mba.ipynb · OSCHINA-MIRROR/thales-ucas-postgrad

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  {
   "source": [
    "# 时间序列\n",
    "\n",
    "\n",
    "之前的时间序列入门，很多人还是觉得不好理解\n",
    "\n",
    "\n",
    "那么我们趁着出分的热度，专门做一下2021年国家线的时间序列预测把\n",
    "\n",
    "\n",
    "## 考研MBA分数分析\n",
    "\n",
    "\n",
    "MBA数值少，好理解\n",
    "\n",
    "\n",
    "2009~2020年 MBA分数分析\n",
    "\n",
    "\n",
    "预测2021年分数线\n",
    "\n",
    "\n",
    "> 不见得准确，因为数据量太少，但是可以好好理解一下时间序列\n",
    "\n",
    "\n",
    "我们把所有的功能只保留三部————\n",
    "\n",
    "\n",
    "- 数据读取\n",
    "- 模型构建\n",
    "- 预测\n",
    "\n",
    "\n",
    "# 数据读取\n",
    "\n",
    "\n",
    "xls表mba.xlsx"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "       总分(A)  英语(A)  管综(A)  总分(B)  英语(B)  管综(B)\n",
       "年份                                             \n",
       "2009年    155     50     85    145     45     77\n",
       "2010年    165     47     94    155     42     84\n",
       "2011年    165     45     90    155     40     80\n",
       "2012年    150     41     82    140     36     72\n",
       "2013年    155     41     82    145     37     72\n",
       "2014年    160     41     82    150     36     72\n",
       "2015年    160     40     80    150     35     70\n",
       "2016年    165     39     78    155     34     68\n",
       "2017年    170     42     84    160     37     74\n",
       "2018年    165     42     84    155     37     74\n",
       "2019年    170     42     84    160     37     74\n",
       "2020年    175     44     88    165     39     78"
      ],
      "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>总分(A)</th>\n      <th>英语(A)</th>\n      <th>管综(A)</th>\n      <th>总分(B)</th>\n      <th>英语(B)</th>\n      <th>管综(B)</th>\n    </tr>\n    <tr>\n      <th>年份</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>2009年</th>\n      <td>155</td>\n      <td>50</td>\n      <td>85</td>\n      <td>145</td>\n      <td>45</td>\n      <td>77</td>\n    </tr>\n    <tr>\n      <th>2010年</th>\n      <td>165</td>\n      <td>47</td>\n      <td>94</td>\n      <td>155</td>\n      <td>42</td>\n      <td>84</td>\n    </tr>\n    <tr>\n      <th>2011年</th>\n      <td>165</td>\n      <td>45</td>\n      <td>90</td>\n      <td>155</td>\n      <td>40</td>\n      <td>80</td>\n    </tr>\n    <tr>\n      <th>2012年</th>\n      <td>150</td>\n      <td>41</td>\n      <td>82</td>\n      <td>140</td>\n      <td>36</td>\n      <td>72</td>\n    </tr>\n    <tr>\n      <th>2013年</th>\n      <td>155</td>\n      <td>41</td>\n      <td>82</td>\n      <td>145</td>\n      <td>37</td>\n      <td>72</td>\n    </tr>\n    <tr>\n      <th>2014年</th>\n      <td>160</td>\n      <td>41</td>\n      <td>82</td>\n      <td>150</td>\n      <td>36</td>\n      <td>72</td>\n    </tr>\n    <tr>\n      <th>2015年</th>\n      <td>160</td>\n      <td>40</td>\n      <td>80</td>\n      <td>150</td>\n      <td>35</td>\n      <td>70</td>\n    </tr>\n    <tr>\n      <th>2016年</th>\n      <td>165</td>\n      <td>39</td>\n      <td>78</td>\n      <td>155</td>\n      <td>34</td>\n      <td>68</td>\n    </tr>\n    <tr>\n      <th>2017年</th>\n      <td>170</td>\n      <td>42</td>\n      <td>84</td>\n      <td>160</td>\n      <td>37</td>\n      <td>74</td>\n    </tr>\n    <tr>\n      <th>2018年</th>\n      <td>165</td>\n      <td>42</td>\n      <td>84</td>\n      <td>155</td>\n      <td>37</td>\n      <td>74</td>\n    </tr>\n    <tr>\n      <th>2019年</th>\n      <td>170</td>\n      <td>42</td>\n      <td>84</td>\n      <td>160</td>\n      <td>37</td>\n      <td>74</td>\n    </tr>\n    <tr>\n      <th>2020年</th>\n      <td>175</td>\n      <td>44</td>\n      <td>88</td>\n      <td>165</td>\n      <td>39</td>\n      <td>78</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 1
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_excel('./mba.xlsx', index_col='年份')\n",
    "df"
   ]
  },
  {
   "source": [
    "## 时间序列图表\n",
    "\n",
    "\n",
    "画个图看看效果\n",
    "\n",
    "\n",
    "> 完全可以在xls里面画，这步可以省略"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['font.family'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "df.plot()"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='年份'>"
      ]
     },
     "metadata": {},
     "execution_count": 2
    },
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "source": [
    "# 模型构建\n",
    "\n",
    "\n",
    "获取一下最佳p和q\n",
    "\n",
    "\n",
    "我们在p=0、1、2、3、4和q=0、1、2中做对比\n",
    "\n",
    "\n",
    "这时候画一个热力图效果很好\n",
    "\n",
    "\n",
    "> 这个函数直接拿去用，不用管里面怎么写的"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from statsmodels.tsa import arima_model\n",
    "import itertools\n",
    "import seaborn as sns #热力图\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "def thermodynamicOrder(df, ar=4, ma=2):\n",
    "    results_aic = pd.DataFrame(\\\n",
    "        index=['AR{}'.format(i) for i in range(0, ar+1)],\\\n",
    "        columns=['MA{}'.format(i) for i in range(0, ma+1)])\n",
    "    for p, q in itertools.product(range(0, ar+1), range(0, ma+1)):\n",
    "        if p==0 and q==0:\n",
    "            results_aic.loc['AR{}'.format(p), 'MA{}'.format(q)] = np.nan\n",
    "            continue\n",
    "        try:\n",
    "            results = arima_model.ARMA(df, (p, q)).fit()\n",
    "            #返回不同pq下的model的BIC值\n",
    "            results_aic.loc['AR{}'.format(p), 'MA{}'.format(q)] = results.aic\n",
    "        except:\n",
    "            continue\n",
    "    results_aic = results_aic[results_aic.columns].astype(float)\n",
    "    fig, ax = plt.subplots(figsize=(10, 8))\n",
    "    ax = sns.heatmap(results_aic,\n",
    "                    #mask=results_aic.isnull(),\n",
    "                    ax=ax,\n",
    "                    annot=True, #将数字显示在热力图上\n",
    "                    fmt='.2f',\n",
    "                    )\n",
    "    ax.set_title('AIC')\n",
    "    plt.show()"
   ]
  },
  {
   "source": [
    "## A区总分建模\n",
    "\n",
    "\n",
    "df里面是所有数据，所以我们要只取里面的某一列\n",
    "\n",
    "\n",
    "python的pandas直接可以按照列取值，非常方便"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x576 with 2 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "thermodynamicOrder(df['总分(A)'], 4, 2)"
   ]
  },
  {
   "source": [
    "记住，最小的值代表最好，并且也是最暗\n",
    "\n",
    "\n",
    "这里是81.78最小，对应的是AR0和MA1\n",
    "\n",
    "\n",
    "也就是p=0，q=1 效果最好\n",
    "\n",
    "\n",
    "然后我们把这个值带入模型\n",
    "\n",
    "\n",
    "> 必须是有色块的值才能用，空白的是非法值，不能用\n",
    "\n",
    "\n",
    "# 预测\n",
    "\n",
    "\n",
    "## 使用刚才的参数创建模型预测2021年数据"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(array([166.07142745]),\n",
       " array([5.11281472]),\n",
       " array([[156.05049474, 176.09236017]]))"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "arima_model.ARMA(df['总分(A)'], (0, 1)).fit().forecast(1)"
   ]
  },
  {
   "source": [
    "我们现在预测出来了总分数据\n",
    "\n",
    "\n",
    "均值 165(166.07142745)\n",
    "\n",
    "\n",
    "最大值 175(176.09236017)\n",
    "\n",
    "\n",
    "最小值 155(156.05049474)\n",
    "\n",
    "\n",
    "> 因为分数线都是5分为最小单位，所以四舍五入\n",
    "\n",
    "\n",
    "## 然后用同样的方法去预测其他的数据\n",
    "\n",
    "\n",
    "### A区英语"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "thermodynamicOrder(df['英语(A)'], 4, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(array([44.12855737]),\n",
       " array([1.92440793]),\n",
       " array([[40.35678715, 47.9003276 ]]))"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "arima_model.ARMA(df['英语(A)'], (1, 0)).fit().forecast(1)"
   ]
  },
  {
   "source": [
    "### A区管综"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x576 with 2 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "thermodynamicOrder(df['管综(A)'], 4, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(array([85.78637262]),\n",
       " array([3.08996814]),\n",
       " array([[79.73014636, 91.84259889]]))"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "arima_model.ARMA(df['管综(A)'], (0, 1)).fit().forecast(1)"
   ]
  },
  {
   "source": [
    "### B区总分"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x576 with 2 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "thermodynamicOrder(df['总分(B)'], 4, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(array([156.07142745]),\n",
       " array([5.11281472]),\n",
       " array([[146.05049474, 166.09236017]]))"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "arima_model.ARMA(df['总分(B)'], (0, 1)).fit().forecast(1)"
   ]
  },
  {
   "source": [
    "### B区英语"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x576 with 2 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "thermodynamicOrder(df['英语(B)'], 4, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(array([39.13014005]), array([1.96343784]), array([[35.2818726, 42.9784075]]))"
      ]
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "arima_model.ARMA(df['英语(B)'], (1, 0)).fit().forecast(1)"
   ]
  },
  {
   "source": [
    "### B区管综"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x576 with 2 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "thermodynamicOrder(df['管综(B)'], 4, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(array([75.75000016]),\n",
       " array([2.96273148]),\n",
       " array([[69.94315315, 81.55684716]]))"
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "arima_model.ARMA(df['管综(B)'], (0, 1)).fit().forecast(1)"
   ]
  },
  {
   "source": [
    "# 我们预测2021年A区分数线为\n",
    "\n",
    "## 均值\n",
    "总分:165\n",
    "英语:44\n",
    "管综:85\n",
    "\n",
    "## 最高\n",
    "总分:175\n",
    "英语:48\n",
    "管综:91\n",
    "\n",
    "## 最低\n",
    "总分:156\n",
    "英语:40\n",
    "管综:80\n",
    "\n",
    "\n",
    "# 我们预测2021年B区分数线为\n",
    "\n",
    "## 均值\n",
    "总分:155\n",
    "英语:39\n",
    "管综:76\n",
    "\n",
    "## 最高\n",
    "总分:165\n",
    "英语:43\n",
    "管综:82\n",
    "\n",
    "## 最低\n",
    "总分:146\n",
    "英语:35\n",
    "管综:70\n",
    "\n",
    "\n",
    "# 预测是不会考虑外因的，我倾向于按照最高来算\n",
    "\n",
    "\n",
    "## A区\n",
    "\n",
    "\n",
    "总分:175\n",
    "英语:48\n",
    "管综:91\n",
    "\n",
    "\n",
    "## B区\n",
    "\n",
    "\n",
    "总分:165\n",
    "英语:43\n",
    "管综:82\n",
    "\n",
    "\n",
    "\n",
    "到了国家线发榜时间咱们看看预测结果~\n",
    "\n",
    "\n",
    "再次声明，不要过多在意数值，本文只是时间序列的应用\n",
    "\n",
    "\n",
    "时间序列，调参非常重要，了解之后，如果想深入，一定要做自相关\n",
    "\n",
    "\n",
    "具体内容请看 [时间序列基础](./README.md)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  }
 ]
}

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