08. Time Series/8.2 TimeSeries with Prophet - Medium posts.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pcMOCwCOczdc"
   },
   "source": [
    "## Series temporales con Prophet: evolución del número de posts en Medium"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "y3VEdg8QczeQ"
   },
   "source": [
    "En este segundo ejercicio intentaremos modelar el número de artículos que se publican diariamente en el blog [Medium](https://medium.com/), en el que aparecen con frecuencia artículos interesantes sobre inteligencia artificial. Comenzamos como siempre cargando las librerías que necesitaremos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6aSEdP9KczeU"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "N7loK5P8czef"
   },
   "source": [
    "Ahora subimos el dataset a nuestro espacio de trabajo de Colab desde el repositorio del curso y lo cargamos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 208
    },
    "colab_type": "code",
    "id": "Rttzq_cqczeg",
    "outputId": "1495eed9-f3fe-4016-bb53-93889745bd8d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-02-20 13:38:08--  https://raw.githubusercontent.com/CenticMurcia/curso-ciencia-datos/master/data/medium_posts.csv.zip\n",
      "Resolviendo raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.192.133, 151.101.128.133, 151.101.64.133, ...\n",
      "Conectando con raw.githubusercontent.com (raw.githubusercontent.com)[151.101.192.133]:443... conectado.\n",
      "Petición HTTP enviada, esperando respuesta... 200 OK\n",
      "Longitud: 4354594 (4,2M) [application/zip]\n",
      "Guardando como: “./../data/medium_posts.csv.zip.3”\n",
      "\n",
      "medium_posts.csv.zi 100%[===================>]   4,15M  2,23MB/s    en 1,9s    \n",
      "\n",
      "2020-02-20 13:38:10 (2,23 MB/s) - “./../data/medium_posts.csv.zip.3” guardado [4354594/4354594]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "##### DOWNLOAD DATASET. ONLY IN COLAB !!!\n",
    "!wget -P ./../data/ https://raw.githubusercontent.com/CenticMurcia/curso-ciencia-datos/master/data/medium_posts.csv.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QVmDfcJjczeo"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('./../data/medium_posts.csv.zip', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J28MI1mHczey"
   },
   "source": [
    "Nos quedaremos solo con dos columnas: `published` y `url`. La primera nos indica el momento en el que se publica el artículo, y la segunda es su dirección web. También vamos a eliminar cualquier post duplicado y los valores perdidos que pudiese haber, y por último convertiremos la fecha al tipo datetime, ya que por defecto pandas la considera como una cadena de texto. Le echamos un vistazo a las primeras cinco filas del dataset ya limpiado, ordenando por la fecha:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zuODUVcMcze1"
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>50931</th>\n",
       "      <td>1970-01-01 00:00:00.001000+00:00</td>\n",
       "      <td>https://medium.com/iiot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40243</th>\n",
       "      <td>1970-01-01 00:00:00.001000+00:00</td>\n",
       "      <td>https://medium.com/@ikaella/melon-rebranding-b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37395</th>\n",
       "      <td>1970-01-18 05:11:46.500000+00:00</td>\n",
       "      <td>http://www.novosti.rs/%D0%B2%D0%B5%D1%81%D1%82...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37398</th>\n",
       "      <td>1970-01-18 05:11:58.200000+00:00</td>\n",
       "      <td>http://www.novosti.rs/%D0%B2%D0%B5%D1%81%D1%82...</td>\n",
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       "    <tr>\n",
       "      <th>37394</th>\n",
       "      <td>1970-01-18 05:26:42.120000+00:00</td>\n",
       "      <td>http://www.novosti.rs/%D0%B2%D0%B5%D1%81%D1%82...</td>\n",
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      "text/plain": [
       "                             published  \\\n",
       "50931 1970-01-01 00:00:00.001000+00:00   \n",
       "40243 1970-01-01 00:00:00.001000+00:00   \n",
       "37395 1970-01-18 05:11:46.500000+00:00   \n",
       "37398 1970-01-18 05:11:58.200000+00:00   \n",
       "37394 1970-01-18 05:26:42.120000+00:00   \n",
       "\n",
       "                                                     url  \n",
       "50931                            https://medium.com/iiot  \n",
       "40243  https://medium.com/@ikaella/melon-rebranding-b...  \n",
       "37395  http://www.novosti.rs/%D0%B2%D0%B5%D1%81%D1%82...  \n",
       "37398  http://www.novosti.rs/%D0%B2%D0%B5%D1%81%D1%82...  \n",
       "37394  http://www.novosti.rs/%D0%B2%D0%B5%D1%81%D1%82...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['published', 'url']].dropna().drop_duplicates()\n",
    "df['published'] = pd.to_datetime(df['published'])\n",
    "df.sort_values(by=['published']).head(n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YhUm9s7gczfW"
   },
   "source": [
    "Vemos que aparecen artículos fechados en 1970, aunque Medium comenzó a funcionar el 15 de agosto de 2012, así que vamos a filtrar los posts a partir de esa fecha y llegando lasta el 26 de junio de 2017, ignorando los demás:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "colab_type": "code",
    "id": "SRiXobScczfZ",
    "outputId": "4aeb98e4-2919-42d4-95fc-21e8e81a89f5"
   },
   "outputs": [
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24630</th>\n",
       "      <td>2012-08-15 00:25:03.373000+00:00</td>\n",
       "      <td>https://medium.com/launch-day/jean-attempts-to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24631</th>\n",
       "      <td>2012-08-15 00:25:29.419000+00:00</td>\n",
       "      <td>https://medium.com/launch-day/dan-and-kristin-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17811</th>\n",
       "      <td>2012-08-15 00:34:59.502000+00:00</td>\n",
       "      <td>https://medium.com/i-m-h-o/the-world-is-social...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17281</th>\n",
       "      <td>2012-08-15 00:40:10.773000+00:00</td>\n",
       "      <td>https://medium.com/@karen/this-farm-table-1cfc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12376</th>\n",
       "      <td>2012-08-15 00:58:25.898000+00:00</td>\n",
       "      <td>https://medium.com/@hattmodgins/lighting-strik...</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                             published  \\\n",
       "24630 2012-08-15 00:25:03.373000+00:00   \n",
       "24631 2012-08-15 00:25:29.419000+00:00   \n",
       "17811 2012-08-15 00:34:59.502000+00:00   \n",
       "17281 2012-08-15 00:40:10.773000+00:00   \n",
       "12376 2012-08-15 00:58:25.898000+00:00   \n",
       "\n",
       "                                                     url  \n",
       "24630  https://medium.com/launch-day/jean-attempts-to...  \n",
       "24631  https://medium.com/launch-day/dan-and-kristin-...  \n",
       "17811  https://medium.com/i-m-h-o/the-world-is-social...  \n",
       "17281  https://medium.com/@karen/this-farm-table-1cfc...  \n",
       "12376  https://medium.com/@hattmodgins/lighting-strik...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[(df['published'] > '2012-08-15') & (df['published'] < '2017-06-26')].sort_values(by=['published'])\n",
    "df.head(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
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    "id": "34NBlBUYczff",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>published</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>89430</th>\n",
       "      <td>2017-06-25 23:00:51.161000+00:00</td>\n",
       "      <td>https://medium.com/@svetlanavoreskova/yes-that...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62153</th>\n",
       "      <td>2017-06-25 23:01:38.625000+00:00</td>\n",
       "      <td>https://medium.com/the-foul-shot/the-timberwol...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62122</th>\n",
       "      <td>2017-06-25 23:36:01.171000+00:00</td>\n",
       "      <td>https://medium.com/push-the-pace/the-official-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72471</th>\n",
       "      <td>2017-06-25 23:41:48.295000+00:00</td>\n",
       "      <td>https://medium.com/parti-xyz-developers/%EA%B4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83283</th>\n",
       "      <td>2017-06-25 23:51:43+00:00</td>\n",
       "      <td>http://www.johanr.com/blog/people-support-dreams</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                             published  \\\n",
       "89430 2017-06-25 23:00:51.161000+00:00   \n",
       "62153 2017-06-25 23:01:38.625000+00:00   \n",
       "62122 2017-06-25 23:36:01.171000+00:00   \n",
       "72471 2017-06-25 23:41:48.295000+00:00   \n",
       "83283        2017-06-25 23:51:43+00:00   \n",
       "\n",
       "                                                     url  \n",
       "89430  https://medium.com/@svetlanavoreskova/yes-that...  \n",
       "62153  https://medium.com/the-foul-shot/the-timberwol...  \n",
       "62122  https://medium.com/push-the-pace/the-official-...  \n",
       "72471  https://medium.com/parti-xyz-developers/%EA%B4...  \n",
       "83283   http://www.johanr.com/blog/people-support-dreams  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yxRIDYemczfn"
   },
   "source": [
    "Como pretendemos predecir el número de publicaciones al día vamos a agregar y contar los posts en cada momento del tiempo, creando una nueva columna `posts`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fLW2eVwEczfs"
   },
   "outputs": [],
   "source": [
    "aggr_df = df.groupby('published')[['url']].count()\n",
    "aggr_df.columns = ['posts']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "S5vK5l9Qczf4"
   },
   "source": [
    "Vemos que estamos agregando según intervalos de tiempo más pequeños que un día, así que necesitaremos agregar en intervalos (*bins*) de 24 horas, lo que en el argot del análisis de series temporales se denomina *resampling*. Como lo que vamos a hacer es reducir la frecuencia de muestreo, se denomina *downsampling*. `pandas` incorpora una función que realiza esta labor automáticamente:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "colab_type": "code",
    "id": "IH-qmA7Kczf7",
    "outputId": "bade81ba-fee5-4a13-befe-6875e7748a40"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>posts</th>\n",
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       "      <th>2012-08-15 00:00:00+00:00</th>\n",
       "      <td>16</td>\n",
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       "      <th>2012-08-16 00:00:00+00:00</th>\n",
       "      <td>11</td>\n",
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       "      <td>4</td>\n",
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       "      <td>2</td>\n",
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       "      <th>2012-08-19 00:00:00+00:00</th>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "                           posts\n",
       "published                       \n",
       "2012-08-15 00:00:00+00:00     16\n",
       "2012-08-16 00:00:00+00:00     11\n",
       "2012-08-17 00:00:00+00:00      4\n",
       "2012-08-18 00:00:00+00:00      2\n",
       "2012-08-19 00:00:00+00:00      1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_df = aggr_df.resample('D').apply(sum)\n",
    "daily_df.head(n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nglwCFv7czgJ"
   },
   "source": [
    "Ahora representaremos gráficamente nuestros datos para intentar detectar visualmente patrones estacionales y desviaciones o anormalidades. Usaremos la librería `Plotly`, que permite crear gráficos interactivos, crearemos nuestra propia función de representación, y la usaremos con nuestro dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HNhJpUeUczgM"
   },
   "outputs": [],
   "source": [
    "from plotly.offline import init_notebook_mode, iplot\n",
    "from plotly import graph_objs as go\n",
    "\n",
    "# En JupyterLab hay que comentarlo, en Colab debe ejecutarse\n",
    "# init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QZia8vkdczgU"
   },
   "outputs": [],
   "source": [
    "def plotly_df(df, title=''):\n",
    "    \"\"\"Visualize all the dataframe columns as line plots.\"\"\"\n",
    "    common_kw = dict(x=df.index, mode='lines')\n",
    "    data = [go.Scatter(y=df[c], name=c, **common_kw) for c in df.columns]\n",
    "    layout = dict(title=title)\n",
    "    fig = dict(data=data, layout=layout)\n",
    "    iplot(fig, show_link=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "colab_type": "code",
    "id": "wB9aCM53czgc",
    "outputId": "bcd3e989-3818-46bc-83a6-9473f756b669"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "mode": "lines",
         "name": "posts",
         "type": "scatter",
         "x": [
          "2012-08-15",
          "2012-08-16",
          "2012-08-17",
          "2012-08-18",
          "2012-08-19",
          "2012-08-20",
          "2012-08-21",
          "2012-08-22",
          "2012-08-23",
          "2012-08-24",
          "2012-08-25",
          "2012-08-26",
          "2012-08-27",
          "2012-08-28",
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       "layout": {
        "title": "Posts on Medium (daily)"
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotly_df(daily_df, title='Posts on Medium (daily)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QykfwNHcczgi"
   },
   "source": [
    "Resulta complicado sacar conclusiones de este gráfico, aunque sea interactivo y se pueda desplazar y hacer zoom, ya que tiene demasiada frecuencia y ruido. Por ello vamos a realizar un nuevo *resampling* agregando posts por semanas. No es la única opción para aumentar la relación señal/ruido, también podríamos hacer un [alisado por media móvil](https://en.wikipedia.org/wiki/Moving_average) o un [alisado exponencial](https://en.wikipedia.org/wiki/Exponential_smoothing)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gcHSbJ29czgj"
   },
   "outputs": [
    {
     "data": {
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       "layout": {
        "title": "Posts on Medium (weekly)"
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekly_df = daily_df.resample('W').apply(sum)\n",
    "plotly_df(weekly_df, title='Posts on Medium (weekly)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zttHOxocczgv"
   },
   "source": [
    "Esta gráfica sometida a *downsampling* resulta más fácil de analizar visualmente, por ejemplo resulta evidente el efecto de las fiestas de navidad.\n",
    "\n",
    "Vemos también que durante los primeros años de Medium el número de publicaciones era muy reducido y no representaba el comportamiento posterior, por lo que para hacer predicciones vamos a utilizar solo datos a partir de 2015:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "colab_type": "code",
    "id": "_AGbNXt5czgw",
    "outputId": "8d58262f-51d1-4dc3-a674-95143da385f8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>posts</th>\n",
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       "    <tr>\n",
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       "      <th>2015-01-01 00:00:00+00:00</th>\n",
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       "      <td>8</td>\n",
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       "      <td>27</td>\n",
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      ],
      "text/plain": [
       "                           posts\n",
       "published                       \n",
       "2015-01-01 00:00:00+00:00      8\n",
       "2015-01-02 00:00:00+00:00     11\n",
       "2015-01-03 00:00:00+00:00     11\n",
       "2015-01-04 00:00:00+00:00      8\n",
       "2015-01-05 00:00:00+00:00     27"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_df = daily_df.loc[daily_df.index >= '2015-01-01']\n",
    "daily_df.head(n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Shj_c7GLczg1"
   },
   "source": [
    "Las conclusiones más importantes que debemos sacar de los gráficos anteriores es que nuestros datos representan un proceso no estacionario con una tendencia muy marcada de crecimiento, en el que se aprecian contribuciones estacionales tanto semanales como anuales y también la contribución de los días anómalos (*holidays*).\n",
    "\n",
    "Ya prodemos proceder a modelar nuestra serie temporal y a hacer predicciones. Como en el notebook anterior, es importante asegurarse de que tenemos instaladas las versiones adecuadas de Prophet, holidays y pandas para evitar incompatibilidades:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "siimvxfsczg4"
   },
   "outputs": [],
   "source": [
    "# !pip install fbprophet\n",
    "# !pip install holidays==0.9.8\n",
    "# !pip install pandas==0.25.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WpZg_av_czg-"
   },
   "outputs": [],
   "source": [
    "from fbprophet import Prophet\n",
    "\n",
    "import logging\n",
    "logging.getLogger().setLevel(logging.ERROR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_GO-jUG1czhE"
   },
   "source": [
    "Ahora procedemos a convertir nuestros datos al formato que requiere Prophet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "colab_type": "code",
    "id": "k_Si-4blczhF",
    "outputId": "a66d5b0e-d06e-4344-b778-ebfdfb5db2b6"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ds</th>\n",
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       "      <th>902</th>\n",
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       "      <td>2017-06-22</td>\n",
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       "    <tr>\n",
       "      <th>904</th>\n",
       "      <td>2017-06-23</td>\n",
       "      <td>421</td>\n",
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       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>2017-06-24</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>906</th>\n",
       "      <td>2017-06-25</td>\n",
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      ],
      "text/plain": [
       "            ds    y\n",
       "902 2017-06-21  422\n",
       "903 2017-06-22  441\n",
       "904 2017-06-23  421\n",
       "905 2017-06-24  277\n",
       "906 2017-06-25  253"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = daily_df.reset_index()\n",
    "df.columns = ['ds', 'y']\n",
    "df.ds = df.ds.dt.tz_localize(None)\n",
    "df.tail(n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5MAMrWFGczhK"
   },
   "source": [
    "De forma general se recomienda incluir como mínimo un año de datos en el modelado de series temporales, en nuestro caso tenemos varios años. Y como suele ser también habitual, dividiremos el dataset en conjuntos de entrenamiento y validación. Quitaremos el último mes de la serie para ver la precisión de nuestras predicciones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "colab_type": "code",
    "id": "Gj4GVh-_czhL",
    "outputId": "48c8ed5b-efc1-4e57-9a12-24bbe8377c49"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "      <th>872</th>\n",
       "      <td>2017-05-22</td>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>2017-05-23</td>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>2017-05-24</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>2017-05-25</td>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>2017-05-26</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ds    y\n",
       "872 2017-05-22  341\n",
       "873 2017-05-23  388\n",
       "874 2017-05-24  375\n",
       "875 2017-05-25  298\n",
       "876 2017-05-26  269"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_size = 30\n",
    "train_df = df[:-prediction_size]\n",
    "train_df.tail(n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hIRog1-zczhQ"
   },
   "source": [
    "Pasamos ya a construir un objeto `Prophet` con los parámetros por defecto y lo entrenamos con el conjunto de entrenamiento y el comando `fit`. Usaremos también el método `Prophet.make_future_dataframe` para crear un dataframe que se extienda 30 días en el futuro para incluir los valores que quitamos anteriormente:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MjJzzJomczhV",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "m = Prophet()\n",
    "m.fit(train_df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "colab_type": "code",
    "id": "h2Q-RioHczha",
    "outputId": "3d179a31-8c8e-4dd2-e33f-9126d29830d4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>2017-06-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>903</th>\n",
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      ],
      "text/plain": [
       "            ds\n",
       "902 2017-06-21\n",
       "903 2017-06-22\n",
       "904 2017-06-23\n",
       "905 2017-06-24\n",
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      ]
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     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "future = m.make_future_dataframe(periods=prediction_size)\n",
    "future.tail(n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vuHtIuZqczhe"
   },
   "source": [
    "En la variable `forecast` colocaremos las predicciones que realizamos con los datos de validación. Al usar el comando `predict` no solo realizamos las predicciones, además se realiza un ajuste con todos los datos históricos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 163
    },
    "colab_type": "code",
    "id": "t0Pv354Wczhf",
    "outputId": "e0190940-b8b3-4181-a98b-ea1c7802b417"
   },
   "outputs": [
    {
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       "      <td>287.111072</td>\n",
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       "      <th>904</th>\n",
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       "      <td>3.478804</td>\n",
       "      <td>3.478804</td>\n",
       "      <td>-1.119637</td>\n",
       "      <td>-1.119637</td>\n",
       "      <td>-1.119637</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>278.408441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>2017-06-24</td>\n",
       "      <td>276.750096</td>\n",
       "      <td>217.546368</td>\n",
       "      <td>266.772668</td>\n",
       "      <td>276.272149</td>\n",
       "      <td>277.213518</td>\n",
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       "      <td>-34.950170</td>\n",
       "      <td>-34.950170</td>\n",
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       "      <td>-34.026715</td>\n",
       "      <td>-34.026715</td>\n",
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       "      <th>906</th>\n",
       "      <td>2017-06-25</td>\n",
       "      <td>277.450918</td>\n",
       "      <td>221.524826</td>\n",
       "      <td>271.230594</td>\n",
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       "      <td>-30.582721</td>\n",
       "      <td>-30.582721</td>\n",
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       "      <td>-0.727955</td>\n",
       "      <td>-0.727955</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>246.140241</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            ds       trend  yhat_lower  yhat_upper  trend_lower  trend_upper  \\\n",
       "902 2017-06-21  274.647630  265.032915  314.902828   274.290903   275.038564   \n",
       "903 2017-06-22  275.348452  262.133520  310.432956   274.963380   275.768378   \n",
       "904 2017-06-23  276.049274  254.132391  302.056592   275.623756   276.492985   \n",
       "905 2017-06-24  276.750096  217.546368  266.772668   276.272149   277.213518   \n",
       "906 2017-06-25  277.450918  221.524826  271.230594   276.945275   277.945071   \n",
       "\n",
       "     additive_terms  additive_terms_lower  additive_terms_upper     weekly  \\\n",
       "902       15.636196             15.636196             15.636196  17.132548   \n",
       "903       11.762621             11.762621             11.762621  13.074716   \n",
       "904        2.359168              2.359168              2.359168   3.478804   \n",
       "905      -34.950170            -34.950170            -34.950170 -34.026715   \n",
       "906      -31.310676            -31.310676            -31.310676 -30.582721   \n",
       "\n",
       "     weekly_lower  weekly_upper    yearly  yearly_lower  yearly_upper  \\\n",
       "902     17.132548     17.132548 -1.496352     -1.496352     -1.496352   \n",
       "903     13.074716     13.074716 -1.312095     -1.312095     -1.312095   \n",
       "904      3.478804      3.478804 -1.119637     -1.119637     -1.119637   \n",
       "905    -34.026715    -34.026715 -0.923455     -0.923455     -0.923455   \n",
       "906    -30.582721    -30.582721 -0.727955     -0.727955     -0.727955   \n",
       "\n",
       "     multiplicative_terms  multiplicative_terms_lower  \\\n",
       "902                   0.0                         0.0   \n",
       "903                   0.0                         0.0   \n",
       "904                   0.0                         0.0   \n",
       "905                   0.0                         0.0   \n",
       "906                   0.0                         0.0   \n",
       "\n",
       "     multiplicative_terms_upper        yhat  \n",
       "902                         0.0  290.283826  \n",
       "903                         0.0  287.111072  \n",
       "904                         0.0  278.408441  \n",
       "905                         0.0  241.799926  \n",
       "906                         0.0  246.140241  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast = m.predict(future)\n",
    "forecast.tail(n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kUmjUToqczhk"
   },
   "source": [
    "El dataframe resultante contiene varias columnas con detalles sobre la predicción, incluyendo los componentes de tendencia y estacionalidad con sus correspondientes intervalos de confianza. Las predicciones en sí están en la columna `yhat`.\n",
    "\n",
    "La propia librería `Prophet` tiene sus propias herramientas para representar los resultados. Por ejemplo, podemos usar el método `Prophet.plot`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 441
    },
    "colab_type": "code",
    "id": "4Mj2pyQGczhl",
    "outputId": "4ee73939-1687-4e88-cdf7-1c783a89bf4c"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m.plot(forecast);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "koVFhD5Mczhq"
   },
   "source": [
    "Esta gráfica no es muy informativa, la única conclusión evidente que sacamos es que hay muchos valores ubicados fuera del inervalo de confianza (*outliers*). Resulta más útil utilizar la función  `Prophet.plot_components`, que nos permite representar separadamente cada componente de la serie: tendencia, componente anual y componente semanal. Si hubiésemos introducido informacion sobre días anómalos (*holidays*) también aparecerían en esta gráfica:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 657
    },
    "colab_type": "code",
    "id": "rL85BP8mczhu",
    "outputId": "686f94e9-8a55-4b69-c4aa-7ab7b670c597"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m.plot_components(forecast);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GV718_3Tczhz"
   },
   "source": [
    "Se observa muy claramente que Prophet ha encontrado un punto de cambio de tendencia alrededor de 2016, \n",
    "cuando se aceleró el crecimiento. También se ve que los sábados y domingos se publican menos posts que otros días, y que en navidad se suele producir un descenso muy marcado."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5AYP0qz-czh3"
   },
   "source": [
    "Vamos ahora a determinar la calidad de nuestro modelo calculando el error en los últimos 30 días que usamos para predecir. Para ello tendremos que comparar los valores observados (verdaderos) $y_i$ y los valores predichos $\\hat{y}_i$. Si echamos un vistazo al objeto `forecast` que aloja nuestras predicciones veremos que contiene toda la información que necesitamos menos los valores observados, por lo que habrá que traerlos desde el dataset original. Para ello definiremos una función, ya que la usaremos varias veces:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "GGEtjFhvczh5",
    "outputId": "c8eeb168-9e9f-4570-e396-d2c27fdd0941"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ds, trend, yhat_lower, yhat_upper, trend_lower, trend_upper, additive_terms, additive_terms_lower, additive_terms_upper, weekly, weekly_lower, weekly_upper, yearly, yearly_lower, yearly_upper, multiplicative_terms, multiplicative_terms_lower, multiplicative_terms_upper, yhat\n"
     ]
    }
   ],
   "source": [
    "print(', '.join(forecast.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "v5en_CCJczh-"
   },
   "outputs": [],
   "source": [
    "def make_comparison_dataframe(historical, forecast):\n",
    "    \"\"\"Join the history with the forecast.\n",
    "    \n",
    "       The resulting dataset will contain columns 'yhat', 'yhat_lower', 'yhat_upper' and 'y'.\n",
    "    \"\"\"\n",
    "    return forecast.set_index('ds')[['yhat', 'yhat_lower', 'yhat_upper']].join(historical.set_index('ds'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8J-eLiVDcziD"
   },
   "source": [
    "Apliquemos esta función a nuestras predicciones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "colab_type": "code",
    "id": "QyWlsXSecziF",
    "outputId": "59df1e30-6682-41dd-a9c1-9d7ecf62ff15"
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yhat</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-06-21</th>\n",
       "      <td>290.283826</td>\n",
       "      <td>265.032915</td>\n",
       "      <td>314.902828</td>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-22</th>\n",
       "      <td>287.111072</td>\n",
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       "      <td>441</td>\n",
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       "      <th>2017-06-24</th>\n",
       "      <td>241.799926</td>\n",
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       "      <td>266.772668</td>\n",
       "      <td>277</td>\n",
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       "    <tr>\n",
       "      <th>2017-06-25</th>\n",
       "      <td>246.140241</td>\n",
       "      <td>221.524826</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                  yhat  yhat_lower  yhat_upper    y\n",
       "ds                                                 \n",
       "2017-06-21  290.283826  265.032915  314.902828  422\n",
       "2017-06-22  287.111072  262.133520  310.432956  441\n",
       "2017-06-23  278.408441  254.132391  302.056592  421\n",
       "2017-06-24  241.799926  217.546368  266.772668  277\n",
       "2017-06-25  246.140241  221.524826  271.230594  253"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ds = df.ds.dt.tz_localize(None)\n",
    "cmp_df = make_comparison_dataframe(df, forecast)\n",
    "cmp_df.tail(n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "S9QhqERecziL"
   },
   "source": [
    "Nos falta por definir una función más para determinar la calidad de las predicciones calculando los errores MAPE y MAE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OeZQufAfcziM"
   },
   "outputs": [],
   "source": [
    "def calculate_forecast_errors(df, prediction_size):\n",
    "    \"\"\"Calculate MAPE and MAE of the forecast.\n",
    "    \n",
    "       Args:\n",
    "           df: joined dataset with 'y' and 'yhat' columns.\n",
    "           prediction_size: number of days at the end to predict.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Make a copy\n",
    "    df = df.copy()\n",
    "    \n",
    "    # Now we calculate the values of e_i and p_i according to the formulas given in the article above.\n",
    "    df['e'] = df['y'] - df['yhat']\n",
    "    df['p'] = 100 * df['e'] / df['y']\n",
    "    \n",
    "    # Recall that we held out the values of the last `prediction_size` days\n",
    "    # in order to predict them and measure the quality of the model. \n",
    "    \n",
    "    # Now cut out the part of the data which we made our prediction for.\n",
    "    predicted_part = df[-prediction_size:]\n",
    "    \n",
    "    # Define the function that averages absolute error values over the predicted part.\n",
    "    error_mean = lambda error_name: np.mean(np.abs(predicted_part[error_name]))\n",
    "    \n",
    "    # Now we can calculate MAPE and MAE and return the resulting dictionary of errors.\n",
    "    return {'MAPE': error_mean('p'), 'MAE': error_mean('e')}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "T18a2CTCcziU",
    "outputId": "ebc98278-2ed1-4eba-90ae-870dc85664f4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE 22.738294605590095\n",
      "MAE 70.52942851097268\n"
     ]
    }
   ],
   "source": [
    "for err_name, err_value in calculate_forecast_errors(cmp_df, prediction_size).items():\n",
    "    print(err_name, err_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BogDPvhkcziX"
   },
   "source": [
    "Vemos que el error relativo de nuestras predicciones es de alrededor de 22.7% y en promedio nos equivocamos por alrededor de 70 publicaciones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_yr4BkDccziX"
   },
   "source": [
    "Para una mejor visualización del modelo vamos a crear nuestra propia función `show_forecast`, incluyendo los valores observados, las predicciones y los intervalos de confianza. Primero representamos los datos para un periodo de tiempo  más corto, para distinguir mejor los puntos. Luego mostramos la eficiencia del modelo para el periodo de los últimos 30 días. Y por último usamos `Plotly` para hacer la gráfica interactiva. Para más información sobre cómo funciona `Plotly` puede consultarse su [documentación](https://plot.ly/python/)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "colab_type": "code",
    "id": "epksOIFTcziZ",
    "outputId": "6eb4c7f3-e6df-453a-ba48-38e9d429b02f"
   },
   "outputs": [
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       ],
       "layout": {
        "showlegend": false,
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        "yaxis": {
         "title": "Posts"
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_forecast(cmp_df, num_predictions, num_values, title):\n",
    "    \"\"\"Visualize the forecast.\"\"\"\n",
    "    \n",
    "    def create_go(name, column, num, **kwargs):\n",
    "        points = cmp_df.tail(num)\n",
    "        args = dict(name=name, x=points.index, y=points[column], mode='lines')\n",
    "        args.update(kwargs)\n",
    "        return go.Scatter(**args)\n",
    "    \n",
    "    lower_bound = create_go('Lower Bound', 'yhat_lower', num_predictions,\n",
    "                            line=dict(width=1),\n",
    "                            marker=dict(color=\"green\"))\n",
    "    upper_bound = create_go('Upper Bound', 'yhat_upper', num_predictions,\n",
    "                            line=dict(width=1),\n",
    "                            marker=dict(color=\"green\"),\n",
    "                            fillcolor='rgba(68, 68, 68, 0.3)', \n",
    "                            fill='tonexty')\n",
    "    forecast = create_go('Forecast', 'yhat', num_predictions,\n",
    "                         line=dict(color='rgb(31, 119, 180)'))\n",
    "    actual = create_go('Actual', 'y', num_values,\n",
    "                       marker=dict(color=\"red\"))\n",
    "    \n",
    "    # In this case the order of the series is important because of the filling\n",
    "    data = [lower_bound, upper_bound, forecast, actual]\n",
    "\n",
    "    layout = go.Layout(yaxis=dict(title='Posts'), title=title, showlegend = False)\n",
    "    fig = go.Figure(data=data, layout=layout)\n",
    "    iplot(fig, show_link=False)\n",
    "\n",
    "show_forecast(cmp_df, prediction_size, 100, 'New posts on Medium')"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dUSiyFqsczif"
   },
   "source": [
    "A primera vista se observa que las predicciones son razonablemente buenas, aunque se obtiene un valor relativamente alto de MAPE, que parece indicar que el modelo no es capaz de capturar la magnitud del efecto estacional semanal. Se observa que muchas predicciones caen fuera del intervalo de confianza.\n",
    "\n",
    "En realidad, Prophet no funciona bien con series temporales que tienen varianza inestable, al menos si se usan los parámetros por defecto. Para solucionarlo podemos aplicar una transformación de nuestros datos, siendo la más frecuente y efectiva la [transformación de Box-Cox](http://onlinestatbook.com/2/transformations/box-cox.html).\n",
    "\n",
    "Se trata de una transformación monotónica que se usa para estabilizar la varianza. Usaremos la transformacíon Box-Cox de un único parámetro $\\lambda$, según la siguiente expresión:\n",
    "\n",
    "$$\n",
    "\\begin{equation}\n",
    "  boxcox^{(\\lambda)}(y_{i}) = \\begin{cases}\n",
    "    \\frac{\\displaystyle y_{i}^{\\lambda} - 1}{\\displaystyle \\lambda} & \\text{si $\\lambda \\neq 0$}.\\\\\n",
    "    ln(y_{i}) & \\text{si $\\lambda = 0$}.\n",
    "  \\end{cases}\n",
    "\\end{equation}\n",
    "$$\n",
    "\n",
    "También tendremos que definir la inversa de dicha transformación, para reconvertir los datos a su formato original:\n",
    "\n",
    "$$\n",
    "\\begin{equation}\n",
    "  invboxcox^{(\\lambda)}(y_{i}) = \\begin{cases}\n",
    "    e^{\\left (\\frac{\\displaystyle ln(\\lambda y_{i} + 1)}{\\displaystyle \\lambda} \\right )} & \\text{si $\\lambda \\neq 0$}.\\\\\n",
    "    e^{y_{i}} & \\text{si $\\lambda = 0$}.\n",
    "  \\end{cases}\n",
    "\\end{equation}\n",
    "$$\n",
    "\n",
    "Definimos esta transformación inversa y preparamos nuestro dataset estableciendo su índice:"
   ]
  },
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   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kiT5bXDEczii"
   },
   "outputs": [],
   "source": [
    "def inverse_boxcox(y, lambda_):\n",
    "    return np.exp(y) if lambda_ == 0 else np.exp(np.log(lambda_ * y + 1) / lambda_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hC1wGvncczim"
   },
   "outputs": [],
   "source": [
    "train_df2 = train_df.copy().set_index('ds')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gM_CYLmXcziq"
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   "source": [
    "Usaremos la función `stats.boxcox` de la librería `Scipy`, que aplica la transformación Box–Cox y que devuelve dos valores. El primero es la serie transformada, y el segundo es valor óptimo estimado de $\\lambda$ en términos de máxima verosimilitud:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PTuPfVtPczir"
   },
   "outputs": [],
   "source": [
    "train_df2['y'], lambda_prophet = stats.boxcox(train_df2['y'])\n",
    "train_df2.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "s5zf_gzhcziw"
   },
   "source": [
    "Creamos ahora un nuevo modelo de `Prophet` y repetimos el proceso de ajuste y predicción realizado anteriormente, revertiendo luego la transformación Box-Cox con nuestra función y el valor que hemos determinado para $\\lambda$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uG1nsmGzczix"
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   "outputs": [],
   "source": [
    "m2 = Prophet()\n",
    "m2.fit(train_df2)\n",
    "future2 = m2.make_future_dataframe(periods=prediction_size)\n",
    "forecast2 = m2.predict(future2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Sp_a4ytaczi3"
   },
   "outputs": [],
   "source": [
    "for column in ['yhat', 'yhat_lower', 'yhat_upper']:\n",
    "    forecast2[column] = inverse_boxcox(forecast2[column], lambda_prophet)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OjppZhl2czi-"
   },
   "source": [
    "Ahora reutilizamos las funciones que definimos anteriormente para comparar valores observados y predichos y calcular errores, comprobando que ambos errores MAPE y MAE se reducen casi a la mitad:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "zYloyrnxczjA",
    "outputId": "f760bbab-47c8-4455-a8cb-4501e95924e4"
   },
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE 11.638811526886505\n",
      "MAE 39.279702380921556\n"
     ]
    }
   ],
   "source": [
    "cmp_df2 = make_comparison_dataframe(df, forecast2)\n",
    "for err_name, err_value in calculate_forecast_errors(cmp_df2, prediction_size).items():\n",
    "    print(err_name, err_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Dei1I_X7czjE"
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   "source": [
    "Por último volvemos a representar gráficamente los resultados, comprobando que los cambios semanales ahora sí que encajan mejor en el modelo."
   ]
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    "show_forecast(cmp_df, prediction_size, 100, 'No transformations')\n",
    "show_forecast(cmp_df2, prediction_size, 100, 'Box–Cox transformation')"
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  },
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   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "Hemos visto que Prophet es una herramienta muy potente y fácil de usar, aunque las predicciones que realiza con la configuración por defecto no sean [ideales](https://en.wikipedia.org/wiki/No_free_lunch_in_search_and_optimization). Para afinar los modelos hay que ajustar muchos parámetros y realizar transformaciones de los datos, si son necesarias. Es una tarea árdua y compleja, que requiere de muchos conocimientos y experiencia. Sin embargo, con muy poca experiencia podemos crear muy facilmente modelos que consideran muchos efectos, como los estacionales o los días anormales (festivos)."
   ]
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  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GzLUT-LwczjN"
   },
   "source": [
    "## Referencias\n",
    "\n",
    "- Repositorio oficial de [Prophet](https://github.com/facebookincubator/prophet) en GitHub.\n",
    "- Documentación oficial de [Prophet](https://facebookincubator.github.io/prophet/docs/quick_start.html).\n",
    "- El artículo científico de Taylor y Letham, [\"Forecasting at scale\"](https://facebookincubator.github.io/prophet/static/prophet_paper_20170113.pdf), en el que se explica el algoritmo con el que trabaja `Prophet`.\n",
    "- [Forecasting Website Traffic Using Facebook’s Prophet Library](http://pbpython.com/prophet-overview.html) — Una introducción práctica a `Prophet` con un ejemplo para predecir el tráfico de una página web.\n",
    "- Rob J. Hyndman, George Athanasopoulos [\"Forecasting: principles and practice\"](https://www.otexts.org/fpp) – Un libro excelente sobre análisis de series temporales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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