Series_Temporales_con_RRNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicción de Series Temporales con Redes Neuronales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El artículo completo con la explicación detallada en el blog: http://www.aprendemachinelearning.com/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Usaremos Keras y Tensorflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importamos las Librerías que vamos a utilizar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:51:03.702007Z",
     "start_time": "2019-02-25T20:50:45.180334Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (16, 9)\n",
    "plt.style.use('fast')\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Activation,Flatten\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cargamos nuestro Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:51:03.748521Z",
     "start_time": "2019-02-25T20:51:03.704287Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fecha\n",
       "2017-01-02    236\n",
       "2017-01-03    237\n",
       "2017-01-04    290\n",
       "2017-01-05    221\n",
       "2017-01-07    128\n",
       "Name: unidades, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('time_series.csv',  parse_dates=[0], header=None,index_col=0, squeeze=True,names=['fecha','unidades'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:51:03.765627Z",
     "start_time": "2019-02-25T20:51:03.753400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    604.000000\n",
       "mean     215.935430\n",
       "std       75.050304\n",
       "min       51.000000\n",
       "25%      171.000000\n",
       "50%      214.000000\n",
       "75%      261.250000\n",
       "max      591.000000\n",
       "Name: unidades, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:51:03.777528Z",
     "start_time": "2019-02-25T20:51:03.770857Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-01-02 00:00:00\n",
      "2018-11-30 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print(df.index.min())\n",
    "print(df.index.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:51:03.794258Z",
     "start_time": "2019-02-25T20:51:03.784557Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "315\n",
      "289\n"
     ]
    }
   ],
   "source": [
    "print(len(df['2017']))\n",
    "print(len(df['2018']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:51:03.859508Z",
     "start_time": "2019-02-25T20:51:03.800478Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fecha\n",
       "2017-01-31    203.923077\n",
       "2017-02-28    184.666667\n",
       "2017-03-31    182.964286\n",
       "2017-04-30    198.960000\n",
       "2017-05-31    201.185185\n",
       "2017-06-30    209.518519\n",
       "2017-07-31    278.923077\n",
       "2017-08-31    316.000000\n",
       "2017-09-30    222.925926\n",
       "2017-10-31    207.851852\n",
       "2017-11-30    185.925926\n",
       "2017-12-31    213.200000\n",
       "2018-01-31    201.384615\n",
       "2018-02-28    190.625000\n",
       "2018-03-31    174.846154\n",
       "2018-04-30    186.000000\n",
       "2018-05-31    190.666667\n",
       "2018-06-30    196.037037\n",
       "2018-07-31    289.500000\n",
       "2018-08-31    309.038462\n",
       "2018-09-30    230.518519\n",
       "2018-10-31    209.444444\n",
       "2018-11-30    184.481481\n",
       "Freq: M, Name: unidades, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meses =df.resample('M').mean()\n",
    "meses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizaciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:51:04.096077Z",
     "start_time": "2019-02-25T20:51:03.862373Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a30ec5860>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(meses['2017'].values)\n",
    "plt.plot(meses['2018'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:51:04.294343Z",
     "start_time": "2019-02-25T20:51:04.099520Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a31776470>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "verano2017 = df['2017-06-01':'2017-09-01']\n",
    "plt.plot(verano2017.values)\n",
    "verano2018 = df['2018-06-01':'2018-09-01']\n",
    "plt.plot(verano2018.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocesado de los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:51:04.344102Z",
     "start_time": "2019-02-25T20:51:04.298243Z"
    }
   },
   "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>var1(t-7)</th>\n",
       "      <th>var1(t-6)</th>\n",
       "      <th>var1(t-5)</th>\n",
       "      <th>var1(t-4)</th>\n",
       "      <th>var1(t-3)</th>\n",
       "      <th>var1(t-2)</th>\n",
       "      <th>var1(t-1)</th>\n",
       "      <th>var1(t)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.314815</td>\n",
       "      <td>-0.311111</td>\n",
       "      <td>-0.114815</td>\n",
       "      <td>-0.370370</td>\n",
       "      <td>-0.714815</td>\n",
       "      <td>-0.103704</td>\n",
       "      <td>-0.225926</td>\n",
       "      <td>-0.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.311111</td>\n",
       "      <td>-0.114815</td>\n",
       "      <td>-0.370370</td>\n",
       "      <td>-0.714815</td>\n",
       "      <td>-0.103704</td>\n",
       "      <td>-0.225926</td>\n",
       "      <td>-0.433333</td>\n",
       "      <td>-0.607407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.114815</td>\n",
       "      <td>-0.370370</td>\n",
       "      <td>-0.714815</td>\n",
       "      <td>-0.103704</td>\n",
       "      <td>-0.225926</td>\n",
       "      <td>-0.433333</td>\n",
       "      <td>-0.607407</td>\n",
       "      <td>-0.522222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.370370</td>\n",
       "      <td>-0.714815</td>\n",
       "      <td>-0.103704</td>\n",
       "      <td>-0.225926</td>\n",
       "      <td>-0.433333</td>\n",
       "      <td>-0.607407</td>\n",
       "      <td>-0.522222</td>\n",
       "      <td>-0.644444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.714815</td>\n",
       "      <td>-0.103704</td>\n",
       "      <td>-0.225926</td>\n",
       "      <td>-0.433333</td>\n",
       "      <td>-0.607407</td>\n",
       "      <td>-0.522222</td>\n",
       "      <td>-0.644444</td>\n",
       "      <td>-0.344444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    var1(t-7)  var1(t-6)  var1(t-5)  var1(t-4)  var1(t-3)  var1(t-2)  \\\n",
       "7   -0.314815  -0.311111  -0.114815  -0.370370  -0.714815  -0.103704   \n",
       "8   -0.311111  -0.114815  -0.370370  -0.714815  -0.103704  -0.225926   \n",
       "9   -0.114815  -0.370370  -0.714815  -0.103704  -0.225926  -0.433333   \n",
       "10  -0.370370  -0.714815  -0.103704  -0.225926  -0.433333  -0.607407   \n",
       "11  -0.714815  -0.103704  -0.225926  -0.433333  -0.607407  -0.522222   \n",
       "\n",
       "    var1(t-1)   var1(t)  \n",
       "7   -0.225926 -0.433333  \n",
       "8   -0.433333 -0.607407  \n",
       "9   -0.607407 -0.522222  \n",
       "10  -0.522222 -0.644444  \n",
       "11  -0.644444 -0.344444  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PASOS=7\n",
    "\n",
    "# convert series to supervised learning\n",
    "def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n",
    "    n_vars = 1 if type(data) is list else data.shape[1]\n",
    "    df = pd.DataFrame(data)\n",
    "    cols, names = list(), list()\n",
    "    # input sequence (t-n, ... t-1)\n",
    "    for i in range(n_in, 0, -1):\n",
    "        cols.append(df.shift(i))\n",
    "        names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # forecast sequence (t, t+1, ... t+n)\n",
    "    for i in range(0, n_out):\n",
    "        cols.append(df.shift(-i))\n",
    "        if i == 0:\n",
    "            names += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n",
    "        else:\n",
    "            names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # put it all together\n",
    "    agg = pd.concat(cols, axis=1)\n",
    "    agg.columns = names\n",
    "    # drop rows with NaN values\n",
    "    if dropnan:\n",
    "        agg.dropna(inplace=True)\n",
    "    return agg\n",
    " \n",
    "# load dataset\n",
    "values = df.values\n",
    "# ensure all data is float\n",
    "values = values.astype('float32')\n",
    "# normalize features\n",
    "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "values=values.reshape(-1, 1) # esto lo hacemos porque tenemos 1 sola dimension\n",
    "scaled = scaler.fit_transform(values)\n",
    "# frame as supervised learning\n",
    "reframed = series_to_supervised(scaled, PASOS, 1)\n",
    "reframed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dividimos en set de Entrenamiento y Validación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:51:04.359517Z",
     "start_time": "2019-02-25T20:51:04.349323Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(567, 1, 7) (567,) (30, 1, 7) (30,)\n"
     ]
    }
   ],
   "source": [
    "# split into train and test sets\n",
    "values = reframed.values\n",
    "n_train_days = 315+289 - (30+PASOS)\n",
    "train = values[:n_train_days, :]\n",
    "test = values[n_train_days:, :]\n",
    "# split into input and outputs\n",
    "x_train, y_train = train[:, :-1], train[:, -1]\n",
    "x_val, y_val = test[:, :-1], test[:, -1]\n",
    "# reshape input to be 3D [samples, timesteps, features]\n",
    "x_train = x_train.reshape((x_train.shape[0], 1, x_train.shape[1]))\n",
    "x_val = x_val.reshape((x_val.shape[0], 1, x_val.shape[1]))\n",
    "print(x_train.shape, y_train.shape, x_val.shape, y_val.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T21:40:14.487519Z",
     "start_time": "2019-02-22T21:40:14.474395Z"
    }
   },
   "source": [
    "# Creamos el Modelo de Red Neuronal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utilizaremos una Red \"normal\" Feedforward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:51:04.368665Z",
     "start_time": "2019-02-25T20:51:04.362320Z"
    }
   },
   "outputs": [],
   "source": [
    "def crear_modeloFF():\n",
    "    model = Sequential() \n",
    "    model.add(Dense(PASOS, input_shape=(1,PASOS),activation='tanh'))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(1, activation='tanh'))\n",
    "    model.compile(loss='mean_absolute_error',optimizer='Adam',metrics=[\"mse\"])\n",
    "    model.summary()\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entrenamos nuestra máquina"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:51:12.615429Z",
     "start_time": "2019-02-25T20:51:04.373597Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 1, 7)              56        \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 7)                 0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 8         \n",
      "=================================================================\n",
      "Total params: 64\n",
      "Trainable params: 64\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 567 samples, validate on 30 samples\n",
      "Epoch 1/40\n",
      "567/567 [==============================] - 0s 795us/step - loss: 0.3436 - mean_squared_error: 0.1881 - val_loss: 0.1672 - val_mean_squared_error: 0.0444\n",
      "Epoch 2/40\n",
      "567/567 [==============================] - 0s 255us/step - loss: 0.2224 - mean_squared_error: 0.0793 - val_loss: 0.1621 - val_mean_squared_error: 0.0426\n",
      "Epoch 3/40\n",
      "567/567 [==============================] - 0s 248us/step - loss: 0.2096 - mean_squared_error: 0.0719 - val_loss: 0.1569 - val_mean_squared_error: 0.0411\n",
      "Epoch 4/40\n",
      "567/567 [==============================] - 0s 241us/step - loss: 0.1984 - mean_squared_error: 0.0660 - val_loss: 0.1562 - val_mean_squared_error: 0.0367\n",
      "Epoch 5/40\n",
      "567/567 [==============================] - 0s 252us/step - loss: 0.1895 - mean_squared_error: 0.0618 - val_loss: 0.1506 - val_mean_squared_error: 0.0374\n",
      "Epoch 6/40\n",
      "567/567 [==============================] - 0s 253us/step - loss: 0.1827 - mean_squared_error: 0.0589 - val_loss: 0.1480 - val_mean_squared_error: 0.0389\n",
      "Epoch 7/40\n",
      "567/567 [==============================] - 0s 307us/step - loss: 0.1775 - mean_squared_error: 0.0575 - val_loss: 0.1518 - val_mean_squared_error: 0.0361\n",
      "Epoch 8/40\n",
      "567/567 [==============================] - 0s 273us/step - loss: 0.1758 - mean_squared_error: 0.0565 - val_loss: 0.1469 - val_mean_squared_error: 0.0388\n",
      "Epoch 9/40\n",
      "567/567 [==============================] - 0s 254us/step - loss: 0.1730 - mean_squared_error: 0.0556 - val_loss: 0.1411 - val_mean_squared_error: 0.0360\n",
      "Epoch 10/40\n",
      "567/567 [==============================] - 0s 278us/step - loss: 0.1719 - mean_squared_error: 0.0554 - val_loss: 0.1401 - val_mean_squared_error: 0.0360\n",
      "Epoch 11/40\n",
      "567/567 [==============================] - 0s 263us/step - loss: 0.1711 - mean_squared_error: 0.0552 - val_loss: 0.1415 - val_mean_squared_error: 0.0368\n",
      "Epoch 12/40\n",
      "567/567 [==============================] - 0s 275us/step - loss: 0.1705 - mean_squared_error: 0.0563 - val_loss: 0.1392 - val_mean_squared_error: 0.0350\n",
      "Epoch 13/40\n",
      "567/567 [==============================] - 0s 275us/step - loss: 0.1704 - mean_squared_error: 0.0560 - val_loss: 0.1387 - val_mean_squared_error: 0.0356\n",
      "Epoch 14/40\n",
      "567/567 [==============================] - 0s 336us/step - loss: 0.1701 - mean_squared_error: 0.0560 - val_loss: 0.1397 - val_mean_squared_error: 0.0356\n",
      "Epoch 15/40\n",
      "567/567 [==============================] - 0s 428us/step - loss: 0.1703 - mean_squared_error: 0.0564 - val_loss: 0.1393 - val_mean_squared_error: 0.0353\n",
      "Epoch 16/40\n",
      "567/567 [==============================] - 0s 498us/step - loss: 0.1712 - mean_squared_error: 0.0564 - val_loss: 0.1398 - val_mean_squared_error: 0.0359\n",
      "Epoch 17/40\n",
      "567/567 [==============================] - 0s 458us/step - loss: 0.1692 - mean_squared_error: 0.0557 - val_loss: 0.1397 - val_mean_squared_error: 0.0352\n",
      "Epoch 18/40\n",
      "567/567 [==============================] - 0s 316us/step - loss: 0.1691 - mean_squared_error: 0.0556 - val_loss: 0.1408 - val_mean_squared_error: 0.0348\n",
      "Epoch 19/40\n",
      "567/567 [==============================] - 0s 273us/step - loss: 0.1684 - mean_squared_error: 0.0557 - val_loss: 0.1438 - val_mean_squared_error: 0.0378\n",
      "Epoch 20/40\n",
      "567/567 [==============================] - 0s 447us/step - loss: 0.1691 - mean_squared_error: 0.0555 - val_loss: 0.1394 - val_mean_squared_error: 0.0352\n",
      "Epoch 21/40\n",
      "567/567 [==============================] - 0s 554us/step - loss: 0.1687 - mean_squared_error: 0.0559 - val_loss: 0.1407 - val_mean_squared_error: 0.0350\n",
      "Epoch 22/40\n",
      "567/567 [==============================] - 0s 518us/step - loss: 0.1688 - mean_squared_error: 0.0559 - val_loss: 0.1409 - val_mean_squared_error: 0.0362\n",
      "Epoch 23/40\n",
      "567/567 [==============================] - 0s 334us/step - loss: 0.1689 - mean_squared_error: 0.0557 - val_loss: 0.1399 - val_mean_squared_error: 0.0356\n",
      "Epoch 24/40\n",
      "567/567 [==============================] - 0s 269us/step - loss: 0.1689 - mean_squared_error: 0.0558 - val_loss: 0.1412 - val_mean_squared_error: 0.0364\n",
      "Epoch 25/40\n",
      "567/567 [==============================] - 0s 259us/step - loss: 0.1690 - mean_squared_error: 0.0559 - val_loss: 0.1403 - val_mean_squared_error: 0.0350\n",
      "Epoch 26/40\n",
      "567/567 [==============================] - 0s 321us/step - loss: 0.1685 - mean_squared_error: 0.0553 - val_loss: 0.1402 - val_mean_squared_error: 0.0358\n",
      "Epoch 27/40\n",
      "567/567 [==============================] - 0s 313us/step - loss: 0.1686 - mean_squared_error: 0.0557 - val_loss: 0.1398 - val_mean_squared_error: 0.0352\n",
      "Epoch 28/40\n",
      "567/567 [==============================] - 0s 273us/step - loss: 0.1693 - mean_squared_error: 0.0555 - val_loss: 0.1409 - val_mean_squared_error: 0.0357\n",
      "Epoch 29/40\n",
      "567/567 [==============================] - 0s 347us/step - loss: 0.1694 - mean_squared_error: 0.0559 - val_loss: 0.1404 - val_mean_squared_error: 0.0359\n",
      "Epoch 30/40\n",
      "567/567 [==============================] - 0s 322us/step - loss: 0.1691 - mean_squared_error: 0.0557 - val_loss: 0.1413 - val_mean_squared_error: 0.0348\n",
      "Epoch 31/40\n",
      "567/567 [==============================] - 0s 322us/step - loss: 0.1712 - mean_squared_error: 0.0576 - val_loss: 0.1408 - val_mean_squared_error: 0.0362\n",
      "Epoch 32/40\n",
      "567/567 [==============================] - 0s 335us/step - loss: 0.1689 - mean_squared_error: 0.0554 - val_loss: 0.1418 - val_mean_squared_error: 0.0348\n",
      "Epoch 33/40\n",
      "567/567 [==============================] - 0s 331us/step - loss: 0.1680 - mean_squared_error: 0.0553 - val_loss: 0.1435 - val_mean_squared_error: 0.0353\n",
      "Epoch 34/40\n",
      "567/567 [==============================] - 0s 336us/step - loss: 0.1688 - mean_squared_error: 0.0555 - val_loss: 0.1409 - val_mean_squared_error: 0.0360\n",
      "Epoch 35/40\n",
      "567/567 [==============================] - 0s 331us/step - loss: 0.1681 - mean_squared_error: 0.0558 - val_loss: 0.1401 - val_mean_squared_error: 0.0350\n",
      "Epoch 36/40\n",
      "567/567 [==============================] - 0s 313us/step - loss: 0.1679 - mean_squared_error: 0.0551 - val_loss: 0.1424 - val_mean_squared_error: 0.0351\n",
      "Epoch 37/40\n",
      "567/567 [==============================] - 0s 321us/step - loss: 0.1682 - mean_squared_error: 0.0553 - val_loss: 0.1406 - val_mean_squared_error: 0.0351\n",
      "Epoch 38/40\n",
      "567/567 [==============================] - 0s 339us/step - loss: 0.1684 - mean_squared_error: 0.0558 - val_loss: 0.1403 - val_mean_squared_error: 0.0353\n",
      "Epoch 39/40\n",
      "567/567 [==============================] - 0s 336us/step - loss: 0.1683 - mean_squared_error: 0.0555 - val_loss: 0.1405 - val_mean_squared_error: 0.0353\n",
      "Epoch 40/40\n",
      "567/567 [==============================] - 0s 342us/step - loss: 0.1682 - mean_squared_error: 0.0555 - val_loss: 0.1402 - val_mean_squared_error: 0.0353\n"
     ]
    }
   ],
   "source": [
    "EPOCHS=40\n",
    "\n",
    "model = crear_modeloFF()\n",
    "\n",
    "history=model.fit(x_train,y_train,epochs=EPOCHS,validation_data=(x_val,y_val),batch_size=PASOS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizamos Resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:51:12.852142Z",
     "start_time": "2019-02-25T20:51:12.618559Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results=model.predict(x_val)\n",
    "print( len(results) )\n",
    "plt.scatter(range(len(y_val)),y_val,c='g')\n",
    "plt.scatter(range(len(results)),results,c='r')\n",
    "plt.title('validate')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:51:13.128405Z",
     "start_time": "2019-02-25T20:51:12.855770Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.title('loss')\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('validate loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:51:13.311943Z",
     "start_time": "2019-02-25T20:51:13.131681Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Accuracy')\n",
    "plt.plot(history.history['mean_squared_error'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:51:13.366609Z",
     "start_time": "2019-02-25T20:51:13.314722Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30, 1, 7)\n",
      "(30,)\n",
      "30\n"
     ]
    },
    {
     "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>real</th>\n",
       "      <th>prediccion</th>\n",
       "      <th>diferencia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>252.000000</td>\n",
       "      <td>214.163651</td>\n",
       "      <td>37.836349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>220.000000</td>\n",
       "      <td>203.868866</td>\n",
       "      <td>16.131134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>296.000000</td>\n",
       "      <td>198.812180</td>\n",
       "      <td>97.187820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64.999992</td>\n",
       "      <td>194.746170</td>\n",
       "      <td>-129.746185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>213.000000</td>\n",
       "      <td>196.406677</td>\n",
       "      <td>16.593323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         real  prediccion  diferencia\n",
       "0  252.000000  214.163651   37.836349\n",
       "1  220.000000  203.868866   16.131134\n",
       "2  296.000000  198.812180   97.187820\n",
       "3   64.999992  194.746170 -129.746185\n",
       "4  213.000000  196.406677   16.593323"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compara = pd.DataFrame(np.array([y_val, [x[0] for x in results]])).transpose()\n",
    "compara.columns = ['real', 'prediccion']\n",
    "\n",
    "inverted = scaler.inverse_transform(compara.values)\n",
    "\n",
    "compara2 = pd.DataFrame(inverted)\n",
    "compara2.columns = ['real', 'prediccion']\n",
    "compara2['diferencia'] = compara2['real'] - compara2['prediccion']\n",
    "compara2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:51:13.401663Z",
     "start_time": "2019-02-25T20:51:13.369640Z"
    }
   },
   "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>real</th>\n",
       "      <th>prediccion</th>\n",
       "      <th>diferencia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>191.633331</td>\n",
       "      <td>196.321518</td>\n",
       "      <td>-4.688193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>57.580818</td>\n",
       "      <td>23.251623</td>\n",
       "      <td>51.338474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>64.999992</td>\n",
       "      <td>150.876389</td>\n",
       "      <td>-129.746185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>169.000000</td>\n",
       "      <td>182.479301</td>\n",
       "      <td>-24.522148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>200.500000</td>\n",
       "      <td>197.162064</td>\n",
       "      <td>1.159111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>220.000000</td>\n",
       "      <td>207.022148</td>\n",
       "      <td>26.922920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>296.000000</td>\n",
       "      <td>241.253464</td>\n",
       "      <td>97.187820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             real  prediccion  diferencia\n",
       "count   30.000000   30.000000   30.000000\n",
       "mean   191.633331  196.321518   -4.688193\n",
       "std     57.580818   23.251623   51.338474\n",
       "min     64.999992  150.876389 -129.746185\n",
       "25%    169.000000  182.479301  -24.522148\n",
       "50%    200.500000  197.162064    1.159111\n",
       "75%    220.000000  207.022148   26.922920\n",
       "max    296.000000  241.253464   97.187820"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compara2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:51:13.594591Z",
     "start_time": "2019-02-25T20:51:13.405616Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a31d9f0f0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compara2['real'].plot()\n",
    "compara2['prediccion'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicción"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A partir de la última semana de noviembre 2018, intentaremos predecir la primer semana de diciembre."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:52:11.024445Z",
     "start_time": "2019-02-25T20:52:10.983681Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fecha\n",
       "2018-11-16    152\n",
       "2018-11-17    111\n",
       "2018-11-19    207\n",
       "2018-11-20    206\n",
       "2018-11-21    183\n",
       "2018-11-22    200\n",
       "2018-11-23    187\n",
       "2018-11-24    189\n",
       "2018-11-25     76\n",
       "2018-11-26    276\n",
       "2018-11-27    220\n",
       "2018-11-28    183\n",
       "2018-11-29    251\n",
       "2018-11-30    189\n",
       "Name: unidades, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ultimosDias = df['2018-11-16':'2018-11-30']\n",
    "ultimosDias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparamos los datos para Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:52:17.532182Z",
     "start_time": "2019-02-25T20:52:17.507681Z"
    }
   },
   "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>var1(t-7)</th>\n",
       "      <th>var1(t-6)</th>\n",
       "      <th>var1(t-5)</th>\n",
       "      <th>var1(t-4)</th>\n",
       "      <th>var1(t-3)</th>\n",
       "      <th>var1(t-2)</th>\n",
       "      <th>var1(t-1)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.65</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.31</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.30</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    var1(t-7)  var1(t-6)  var1(t-5)  var1(t-4)  var1(t-3)  var1(t-2)  \\\n",
       "7       -0.24      -0.65       0.31       0.30       0.07       0.24   \n",
       "8       -0.65       0.31       0.30       0.07       0.24       0.11   \n",
       "9        0.31       0.30       0.07       0.24       0.11       0.13   \n",
       "10       0.30       0.07       0.24       0.11       0.13      -1.00   \n",
       "11       0.07       0.24       0.11       0.13      -1.00       1.00   \n",
       "12       0.24       0.11       0.13      -1.00       1.00       0.44   \n",
       "13       0.11       0.13      -1.00       1.00       0.44       0.07   \n",
       "\n",
       "    var1(t-1)  \n",
       "7        0.11  \n",
       "8        0.13  \n",
       "9       -1.00  \n",
       "10       1.00  \n",
       "11       0.44  \n",
       "12       0.07  \n",
       "13       0.75  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = ultimosDias.values\n",
    "values = values.astype('float32')\n",
    "# normalize features\n",
    "values=values.reshape(-1, 1) # esto lo hacemos porque tenemos 1 sola dimension\n",
    "scaled = scaler.fit_transform(values)\n",
    "reframed = series_to_supervised(scaled, PASOS, 1)\n",
    "reframed.drop(reframed.columns[[7]], axis=1, inplace=True)\n",
    "reframed.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:52:21.914334Z",
     "start_time": "2019-02-25T20:52:21.904612Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1, 7)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[ 0.11000001,  0.13      , -1.        ,  1.        ,\n",
       "          0.44000006,  0.06999993,  0.75      ]]], dtype=float32)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = reframed.values\n",
    "x_test = values[6:, :]\n",
    "x_test = x_test.reshape((x_test.shape[0], 1, x_test.shape[1]))\n",
    "print(x_test.shape)\n",
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:51:13.693886Z",
     "start_time": "2019-02-25T20:51:13.687944Z"
    }
   },
   "outputs": [],
   "source": [
    "def agregarNuevoValor(x_test,nuevoValor):\n",
    "    for i in range(x_test.shape[2]-1):\n",
    "        x_test[0][0][i] = x_test[0][0][i+1]\n",
    "    x_test[0][0][x_test.shape[2]-1]=nuevoValor\n",
    "    return x_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pronóstico para la \"próxima semana\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:51:13.721175Z",
     "start_time": "2019-02-25T20:51:13.697146Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0.11000001  0.13       -1.          1.          0.44000006\n",
      "    0.06999993  0.75      ]]]\n",
      "[[[ 0.13       -1.          1.          0.44000006  0.06999993\n",
      "    0.75       -0.01510962]]]\n",
      "[[[-1.          1.          0.44000006  0.06999993  0.75\n",
      "   -0.01510962 -0.3473066 ]]]\n",
      "[[[ 1.          0.44000006  0.06999993  0.75       -0.01510962\n",
      "   -0.3473066   0.4949292 ]]]\n",
      "[[[ 0.44000006  0.06999993  0.75       -0.01510962 -0.3473066\n",
      "    0.4949292   0.2773262 ]]]\n",
      "[[[ 0.06999993  0.75       -0.01510962 -0.3473066   0.4949292\n",
      "    0.2773262   0.01309414]]]\n",
      "[[[ 0.75       -0.01510962 -0.3473066   0.4949292   0.2773262\n",
      "    0.01309414  0.32155222]]]\n"
     ]
    }
   ],
   "source": [
    "results=[]\n",
    "for i in range(7):\n",
    "    parcial=model.predict(x_test)\n",
    "    results.append(parcial[0])\n",
    "    print(x_test)\n",
    "    x_test=agregarNuevoValor(x_test,parcial[0])\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Re-Convertimos los resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T21:03:35.759150Z",
     "start_time": "2019-02-25T21:03:35.746646Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([-0.01510962], dtype=float32), array([-0.3473066], dtype=float32), array([0.4949292], dtype=float32), array([0.2773262], dtype=float32), array([0.01309414], dtype=float32), array([0.32155222], dtype=float32), array([-0.00763017], dtype=float32)]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[174.48904094],\n",
       "       [141.26934129],\n",
       "       [225.49292353],\n",
       "       [203.73262324],\n",
       "       [177.30941712],\n",
       "       [208.1552254 ],\n",
       "       [175.23698644]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adimen = [x for x in results]    \n",
    "print(adimen)\n",
    "inverted = scaler.inverse_transform(adimen)\n",
    "inverted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizamos el pronóstico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T21:08:28.155409Z",
     "start_time": "2019-02-25T21:08:27.987373Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediccion1SemanaDiciembre = pd.DataFrame(inverted)\n",
    "prediccion1SemanaDiciembre.columns = ['pronostico']\n",
    "prediccion1SemanaDiciembre.plot()\n",
    "prediccion1SemanaDiciembre.to_csv('pronostico.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:51:13.944994Z",
     "start_time": "2019-02-25T20:51:13.932992Z"
    }
   },
   "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>pronostico</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>174.489041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>141.269341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>225.492924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>203.732623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>177.309417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>208.155225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>175.236986</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pronostico\n",
       "0  174.489041\n",
       "1  141.269341\n",
       "2  225.492924\n",
       "3  203.732623\n",
       "4  177.309417\n",
       "5  208.155225\n",
       "6  175.236986"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediccion1SemanaDiciembre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Agregamos el resultado en el dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T20:51:13.974698Z",
     "start_time": "2019-02-25T20:51:13.948522Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "174.48904094075502\n",
      "141.26934128856678\n",
      "225.49292353152313\n",
      "203.73262324331603\n",
      "177.30941712430308\n",
      "208.15522539962586\n",
      "175.23698644310952\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "fecha\n",
       "2018-11-24 00:00:00    189.000000\n",
       "2018-11-25 00:00:00     76.000000\n",
       "2018-11-26 00:00:00    276.000000\n",
       "2018-11-27 00:00:00    220.000000\n",
       "2018-11-28 00:00:00    183.000000\n",
       "2018-11-29 00:00:00    251.000000\n",
       "2018-11-30 00:00:00    189.000000\n",
       "2018-12-01 00:00:00    174.489041\n",
       "2018-12-02 00:00:00    141.269341\n",
       "2018-12-03 00:00:00    225.492924\n",
       "2018-12-04 00:00:00    203.732623\n",
       "2018-12-05 00:00:00    177.309417\n",
       "2018-12-06 00:00:00    208.155225\n",
       "2018-12-07 00:00:00    175.236986\n",
       "Name: unidades, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i=0\n",
    "for fila in prediccion1SemanaDiciembre.pronostico:\n",
    "    i=i+1\n",
    "    ultimosDias.loc['2018-12-0' + str(i) + ' 00:00:00'] = fila\n",
    "    print(fila)\n",
    "ultimosDias.tail(14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El artículo completo en www.aprendemachinelearning.com"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}