{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dbb28a98",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "# LLMs based Forecasting（via DeepSeek API）\n",
    "\n",
    "## Feng Li\n",
    "\n",
    "### Guanghua School of Management\n",
    "### Peking University\n",
    "\n",
    "### [feng.li@gsm.pku.edu.cn](feng.li@gsm.pku.edu.cn)\n",
    "### Course home page: [https://feng.li/forecasting-with-ai](https://feng.li/forecasting-with-ai)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62e5e84a-4879-453e-b4bc-8c37a06d0672",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Let’s forecast\n",
    "\n",
    "我们在这里使用 **DeepSeek API** 来完成“用历史乘客（passenger）数据预测未来几期”的任务。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e67c3caf",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "\u001b[33mWARNING: Skipping /usr/lib/python3.12/dist-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
      "\u001b[0mLooking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Requirement already satisfied: openai in /home/fli/.local/lib/python3.12/site-packages (1.107.2)\n",
      "Requirement already satisfied: anyio<5,>=3.5.0 in /usr/local/lib/python3.12/dist-packages (from openai) (4.5.0)\n",
      "Requirement already satisfied: distro<2,>=1.7.0 in /usr/lib/python3/dist-packages (from openai) (1.9.0)\n",
      "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from openai) (0.27.2)\n",
      "Requirement already satisfied: jiter<1,>=0.4.0 in /home/fli/.local/lib/python3.12/site-packages (from openai) (0.10.0)\n",
      "Requirement already satisfied: pydantic<3,>=1.9.0 in /usr/local/lib/python3.12/dist-packages (from openai) (2.9.2)\n",
      "Requirement already satisfied: sniffio in /usr/local/lib/python3.12/dist-packages (from openai) (1.3.1)\n",
      "Requirement already satisfied: tqdm>4 in /home/fli/.local/lib/python3.12/site-packages (from openai) (4.67.1)\n",
      "Requirement already satisfied: typing-extensions<5,>=4.11 in /home/fli/.local/lib/python3.12/site-packages (from openai) (4.15.0)\n",
      "Requirement already satisfied: idna>=2.8 in /usr/lib/python3/dist-packages (from anyio<5,>=3.5.0->openai) (3.6)\n",
      "Requirement already satisfied: certifi in /usr/lib/python3/dist-packages (from httpx<1,>=0.23.0->openai) (2023.11.17)\n",
      "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->openai) (1.0.5)\n",
      "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->openai) (0.14.0)\n",
      "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=1.9.0->openai) (0.7.0)\n",
      "Requirement already satisfied: pydantic-core==2.23.4 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=1.9.0->openai) (2.23.4)\n",
      "\u001b[33mWARNING: Skipping /usr/lib/python3.12/dist-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "! pip install openai --break-system-packages"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f697d602-a60d-43cc-9529-e79e5b5f49eb",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Load the DeepSeek API\n",
    "\n",
    "### 如何申请 DeepSeek API Key\n",
    "1. 登录 DeepSeek 平台的 API Keys 页面 (https://platform.deepseek.com/api_keys) 创建 Key 。\n",
    "2. 点击创建 API KEY\n",
    "3. 输入 API KEY 的名称\n",
    "4. 复制 API KEY。⚠️请注意，一定要立刻复制"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4685fdd-edc5-4bf5-b2a6-b20cc60f7a3a",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### Use the DeepSeek API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9bc594a3",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import json, re, pandas as pd\n",
    "from openai import OpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e8a4a51-043a-4d60-beba-76070fbdfea7",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "DEEPSEEK_API_KEY = \"YOUR_DEEPSEEK_API_KEY\"  # ← 替换为你的真实 Key\n",
    "client = OpenAI(api_key=DEEPSEEK_API_KEY, base_url=\"https://api.deepseek.com/v1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ecc0cf5-213d-4cc9-acfd-086ca6d4a943",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## 读取 passenger 数据\n",
    "\n",
    "要求列：`unique_id`, `ds`, `y`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dc5564a6",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "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>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>D1</td>\n",
       "      <td>1949-01-01</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D1</td>\n",
       "      <td>1949-02-01</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D1</td>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D1</td>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>D1</td>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>D1</td>\n",
       "      <td>1960-08-01</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>D1</td>\n",
       "      <td>1960-09-01</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>D1</td>\n",
       "      <td>1960-10-01</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>D1</td>\n",
       "      <td>1960-11-01</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>D1</td>\n",
       "      <td>1960-12-01</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    unique_id         ds    y\n",
       "0          D1 1949-01-01  112\n",
       "1          D1 1949-02-01  118\n",
       "2          D1 1949-03-01  132\n",
       "3          D1 1949-04-01  129\n",
       "4          D1 1949-05-01  121\n",
       "..        ...        ...  ...\n",
       "139        D1 1960-08-01  606\n",
       "140        D1 1960-09-01  508\n",
       "141        D1 1960-10-01  461\n",
       "142        D1 1960-11-01  390\n",
       "143        D1 1960-12-01  432\n",
       "\n",
       "[144 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path = \"../data/air_passengers_with_id.csv\"\n",
    "df = pd.read_csv(data_path)\n",
    "df.columns = [c.lower() for c in df.columns]\n",
    "df[\"ds\"] = pd.to_datetime(df[\"ds\"], errors=\"raise\")   # 把时间列转化成 Pandas 的时间格式\n",
    "df = df.sort_values([\"unique_id\",\"ds\"]).reset_index(drop=True)\n",
    "uid = df[\"unique_id\"].iloc[0]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11f861a1-3c8f-4458-b0e5-a5703f481503",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## 可视化原始时间序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "50a7d52d-9426-4cc1-800c-ab0d7fe26fff",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'D1 — history'}, xlabel='ds'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_plot = df[df['unique_id']==uid].sort_values('ds').set_index('ds')\n",
    "df_plot['y'].plot(title=f'{uid} — history', figsize=(10,4), legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b392740c-9e69-4da4-a8ca-d1205383a014",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## DeepSeek 预测\n",
    "我们通过提示词约定 **严格 JSON** 输出，并解析为 DataFrame。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8c385d56",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def build_prompt(df, h=12, freq='MS', tail=120):\n",
    "    sub = df[df['unique_id']==uid].sort_values('ds').tail(tail)\n",
    "    hist = [{'ds': r.ds.strftime('%Y-%m-%d'), 'y': float(r.y)} for r in sub.itertuples(index=False)]\n",
    "    return (\n",
    "        f'你是一名时间序列预测助手。下面给出乘客序列的历史数据（频率 {freq}）。\\n'\n",
    "        f'请预测未来 {h} 期，并**只输出严格 JSON**。\\n\\n'\n",
    "        f'【历史数据】\\n{json.dumps(hist, ensure_ascii=False, indent=2)}\\n\\n'\n",
    "        '{\\n'\n",
    "        f'  \"h\": {h},\\n'\n",
    "        f'  \"freq\": \"{freq}\",\\n'\n",
    "        '  \"forecast\": [ { \"ds\": \"YYYY-MM-DD\", \"yhat\": <float> } ]\\n'\n",
    "        '}'\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a94a88cc-8477-4cdb-9b95-1b1589fa893a",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'你是一名时间序列预测助手。下面给出乘客序列的历史数据（频率 MS）。\\n请预测未来 12 期，并**只输出严格 JSON**。\\n\\n【历史数据】\\n[\\n  {\\n    \"ds\": \"1951-01-01\",\\n    \"y\": 145.0\\n  },\\n  {\\n    \"ds\": \"1951-02-01\",\\n    \"y\": 150.0\\n  },\\n  {\\n    \"ds\": \"1951-03-01\",\\n    \"y\": 178.0\\n  },\\n  {\\n    \"ds\": \"1951-04-01\",\\n    \"y\": 163.0\\n  },\\n  {\\n    \"ds\": \"1951-05-01\",\\n    \"y\": 172.0\\n  },\\n  {\\n    \"ds\": \"1951-06-01\",\\n    \"y\": 178.0\\n  },\\n  {\\n    \"ds\": \"1951-07-01\",\\n    \"y\": 199.0\\n  },\\n  {\\n    \"ds\": \"1951-08-01\",\\n    \"y\": 199.0\\n  },\\n  {\\n    \"ds\": \"1951-09-01\",\\n    \"y\": 184.0\\n  },\\n  {\\n    \"ds\": \"1951-10-01\",\\n    \"y\": 162.0\\n  },\\n  {\\n    \"ds\": \"1951-11-01\",\\n    \"y\": 146.0\\n  },\\n  {\\n    \"ds\": \"1951-12-01\",\\n    \"y\": 166.0\\n  },\\n  {\\n    \"ds\": \"1952-01-01\",\\n    \"y\": 171.0\\n  },\\n  {\\n    \"ds\": \"1952-02-01\",\\n    \"y\": 180.0\\n  },\\n  {\\n    \"ds\": \"1952-03-01\",\\n    \"y\": 193.0\\n  },\\n  {\\n    \"ds\": \"1952-04-01\",\\n    \"y\": 181.0\\n  },\\n  {\\n    \"ds\": \"1952-05-01\",\\n    \"y\": 183.0\\n  },\\n  {\\n    \"ds\": \"1952-06-01\",\\n    \"y\": 218.0\\n  },\\n  {\\n    \"ds\": \"1952-07-01\",\\n    \"y\": 230.0\\n  },\\n  {\\n    \"ds\": \"1952-08-01\",\\n    \"y\": 242.0\\n  },\\n  {\\n    \"ds\": \"1952-09-01\",\\n    \"y\": 209.0\\n  },\\n  {\\n    \"ds\": \"1952-10-01\",\\n    \"y\": 191.0\\n  },\\n  {\\n    \"ds\": \"1952-11-01\",\\n    \"y\": 172.0\\n  },\\n  {\\n    \"ds\": \"1952-12-01\",\\n    \"y\": 194.0\\n  },\\n  {\\n    \"ds\": \"1953-01-01\",\\n    \"y\": 196.0\\n  },\\n  {\\n    \"ds\": \"1953-02-01\",\\n    \"y\": 196.0\\n  },\\n  {\\n    \"ds\": \"1953-03-01\",\\n    \"y\": 236.0\\n  },\\n  {\\n    \"ds\": \"1953-04-01\",\\n    \"y\": 235.0\\n  },\\n  {\\n    \"ds\": \"1953-05-01\",\\n    \"y\": 229.0\\n  },\\n  {\\n    \"ds\": \"1953-06-01\",\\n    \"y\": 243.0\\n  },\\n  {\\n    \"ds\": \"1953-07-01\",\\n    \"y\": 264.0\\n  },\\n  {\\n    \"ds\": \"1953-08-01\",\\n    \"y\": 272.0\\n  },\\n  {\\n    \"ds\": \"1953-09-01\",\\n    \"y\": 237.0\\n  },\\n  {\\n    \"ds\": \"1953-10-01\",\\n    \"y\": 211.0\\n  },\\n  {\\n    \"ds\": \"1953-11-01\",\\n    \"y\": 180.0\\n  },\\n  {\\n    \"ds\": \"1953-12-01\",\\n    \"y\": 201.0\\n  },\\n  {\\n    \"ds\": \"1954-01-01\",\\n    \"y\": 204.0\\n  },\\n  {\\n    \"ds\": \"1954-02-01\",\\n    \"y\": 188.0\\n  },\\n  {\\n    \"ds\": \"1954-03-01\",\\n    \"y\": 235.0\\n  },\\n  {\\n    \"ds\": \"1954-04-01\",\\n    \"y\": 227.0\\n  },\\n  {\\n    \"ds\": \"1954-05-01\",\\n    \"y\": 234.0\\n  },\\n  {\\n    \"ds\": \"1954-06-01\",\\n    \"y\": 264.0\\n  },\\n  {\\n    \"ds\": \"1954-07-01\",\\n    \"y\": 302.0\\n  },\\n  {\\n    \"ds\": \"1954-08-01\",\\n    \"y\": 293.0\\n  },\\n  {\\n    \"ds\": \"1954-09-01\",\\n    \"y\": 259.0\\n  },\\n  {\\n    \"ds\": \"1954-10-01\",\\n    \"y\": 229.0\\n  },\\n  {\\n    \"ds\": \"1954-11-01\",\\n    \"y\": 203.0\\n  },\\n  {\\n    \"ds\": \"1954-12-01\",\\n    \"y\": 229.0\\n  },\\n  {\\n    \"ds\": \"1955-01-01\",\\n    \"y\": 242.0\\n  },\\n  {\\n    \"ds\": \"1955-02-01\",\\n    \"y\": 233.0\\n  },\\n  {\\n    \"ds\": \"1955-03-01\",\\n    \"y\": 267.0\\n  },\\n  {\\n    \"ds\": \"1955-04-01\",\\n    \"y\": 269.0\\n  },\\n  {\\n    \"ds\": \"1955-05-01\",\\n    \"y\": 270.0\\n  },\\n  {\\n    \"ds\": \"1955-06-01\",\\n    \"y\": 315.0\\n  },\\n  {\\n    \"ds\": \"1955-07-01\",\\n    \"y\": 364.0\\n  },\\n  {\\n    \"ds\": \"1955-08-01\",\\n    \"y\": 347.0\\n  },\\n  {\\n    \"ds\": \"1955-09-01\",\\n    \"y\": 312.0\\n  },\\n  {\\n    \"ds\": \"1955-10-01\",\\n    \"y\": 274.0\\n  },\\n  {\\n    \"ds\": \"1955-11-01\",\\n    \"y\": 237.0\\n  },\\n  {\\n    \"ds\": \"1955-12-01\",\\n    \"y\": 278.0\\n  },\\n  {\\n    \"ds\": \"1956-01-01\",\\n    \"y\": 284.0\\n  },\\n  {\\n    \"ds\": \"1956-02-01\",\\n    \"y\": 277.0\\n  },\\n  {\\n    \"ds\": \"1956-03-01\",\\n    \"y\": 317.0\\n  },\\n  {\\n    \"ds\": \"1956-04-01\",\\n    \"y\": 313.0\\n  },\\n  {\\n    \"ds\": \"1956-05-01\",\\n    \"y\": 318.0\\n  },\\n  {\\n    \"ds\": \"1956-06-01\",\\n    \"y\": 374.0\\n  },\\n  {\\n    \"ds\": \"1956-07-01\",\\n    \"y\": 413.0\\n  },\\n  {\\n    \"ds\": \"1956-08-01\",\\n    \"y\": 405.0\\n  },\\n  {\\n    \"ds\": \"1956-09-01\",\\n    \"y\": 355.0\\n  },\\n  {\\n    \"ds\": \"1956-10-01\",\\n    \"y\": 306.0\\n  },\\n  {\\n    \"ds\": \"1956-11-01\",\\n    \"y\": 271.0\\n  },\\n  {\\n    \"ds\": \"1956-12-01\",\\n    \"y\": 306.0\\n  },\\n  {\\n    \"ds\": \"1957-01-01\",\\n    \"y\": 315.0\\n  },\\n  {\\n    \"ds\": \"1957-02-01\",\\n    \"y\": 301.0\\n  },\\n  {\\n    \"ds\": \"1957-03-01\",\\n    \"y\": 356.0\\n  },\\n  {\\n    \"ds\": \"1957-04-01\",\\n    \"y\": 348.0\\n  },\\n  {\\n    \"ds\": \"1957-05-01\",\\n    \"y\": 355.0\\n  },\\n  {\\n    \"ds\": \"1957-06-01\",\\n    \"y\": 422.0\\n  },\\n  {\\n    \"ds\": \"1957-07-01\",\\n    \"y\": 465.0\\n  },\\n  {\\n    \"ds\": \"1957-08-01\",\\n    \"y\": 467.0\\n  },\\n  {\\n    \"ds\": \"1957-09-01\",\\n    \"y\": 404.0\\n  },\\n  {\\n    \"ds\": \"1957-10-01\",\\n    \"y\": 347.0\\n  },\\n  {\\n    \"ds\": \"1957-11-01\",\\n    \"y\": 305.0\\n  },\\n  {\\n    \"ds\": \"1957-12-01\",\\n    \"y\": 336.0\\n  },\\n  {\\n    \"ds\": \"1958-01-01\",\\n    \"y\": 340.0\\n  },\\n  {\\n    \"ds\": \"1958-02-01\",\\n    \"y\": 318.0\\n  },\\n  {\\n    \"ds\": \"1958-03-01\",\\n    \"y\": 362.0\\n  },\\n  {\\n    \"ds\": \"1958-04-01\",\\n    \"y\": 348.0\\n  },\\n  {\\n    \"ds\": \"1958-05-01\",\\n    \"y\": 363.0\\n  },\\n  {\\n    \"ds\": \"1958-06-01\",\\n    \"y\": 435.0\\n  },\\n  {\\n    \"ds\": \"1958-07-01\",\\n    \"y\": 491.0\\n  },\\n  {\\n    \"ds\": \"1958-08-01\",\\n    \"y\": 505.0\\n  },\\n  {\\n    \"ds\": \"1958-09-01\",\\n    \"y\": 404.0\\n  },\\n  {\\n    \"ds\": \"1958-10-01\",\\n    \"y\": 359.0\\n  },\\n  {\\n    \"ds\": \"1958-11-01\",\\n    \"y\": 310.0\\n  },\\n  {\\n    \"ds\": \"1958-12-01\",\\n    \"y\": 337.0\\n  },\\n  {\\n    \"ds\": \"1959-01-01\",\\n    \"y\": 360.0\\n  },\\n  {\\n    \"ds\": \"1959-02-01\",\\n    \"y\": 342.0\\n  },\\n  {\\n    \"ds\": \"1959-03-01\",\\n    \"y\": 406.0\\n  },\\n  {\\n    \"ds\": \"1959-04-01\",\\n    \"y\": 396.0\\n  },\\n  {\\n    \"ds\": \"1959-05-01\",\\n    \"y\": 420.0\\n  },\\n  {\\n    \"ds\": \"1959-06-01\",\\n    \"y\": 472.0\\n  },\\n  {\\n    \"ds\": \"1959-07-01\",\\n    \"y\": 548.0\\n  },\\n  {\\n    \"ds\": \"1959-08-01\",\\n    \"y\": 559.0\\n  },\\n  {\\n    \"ds\": \"1959-09-01\",\\n    \"y\": 463.0\\n  },\\n  {\\n    \"ds\": \"1959-10-01\",\\n    \"y\": 407.0\\n  },\\n  {\\n    \"ds\": \"1959-11-01\",\\n    \"y\": 362.0\\n  },\\n  {\\n    \"ds\": \"1959-12-01\",\\n    \"y\": 405.0\\n  },\\n  {\\n    \"ds\": \"1960-01-01\",\\n    \"y\": 417.0\\n  },\\n  {\\n    \"ds\": \"1960-02-01\",\\n    \"y\": 391.0\\n  },\\n  {\\n    \"ds\": \"1960-03-01\",\\n    \"y\": 419.0\\n  },\\n  {\\n    \"ds\": \"1960-04-01\",\\n    \"y\": 461.0\\n  },\\n  {\\n    \"ds\": \"1960-05-01\",\\n    \"y\": 472.0\\n  },\\n  {\\n    \"ds\": \"1960-06-01\",\\n    \"y\": 535.0\\n  },\\n  {\\n    \"ds\": \"1960-07-01\",\\n    \"y\": 622.0\\n  },\\n  {\\n    \"ds\": \"1960-08-01\",\\n    \"y\": 606.0\\n  },\\n  {\\n    \"ds\": \"1960-09-01\",\\n    \"y\": 508.0\\n  },\\n  {\\n    \"ds\": \"1960-10-01\",\\n    \"y\": 461.0\\n  },\\n  {\\n    \"ds\": \"1960-11-01\",\\n    \"y\": 390.0\\n  },\\n  {\\n    \"ds\": \"1960-12-01\",\\n    \"y\": 432.0\\n  }\\n]\\n\\n{\\n  \"h\": 12,\\n  \"freq\": \"MS\",\\n  \"forecast\": [ { \"ds\": \"YYYY-MM-DD\", \"yhat\": <float> } ]\\n}'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prompt = build_prompt(df, h=12, freq='MS')\n",
    "prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "da77abcd",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 我们写一个函数解析一下JSON的输出\n",
    "def parse_json(text):\n",
    "    try: return json.loads(text)\n",
    "    except: pass\n",
    "    if '```' in text:\n",
    "        for part in text.split('```'):\n",
    "            p = part.strip()\n",
    "            if p.lower().startswith('json'): p = p[4:].strip()\n",
    "            try: return json.loads(p)\n",
    "            except: pass\n",
    "    m = re.search(r'\\{[\\s\\S]*\\}', text)\n",
    "    if m: return json.loads(m.group(0))\n",
    "    raise ValueError('无法解析JSON: '+text[:200])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76d6e1e7-4da2-4da5-b7e5-3ea976c2faab",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 6期预测（short horizon）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3edf798c-363b-44a7-aae7-166fba26aa6c",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "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>ds</th>\n",
       "      <th>yhat</th>\n",
       "      <th>unique_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1961-01-01</td>\n",
       "      <td>445.2</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961-02-01</td>\n",
       "      <td>420.1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1961-03-01</td>\n",
       "      <td>488.3</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1961-04-01</td>\n",
       "      <td>475.6</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1961-05-01</td>\n",
       "      <td>496.8</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds   yhat unique_id\n",
       "0 1961-01-01  445.2        D1\n",
       "1 1961-02-01  420.1        D1\n",
       "2 1961-03-01  488.3        D1\n",
       "3 1961-04-01  475.6        D1\n",
       "4 1961-05-01  496.8        D1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prompt = build_prompt(df, h=6, freq='MS')\n",
    "resp = client.chat.completions.create(model='deepseek-chat', messages=[{'role':'user','content':prompt}], temperature=0)\n",
    "raw = resp.choices[0].message.content\n",
    "obj = parse_json(raw)\n",
    "fc6 = pd.DataFrame(obj['forecast']).assign(unique_id=uid)\n",
    "fc6['ds'] = pd.to_datetime(fc6['ds'])\n",
    "fc6['yhat'] = pd.to_numeric(fc6['yhat'], errors='coerce')\n",
    "fc6 = fc6.dropna().sort_values('ds').reset_index(drop=True)\n",
    "fc6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "54fe7337-64e4-4505-8dc1-5a14244d6a7b",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10ae8f750>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist = df[df['unique_id']==uid].sort_values('ds').set_index('ds')\n",
    "fc_plot = fc6.set_index('ds')\n",
    "ax = hist['y'].plot(label='history', figsize=(10,4))\n",
    "fc_plot['yhat'].plot(ax=ax, label='forecast')\n",
    "ax.set_title(f\"{uid} — history & 6-step forecast\")\n",
    "ax.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dd7253e-bdfe-4dd2-8129-33896b34e5e6",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 12 期预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4db0e228-0226-4734-90ab-f989be1a45f3",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "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>ds</th>\n",
       "      <th>yhat</th>\n",
       "      <th>unique_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1961-01-01</td>\n",
       "      <td>444.2</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961-02-01</td>\n",
       "      <td>422.1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1961-03-01</td>\n",
       "      <td>456.8</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1961-04-01</td>\n",
       "      <td>478.3</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1961-05-01</td>\n",
       "      <td>492.6</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds   yhat unique_id\n",
       "0 1961-01-01  444.2        D1\n",
       "1 1961-02-01  422.1        D1\n",
       "2 1961-03-01  456.8        D1\n",
       "3 1961-04-01  478.3        D1\n",
       "4 1961-05-01  492.6        D1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prompt = build_prompt(df, h=12, freq='MS')\n",
    "resp = client.chat.completions.create(model='deepseek-chat', messages=[{'role':'user','content':prompt}], temperature=0)\n",
    "raw = resp.choices[0].message.content\n",
    "obj = parse_json(raw)\n",
    "fc12 = pd.DataFrame(obj['forecast']).assign(unique_id=uid)\n",
    "fc12['ds'] = pd.to_datetime(fc12['ds'])\n",
    "fc12['yhat'] = pd.to_numeric(fc12['yhat'], errors='coerce')\n",
    "fc12 = fc12.dropna().sort_values('ds').reset_index(drop=True)\n",
    "fc12.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c2a79408",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10ae8da90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist = df[df['unique_id']==uid].sort_values('ds').set_index('ds')\n",
    "fc_plot = fc12.set_index('ds')\n",
    "ax = hist['y'].plot(label='history', figsize=(10,4))\n",
    "fc_plot['yhat'].plot(ax=ax, label='forecast')\n",
    "ax.set_title(f\"{uid} — history & 12-step forecast\")\n",
    "ax.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8404dd81-726f-4643-9234-5427a724916a",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 36期预测（long horizon）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e809a824-205c-4a4c-bbe8-7da229cf9f66",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "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>ds</th>\n",
       "      <th>yhat</th>\n",
       "      <th>unique_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1961-01-01</td>\n",
       "      <td>445.2</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961-02-01</td>\n",
       "      <td>420.1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1961-03-01</td>\n",
       "      <td>461.8</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1961-04-01</td>\n",
       "      <td>475.3</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1961-05-01</td>\n",
       "      <td>489.6</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds   yhat unique_id\n",
       "0 1961-01-01  445.2        D1\n",
       "1 1961-02-01  420.1        D1\n",
       "2 1961-03-01  461.8        D1\n",
       "3 1961-04-01  475.3        D1\n",
       "4 1961-05-01  489.6        D1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prompt = build_prompt(df, h=36, freq='MS')\n",
    "resp = client.chat.completions.create(model='deepseek-chat', messages=[{'role':'user','content':prompt}], temperature=0)\n",
    "raw = resp.choices[0].message.content\n",
    "obj = parse_json(raw)\n",
    "fc36 = pd.DataFrame(obj['forecast']).assign(unique_id=uid)\n",
    "fc36['ds'] = pd.to_datetime(fc36['ds'])\n",
    "fc36['yhat'] = pd.to_numeric(fc36['yhat'], errors='coerce')\n",
    "fc36 = fc36.dropna().sort_values('ds').reset_index(drop=True)\n",
    "fc36.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c7ea3577-71bc-442d-b181-7aad874b4cf8",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10af80a50>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist = df[df['unique_id']==uid].sort_values('ds').set_index('ds')\n",
    "fc_plot = fc36.set_index('ds')\n",
    "ax = hist['y'].plot(label='history', figsize=(10,4))\n",
    "fc_plot['yhat'].plot(ax=ax, label='forecast')\n",
    "ax.set_title(f\"{uid} — history & 36-step forecast\")\n",
    "ax.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a125110-dea8-4a7d-927c-fa04f64c354d",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Lab\n",
    "\n",
    "- 请找一个你自己的数据，调整成 `air_passengers_with_id.csv` 的格式。\n",
    "- 使用我们上面的程序做一个预测，利用你的行业知识评估一下一下这个预测是否是一个可靠的预测。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "/usr/bin/python (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}