diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index 3c2371dc267dc22d11e4fd1b55e51360d2658d84..6cd46a145bc2d6cfae8eac42287f27b357dea857 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -16,7 +16,6 @@ " %matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", - "import isoweek\n", "import requests\n", "import os" ] @@ -3550,7 +3549,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 22, @@ -3574,6 +3573,53 @@ "data['seasonally adjusted filled'].plot()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Regardons sur une période de 10 ans." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "period = (pd.Timestamp(year=1969,month=1,day=1)<=data.index) & (data.index<=pd.Timestamp(year=1979,month=1,day=1))\n", + "data[period].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il semble que l'évolution de la concentration atmosphérique de CO2 soit la somme d'une tendance linéaire et d'un signal périodique." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -3585,12 +3631,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Nous allons utiliser le module d'analyse saisonnière de `statsmodels` pour décomposer l'évolution de la concentration atmosphérique de CO2 en la somme d'une tendance et un motif périodique." + "Nous allons utiliser le module d'analyse saisonnière de `statsmodels` pour décomposer l'évolution de la concentration atmosphérique de CO2 en la somme d'une tendance et un motif périodique. On utiliste un modèle additif: Y[t] = T[t] + S[t] + e[t]. L'évolution est la somme d'une tendance, d'un signal périodique et d'un bruit." ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -3606,7 +3652,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -3615,7 +3661,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -4580,7 +4626,7 @@ "[770 rows x 10 columns]" ] }, - "execution_count": 25, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -4591,7 +4637,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -4639,12 +4685,282 @@ "result.plot()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On peut voir que la courbe de mesure du C02 est bien la somme d'une tendance haussière, d'un motif saisonnier et d'un bruit qui appraît centré." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Caractérisation du signal périodique" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "seasonal = result.seasonal" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "seasonal[(pd.Timestamp(year=1969,month=1,day=1)<=data.index) & (data.index<=pd.Timestamp(year=1974,month=1,day=1))].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le signal périodique semble avoir une période d'un an, ce qui est cohérent avec ce que l'on s'imagine." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prédictions à 2025" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous allons utiliser un simple modèle autoregessif pour prédire les valeurs de concentration jusqu'à fin 2025, basé la tendance des données historiques." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "import statsmodels.api as sm\n", + "model = sm.tsa.AR(data['seasonally adjusted filled']).fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2022-04-01 417.474450\n", + "2022-05-01 417.700892\n", + "2022-06-01 418.026783\n", + "2022-07-01 418.214831\n", + "2022-08-01 418.477222\n", + "2022-09-01 418.851828\n", + "2022-10-01 419.125336\n", + "2022-11-01 419.321020\n", + "2022-12-01 419.557878\n", + "2023-01-01 419.818090\n", + "2023-02-01 419.956343\n", + "2023-03-01 419.955904\n", + "2023-04-01 420.153815\n", + "2023-05-01 420.412869\n", + "2023-06-01 420.724373\n", + "2023-07-01 420.947375\n", + "2023-08-01 421.261647\n", + "2023-09-01 421.580019\n", + "2023-10-01 421.843831\n", + "2023-11-01 421.983701\n", + "2023-12-01 422.175147\n", + "2024-01-01 422.392138\n", + "2024-02-01 422.581931\n", + "2024-03-01 422.765989\n", + "2024-04-01 422.985692\n", + "2024-05-01 423.236671\n", + "2024-06-01 423.486124\n", + "2024-07-01 423.728360\n", + "2024-08-01 423.988920\n", + "2024-09-01 424.243029\n", + "2024-10-01 424.470824\n", + "2024-11-01 424.665367\n", + "2024-12-01 424.872283\n", + "2025-01-01 425.084218\n", + "2025-02-01 425.301274\n", + "2025-03-01 425.519118\n", + "2025-04-01 425.755889\n", + "2025-05-01 426.003422\n", + "2025-06-01 426.248779\n", + "2025-07-01 426.486635\n", + "2025-08-01 426.724917\n", + "2025-09-01 426.960411\n", + "2025-10-01 427.186092\n", + "2025-11-01 427.405423\n", + "2025-12-01 427.627688\n", + "Freq: MS, dtype: float64" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "forecast_trend = model.predict(start=pd.Timestamp(year=2022,month=4,day=1), end=pd.Timestamp(year=2025,month=12,day=1))\n", + "forecast_trend" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1958-03-01 NaN\n", + "1958-04-01 NaN\n", + "1958-05-01 NaN\n", + "1958-06-01 NaN\n", + "1958-07-01 NaN\n", + "1958-08-01 NaN\n", + "1958-09-01 315.400417\n", + "1958-10-01 315.450000\n", + "1958-11-01 315.493333\n", + "1958-12-01 315.562500\n", + "1959-01-01 315.627083\n", + "1959-02-01 315.649583\n", + "1959-03-01 315.670833\n", + "1959-04-01 315.736250\n", + "1959-05-01 315.837083\n", + "1959-06-01 315.937500\n", + "1959-07-01 316.011250\n", + "1959-08-01 316.067083\n", + "1959-09-01 316.125000\n", + "1959-10-01 316.215833\n", + "1959-11-01 316.341250\n", + "1959-12-01 316.473333\n", + "1960-01-01 316.602500\n", + "1960-02-01 316.719167\n", + "1960-03-01 316.781250\n", + "1960-04-01 316.817083\n", + "1960-05-01 316.846250\n", + "1960-06-01 316.879583\n", + "1960-07-01 316.924583\n", + "1960-08-01 316.974167\n", + " ... \n", + "2023-07-01 420.947375\n", + "2023-08-01 421.261647\n", + "2023-09-01 421.580019\n", + "2023-10-01 421.843831\n", + "2023-11-01 421.983701\n", + "2023-12-01 422.175147\n", + "2024-01-01 422.392138\n", + "2024-02-01 422.581931\n", + "2024-03-01 422.765989\n", + "2024-04-01 422.985692\n", + "2024-05-01 423.236671\n", + "2024-06-01 423.486124\n", + "2024-07-01 423.728360\n", + "2024-08-01 423.988920\n", + "2024-09-01 424.243029\n", + "2024-10-01 424.470824\n", + "2024-11-01 424.665367\n", + "2024-12-01 424.872283\n", + "2025-01-01 425.084218\n", + "2025-02-01 425.301274\n", + "2025-03-01 425.519118\n", + "2025-04-01 425.755889\n", + "2025-05-01 426.003422\n", + "2025-06-01 426.248779\n", + "2025-07-01 426.486635\n", + "2025-08-01 426.724917\n", + "2025-09-01 426.960411\n", + "2025-10-01 427.186092\n", + "2025-11-01 427.405423\n", + "2025-12-01 427.627688\n", + "Length: 815, dtype: float64" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "historical_trend = result.trend\n", + "historical_trend_color = ['blue' for _ in historical_trend]\n", + "forecast_trend_color = ['red' for _ in forecast_trend]\n", + "all_colors = [*historical_trend_color, *forecast_trend_color]\n", + "all_points = pd.concat([historical_trend, forecast_trend], axis = 0)\n", + "all_points" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(all_points.index, all_points.values, c=all_colors,s=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On peut voir la prédiction en rouge jusqu'à fin 2025." + ] } ], "metadata": {