.76</td>\n",
-       "      <td>410.42</td>\n",
-       "      <td>317.53</td>\n",
-       "      <td>408.58</td>\n",
-       "      <td>314.93</td>\n",
-       "      <td>409.16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-07-27</th>\n",
-       "      <td>410.32</td>\n",
-       "      <td>410.47</td>\n",
-       "      <td>316.04</td>\n",
-       "      <td>408.78</td>\n",
-       "      <td>314.68</td>\n",
-       "      <td>408.96</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-08-03</th>\n",
-       "      <td>410.50</td>\n",
-       "      <td>410.52</td>\n",
-       "      <td>316.17</td>\n",
-       "      <td>409.03</td>\n",
-       "      <td>314.45</td>\n",
-       "      <td>408.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-08-10</th>\n",
-       "      <td>410.48</td>\n",
-       "      <td>410.56</td>\n",
-       "      <td>316.11</td>\n",
-       "      <td>409.32</td>\n",
-       "      <td>314.25</td>\n",
-       "      <td>408.62</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-08-17</th>\n",
-       "      <td>410.05</td>\n",
-       "      <td>410.61</td>\n",
-       "      <td>315.63</td>\n",
-       "      <td>409.64</td>\n",
-       "      <td>314.07</td>\n",
-       "      <td>408.49</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-08-24</th>\n",
-       "      <td>409.52</td>\n",
-       "      <td>410.65</td>\n",
-       "      <td>315.06</td>\n",
-       "      <td>409.99</td>\n",
-       "      <td>313.93</td>\n",
-       "      <td>408.39</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-08-31</th>\n",
-       "      <td>409.32</td>\n",
-       "      <td>410.70</td>\n",
-       "      <td>314.81</td>\n",
-       "      <td>410.37</td>\n",
-       "      <td>313.82</td>\n",
-       "      <td>408.32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-09-07</th>\n",
-       "      <td>408.80</td>\n",
-       "      <td>410.74</td>\n",
-       "      <td>314.25</td>\n",
-       "      <td>410.76</td>\n",
-       "      <td>313.74</td>\n",
-       "      <td>408.29</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-09-14</th>\n",
-       "      <td>408.61</td>\n",
-       "      <td>410.79</td>\n",
-       "      <td>314.01</td>\n",
-       "      <td>411.18</td>\n",
-       "      <td>313.70</td>\n",
-       "      <td>408.30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-09-21</th>\n",
-       "      <td>408.50</td>\n",
-       "      <td>410.83</td>\n",
-       "      <td>313.86</td>\n",
-       "      <td>411.60</td>\n",
-       "      <td>313.70</td>\n",
-       "      <td>408.34</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-09-28</th>\n",
-       "      <td>408.28</td>\n",
-       "      <td>410.88</td>\n",
-       "      <td>313.59</td>\n",
-       "      <td>412.02</td>\n",
-       "      <td>313.73</td>\n",
-       "      <td>408.42</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-10-05</th>\n",
-       "      <td>407.99</td>\n",
-       "      <td>410.92</td>\n",
-       "      <td>313.26</td>\n",
-       "      <td>412.43</td>\n",
-       "      <td>313.80</td>\n",
-       "      <td>408.54</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-10-12</th>\n",
-       "      <td>408.61</td>\n",
-       "      <td>410.97</td>\n",
-       "      <td>313.83</td>\n",
-       "      <td>412.84</td>\n",
-       "      <td>313.91</td>\n",
-       "      <td>408.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-10-19</th>\n",
-       "      <td>408.77</td>\n",
-       "      <td>411.01</td>\n",
-       "      <td>313.95</td>\n",
-       "      <td>413.23</td>\n",
-       "      <td>314.05</td>\n",
-       "      <td>408.87</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-10-26</th>\n",
-       "      <td>408.68</td>\n",
-       "      <td>411.06</td>\n",
-       "      <td>313.81</td>\n",
-       "      <td>413.60</td>\n",
-       "      <td>314.21</td>\n",
-       "      <td>409.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-11-02</th>\n",
-       "      <td>409.86</td>\n",
-       "      <td>411.10</td>\n",
-       "      <td>314.95</td>\n",
-       "      <td>413.94</td>\n",
-       "      <td>314.41</td>\n",
-       "      <td>409.33</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-11-09</th>\n",
-       "      <td>410.15</td>\n",
-       "      <td>411.15</td>\n",
-       "      <td>315.19</td>\n",
-       "      <td>414.24</td>\n",
-       "      <td>314.64</td>\n",
-       "      <td>409.60</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-11-16</th>\n",
-       "      <td>410.22</td>\n",
-       "      <td>411.19</td>\n",
-       "      <td>315.22</td>\n",
-       "      <td>414.51</td>\n",
-       "      <td>314.88</td>\n",
-       "      <td>409.89</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-11-23</th>\n",
-       "      <td>410.48</td>\n",
-       "      <td>411.24</td>\n",
-       "      <td>315.43</td>\n",
-       "      <td>414.74</td>\n",
-       "      <td>315.15</td>\n",
-       "      <td>410.20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-11-30</th>\n",
-       "      <td>410.92</td>\n",
-       "      <td>411.29</td>\n",
-       "      <td>315.82</td>\n",
-       "      <td>414.93</td>\n",
-       "      <td>315.43</td>\n",
-       "      <td>410.53</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-12-07</th>\n",
-       "      <td>411.27</td>\n",
-       "      <td>411.33</td>\n",
-       "      <td>316.13</td>\n",
-       "      <td>415.06</td>\n",
-       "      <td>315.73</td>\n",
-       "      <td>410.87</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-12-14</th>\n",
-       "      <td>411.67</td>\n",
-       "      <td>411.38</td>\n",
-       "      <td>316.48</td>\n",
-       "      <td>415.15</td>\n",
-       "      <td>316.03</td>\n",
-       "      <td>411.21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-12-21</th>\n",
-       "      <td>412.30</td>\n",
-       "      <td>411.42</td>\n",
-       "      <td>317.07</td>\n",
-       "      <td>415.20</td>\n",
-       "      <td>316.33</td>\n",
-       "      <td>411.56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-12-28</th>\n",
-       "      <td>412.59</td>\n",
-       "      <td>411.47</td>\n",
-       "      <td>317.31</td>\n",
-       "      <td>415.19</td>\n",
-       "      <td>316.63</td>\n",
-       "      <td>411.91</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-01-04</th>\n",
-       "      <td>413.19</td>\n",
-       "      <td>411.51</td>\n",
-       "      <td>317.87</td>\n",
-       "      <td>415.14</td>\n",
-       "      <td>316.92</td>\n",
-       "      <td>412.25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-01-11</th>\n",
-       "      <td>413.39</td>\n",
-       "      <td>411.56</td>\n",
-       "      <td>318.02</td>\n",
-       "      <td>415.04</td>\n",
-       "      <td>317.21</td>\n",
-       "      <td>412.57</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-01-25</th>\n",
-       "      <td>413.36</td>\n",
-       "      <td>411.65</td>\n",
-       "      <td>317.90</td>\n",
-       "      <td>414.71</td>\n",
-       "      <td>317.73</td>\n",
-       "      <td>413.18</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-02-01</th>\n",
-       "      <td>413.99</td>\n",
-       "      <td>411.69</td>\n",
-       "      <td>318.49</td>\n",
-       "      <td>414.49</td>\n",
-       "      <td>317.95</td>\n",
-       "      <td>413.46</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3156 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               CO2  trend_fit  untrend_data  global_fit  fit_global  \\\n",
-       "date                                                                  \n",
-       "1958-03-29  316.19     314.57        317.81      315.44      316.20   \n",
-       "1958-04-05  317.31     314.59        318.91      315.83      316.50   \n",
-       "1958-04-12  317.69     314.60        319.28      316.21      316.80   \n",
-       "1958-04-19  317.58     314.61        319.16      316.59      317.09   \n",
-       "1958-04-26  316.48     314.63        318.04      316.94      317.36   \n",
-       "1958-05-03  316.95     314.64        318.50      317.27      317.62   \n",
-       "1958-05-17  317.56     314.67        319.08      317.83      318.07   \n",
-       "1958-05-24  317.99     314.69        319.49      318.06      318.26   \n",
-       "1958-07-05  315.85     314.78        317.26      318.48      318.68   \n",
-       "1958-07-12  315.85     314.79        317.25      318.38      318.63   \n",
-       "1958-07-19  315.46     314.81        316.84      318.24      318.54   \n",
-       "1958-07-26  315.59     314.82        316.96      318.05      318.41   \n",
-       "1958-08-02  315.64     314.84        316.99      317.83      318.26   \n",
-       "1958-08-09  315.10     314.85        316.44      317.57      318.07   \n",
-       "1958-08-16  315.09     314.87        316.41      317.28      317.85   \n",
-       "1958-08-30  314.14     314.90        315.43      316.62      317.36   \n",
-       "1958-09-06  313.54     314.91        314.82      316.27      317.08   \n",
-       "1958-11-08  313.05     315.05        314.19      313.47      314.53   \n",
-       "1958-11-15  313.26     315.06        314.39      313.30      314.32   \n",
-       "1958-11-22  313.57     315.08        314.68      313.18      314.13   \n",
-       "1958-11-29  314.01     315.09        315.11      313.10      313.98   \n",
-       "1958-12-06  314.56     315.11        315.64      313.07      313.86   \n",
-       "1958-12-13  314.41     315.12        315.48      313.08      313.77   \n",
-       "1958-12-20  314.77     315.14        315.82      313.15      313.71   \n",
-       "1958-12-27  315.21     315.15        316.25      313.26      313.70   \n",
-       "1959-01-03  315.24     315.17        316.26      313.42      313.72   \n",
-       "1959-01-10  315.50     315.18        316.51      313.63      313.77   \n",
-       "1959-01-17  315.69     315.20        316.68      313.87      313.86   \n",
-       "1959-01-24  315.86     315.21        316.84      314.15      313.99   \n",
-       "1959-01-31  315.42     315.23        316.38      314.46      314.15   \n",
-       "...            ...        ...           ...         ...         ...   \n",
-       "2019-07-06  412.69     410.33        318.55      408.31      315.48   \n",
-       "2019-07-13  412.30     410.38        318.11      408.42      315.20   \n",
-       "2019-07-20  411.76     410.42        317.53      408.58      314.93   \n",
-       "2019-07-27  410.32     410.47        316.04      408.78      314.68   \n",
-       "2019-08-03  410.50     410.52        316.17      409.03      314.45   \n",
-       "2019-08-10  410.48     410.56        316.11      409.32      314.25   \n",
-       "2019-08-17  410.05     410.61        315.63      409.64      314.07   \n",
-       "2019-08-24  409.52     410.65        315.06      409.99      313.93   \n",
-       "2019-08-31  409.32     410.70        314.81      410.37      313.82   \n",
-       "2019-09-07  408.80     410.74        314.25      410.76      313.74   \n",
-       "2019-09-14  408.61     410.79        314.01      411.18      313.70   \n",
-       "2019-09-21  408.50     410.83        313.86      411.60      313.70   \n",
-       "2019-09-28  408.28     410.88        313.59      412.02      313.73   \n",
-       "2019-10-05  407.99     410.92        313.26      412.43      313.80   \n",
-       "2019-10-12  408.61     410.97        313.83      412.84      313.91   \n",
-       "2019-10-19  408.77     411.01        313.95      413.23      314.05   \n",
-       "2019-10-26  408.68     411.06        313.81      413.60      314.21   \n",
-       "2019-11-02  409.86     411.10        314.95      413.94      314.41   \n",
-       "2019-11-09  410.15     411.15        315.19      414.24      314.64   \n",
-       "2019-11-16  410.22     411.19        315.22      414.51      314.88   \n",
-       "2019-11-23  410.48     411.24        315.43      414.74      315.15   \n",
-       "2019-11-30  410.92     411.29        315.82      414.93      315.43   \n",
-       "2019-12-07  411.27     411.33        316.13      415.06      315.73   \n",
-       "2019-12-14  411.67     411.38        316.48      415.15      316.03   \n",
-       "2019-12-21  412.30     411.42        317.07      415.20      316.33   \n",
-       "2019-12-28  412.59     411.47        317.31      415.19      316.63   \n",
-       "2020-01-04  413.19     411.51        317.87      415.14      316.92   \n",
-       "2020-01-11  413.39     411.56        318.02      415.04      317.21   \n",
-       "2020-01-25  413.36     411.65        317.90      414.71      317.73   \n",
-       "2020-02-01  413.99     411.69        318.49      414.49      317.95   \n",
-       "\n",
-       "            global_fit_global  \n",
-       "date                           \n",
-       "1958-03-29             314.58  \n",
-       "1958-04-05             314.90  \n",
-       "1958-04-12             315.21  \n",
-       "1958-04-19             315.51  \n",
-       "1958-04-26             315.80  \n",
-       "1958-05-03             316.08  \n",
-       "1958-05-17             316.56  \n",
-       "1958-05-24             316.76  \n",
-       "1958-07-05             317.27  \n",
-       "1958-07-12             317.23  \n",
-       "1958-07-19             317.15  \n",
-       "1958-07-26             317.04  \n",
-       "1958-08-02             316.90  \n",
-       "1958-08-09             316.73  \n",
-       "1958-08-16             316.53  \n",
-       "1958-08-30             316.06  \n",
-       "1958-09-06             315.80  \n",
-       "1958-11-08             313.39  \n",
-       "1958-11-15             313.19  \n",
-       "1958-11-22             313.02  \n",
-       "1958-11-29             312.88  \n",
-       "1958-12-06             312.77  \n",
-       "1958-12-13             312.70  \n",
-       "1958-12-20             312.66  \n",
-       "1958-12-27             312.66  \n",
-       "1959-01-03             312.69  \n",
-       "1959-01-10             312.76  \n",
-       "1959-01-17             312.87  \n",
-       "1959-01-24             313.01  \n",
-       "1959-01-31             313.19  \n",
-       "...                       ...  \n",
-       "2019-07-06             409.63  \n",
-       "2019-07-13             409.39  \n",
-       "2019-07-20             409.16  \n",
-       "2019-07-27             408.96  \n",
-       "2019-08-03             408.78  \n",
-       "2019-08-10             408.62  \n",
-       "2019-08-17             408.49  \n",
-       "2019-08-24             408.39  \n",
-       "2019-08-31             408.32  \n",
-       "2019-09-07             408.29  \n",
-       "2019-09-14             408.30  \n",
-       "2019-09-21             408.34  \n",
-       "2019-09-28             408.42  \n",
-       "2019-10-05             408.54  \n",
-       "2019-10-12             408.69  \n",
-       "2019-10-19             408.87  \n",
-       "2019-10-26             409.08  \n",
-       "2019-11-02             409.33  \n",
-       "2019-11-09             409.60  \n",
-       "2019-11-16             409.89  \n",
-       "2019-11-23             410.20  \n",
-       "2019-11-30             410.53  \n",
-       "2019-12-07             410.87  \n",
-       "2019-12-14             411.21  \n",
-       "2019-12-21             411.56  \n",
-       "2019-12-28             411.91  \n",
-       "2020-01-04             412.25  \n",
-       "2020-01-11             412.57  \n",
-       "2020-01-25             413.18  \n",
-       "2020-02-01             413.46  \n",
-       "\n",
-       "[3156 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 60,
@@ -1963,6 +1217,130 @@
     "r2_score(data['CO2'], data['global_fit_global'])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Prédiction pour 2025"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(52,)"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred_range = pd.date_range(start='2025/01/01', end='2025/12/31', freq='W')\n",
+    "pred_range.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time = dates_to_idx(pred_range)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred_2025 = pd.Series(global_fit_season(time), index=pred_range) - 316.19"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe4d735b6d8>"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pred_2025.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe4d7420dd8>"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Initialisation du graph\n",
+    "fig = plt.figure()\n",
+    "\n",
+    "# Définition des axes\n",
+    "ax = plt.subplot(111)\n",
+    "\n",
+    "# Titre du graph\n",
+    "ax.set_title(\"CO2 dans l'atmosphère - Base complète\")\n",
+    "\n",
+    "# Label des axes\n",
+    "ax.set_ylabel(\"Concentration de CO2 [$ppm$]\")\n",
+    "\n",
+    "# Graph sur la base complète\n",
+    "data.drop(['untrend_data', 'trend_fit', 'global_fit', 'fit_global'], axis=1).plot(ax=ax, figsize=(15,10))\n",
+    "pred_2025.plot()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,