서울_부동산매매_시계열_LSTM_분석.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "서울 부동산매매 시계열 LSTM 분석.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Jungsan005/Bigdata-Class/blob/master/%EC%84%9C%EC%9A%B8_%EB%B6%80%EB%8F%99%EC%82%B0%EB%A7%A4%EB%A7%A4_%EC%8B%9C%EA%B3%84%EC%97%B4_LSTM_%EB%B6%84%EC%84%9D.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kGs7YRc_YoQl"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "maf1UWtTWEIb",
        "outputId": "2228439c-4d5d-4ca8-a1a9-de78403f7f84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# 데이터 불러오기\n",
        "seoul = pd.read_csv('https://raw.githubusercontent.com/Jungsan005/Bigdata-Class/master/seoul.csv')\n",
        "seoul.head(5)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>year</th>\n",
              "      <th>month</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>497.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010</td>\n",
              "      <td>2</td>\n",
              "      <td>481.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2010</td>\n",
              "      <td>3</td>\n",
              "      <td>478.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2010</td>\n",
              "      <td>4</td>\n",
              "      <td>471.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2010</td>\n",
              "      <td>5</td>\n",
              "      <td>475.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   year  month  price\n",
              "0  2010      1  497.6\n",
              "1  2010      2  481.1\n",
              "2  2010      3  478.7\n",
              "3  2010      4  471.2\n",
              "4  2010      5  475.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tqEjEbSvYi_C",
        "outputId": "1ea43001-8ed8-461c-e256-6230be96d692",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "# 시각화로 데이터 확인\n",
        "plt.plot(seoul.price)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fb28d9b3908>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f0b1XwqdWaRa"
      },
      "source": [
        "import math\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from keras.callbacks import EarlyStopping"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nZYg2eCoXlaT"
      },
      "source": [
        "# 랜덤 시드 설정\n",
        "np.random.seed(2008072100)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RUkBNVJNXoab",
        "outputId": "f29376a9-e178-4eb4-d993-472074a10087",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "# 평당 가격 데이터를 float32로 변환\n",
        "dataset = seoul.price.to_numpy()\n",
        "dataset = dataset.astype('float32')\n",
        "dataset[:12]"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([497.6, 481.1, 478.7, 471.2, 475. , 468.7, 479.2, 463.1, 469.2,\n",
              "       462.9, 476.4, 475.6], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lz7UwjelYK9o",
        "outputId": "6b374a67-98e8-4273-fd48-30eb53be3042",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        }
      },
      "source": [
        "# 값의 범위를 0~1로 정규화\n",
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "dataset = scaler.fit_transform(dataset.reshape(-1,1))\n",
        "dataset[:12]"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.11123788],\n",
              "       [0.06417561],\n",
              "       [0.05733025],\n",
              "       [0.03593838],\n",
              "       [0.04677689],\n",
              "       [0.02880776],\n",
              "       [0.05875635],\n",
              "       [0.01283514],\n",
              "       [0.03023386],\n",
              "       [0.01226461],\n",
              "       [0.05077004],\n",
              "       [0.04848826]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JDmH17xeYVSM",
        "outputId": "018efb95-264c-413a-8736-e8c0acd3c17a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# 7.5:2.5 비율로 훈련데이터와 테스트데이터 분리 \n",
        "train_size = int(len(dataset) * 0.75)\n",
        "test_size = len(dataset) - train_size\n",
        "train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]\n",
        "print(len(train), len(test))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "90 30\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hbRo6Mc6aX8S",
        "outputId": "36735ea1-ea54-46c9-c26c-506b1e6110e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "# 훈련데이터와 테스트데이터 시각화\n",
        "x = len(train)+1\n",
        "y = len(test)+1\n",
        "df_train = pd.DataFrame(train, index=range(1, x))\n",
        "df_test = pd.DataFrame(test, index=range(x, x+y-1) )\n",
        "\n",
        "ax = df_train.plot()\n",
        "df_test.plot(ax=ax)\n",
        "plt.legend(['train', 'test'])"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fb2441bdda0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ysgpyDDRdFoo"
      },
      "source": [
        "# X는 현재 시점(t)의 평당 가격이고, \n",
        "# Y는 다음 시점(t+1)의 평당 가격인 데이터 집합을 생성하는 함수 정의\n",
        "def shift_dataset(dataset, shift=1):\n",
        "\tdataX, dataY = [], []\n",
        "\tfor i in range(len(dataset)-shift-1):\n",
        "\t\ta = dataset[i:(i+shift), 0]\n",
        "\t\tdataX.append(a)\n",
        "\t\tdataY.append(dataset[i + shift, 0])\n",
        "\treturn np.array(dataX), np.array(dataY)"
      ],
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r7R4LQdbdt9K"
      },
      "source": [
        "# train 데이터와 test 데이터에 대해 X, Y 컬럼 생성\n",
        "shift = 1\n",
        "X_train, y_train = shift_dataset(train, shift)\n",
        "X_test, y_test = shift_dataset(test, shift)"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PeuYa-mWeEPh"
      },
      "source": [
        "# keras에서는 RNN 계열 모델을 훈련시킬때 \n",
        "# 데이터는 반드시 3차원 형식이어야 하고 \n",
        "# 각 차원은 (sample, timestep, feature) 로 구성되어야 함\n",
        "# 일반적인 MLP 모델에서는 sample와 feature 등으로 구성되는 2차원 배열이지만,\n",
        "# RNN에서는 \"시간\"이라는 개념이 포함되기 때문에 차원이 하나 더 늘어남\n",
        "\n",
        "X_train = X_train.reshape(X_train.shape[0], 1, X_train.shape[1])\n",
        "X_test = X_test.reshape(X_test.shape[0], 1, X_test.shape[1])"
      ],
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "orkcKrBaeJJF",
        "outputId": "d2fee05a-842a-448f-f476-809f557324d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        }
      },
      "source": [
        "# keras를 이용해서 LSTM 모델 생성 \n",
        "# 입력유형은 (timestep, feature) \n",
        "# 출력유형은 target의 갯수가 1이므로 Dense(1)을 추가함 \n",
        "# 연속적인 값을 예측하는 것이기 때문에 손실함수는 mean squared error로 설정\n",
        "# 최적화함수는 일반적으로 adam으로 설정\n",
        "# 훈련 데이터를 100번 사용해서 학습을 실시함\n",
        "# 훈련 데이터를 5개의 작은 조각batch들로 나누고 이 조각들을 순회하며 학습을 진행\n",
        "# 따라서, 총 학습횟수는 이포크수 x (훈련데이터수 / 배치사이즈) \n",
        "# 학습시 훈련의 진행도를 보여주는 진행 막대를 보여줌\n",
        "\n",
        "model = Sequential()\n",
        "model.add(LSTM(4, input_shape=(1, shift)))\n",
        "model.add(Dense(1))\n",
        "model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "\n",
        "# model.fit(X_train, y_train, epochs=100, batch_size=1, verbose=1)\n",
        "history = model.fit(X_train, y_train, epochs=100, batch_size=1, verbose=0,\n",
        "                    validation_data=(X_test, y_test), \n",
        "                    callbacks=[EarlyStopping(monitor='val_loss', patience=10)])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_3 (LSTM)                (None, 4)                 96        \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 5         \n",
            "=================================================================\n",
            "Total params: 101\n",
            "Trainable params: 101\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QXvLgHLnwOpV",
        "outputId": "0f190efd-e02f-465f-a217-1945eda0595e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "plt.plot(history.history['loss'], label='Train Loss')\n",
        "plt.plot(history.history['val_loss'], label='Test Loss')\n",
        "plt.legend()"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fb237143860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fGHRFItJGmdlC51xRQ/P0y9imyM6D8dPggFEw+zIofijoikREvkJB31RZ7WDc3+CgE+DvV8Ab9wddkYjIDhT06ZCVC+f8FfqfDM9eA58uDroiEZE6Cvp0ieXAGfd419s/dyO0snMfItJ2KejTqV1nOOY6+Phl+DCzryIVkfBQ0Kdb0QXQ7UB4/idQUx10NSIiCvq0i2bB8VPh8w9h4cNBVyMioqBvFgefAr2PhHn/CxWlQVcjIm2cgr45mMGJv4CtG2H+7UFXIyJtnIK+uew3FAaPhwV/gi9WB12NiLRhCvrmdOxPwKLw4s+CrkRE2jAFfXPq1ANGXOZ1Z1ySIf33iEjoKOib2xE/hPbdYe4N+hGViARCQd/ccjrAsTfCmtfh/aeDrkZE2iAFfUsYOhG6D4QXboJ45e6XFxFJIwV9S4hE4YSfwxer4I37gq5GRNoYBX1LOfA4OGA0vHwbbM38m2yJSOZQ0LekE34BlWXw71uDrkRE2hAFfUvaeyB8/Xx48wH4fEXQ1YhIG6Ggb2mjboRYrndiVkSkBSjoW1p+dzjyCvjg77DqlaCrEZE2QEEfhG/+ADr2gH9erz7rRaTZKeiDkJ0HJ90Cny2Gf/866GpEJOQU9EEZOMbr3XL+b2DNG0FXIyIhpqAP0sm3Qsee8OTFUFkedDUiElIK+iDldoIz7/V+MTv3+qCrEZGQSinozewkM1tmZivM7LoG5h9lZovMLG5mZ9WbV2Nmb/uP2ekqPDR6j/B6uFz0F/jg2aCrEZEQ2m3Qm1kUuAs4GRgIjDezgfUW+wSYDDzWwEtsc84N8R9jmlhvOI26EfY5FGZfDuXrg65GREImlRb9MGCFc26lc64KmAaMTV7AObfKObcYSDRDjeEXy4Yz7/e6R3j6MvVbLyJplUrQ9wDWJI2X+NNSlWtmxWa2wMy+1dACZnaxv0zxhg0bGvHSIdJ9ABz/M1g+FxY+FHQ1IhIiLXEytrdzrgg4F7jTzA6ov4Bz7j7nXJFzrqigoKAFSmqlhl0C/Y6BuTeqLxwRSZtUgn4t0CtpvKc/LSXOubX+80pgHjC0EfW1LZEIfOtuiGbDUxfrV7MikhapBP2bwEFm1tfMsoFxQEpXz5hZFzPL8Yf3Ao4A3t/TYtuEjvvBaXfA2oXw8m+CrkZEQmC3Qe+ciwOXAXOBpcAM59wSM5tqZmMAzOwbZlYCnA3ca2ZL/NUHAMVm9g7wEnCLc05BvzuFZ8Kgc7yblJQUB12NiGQ4c63sCo+ioiJXXKxwo6IU7j4CollwyXzIyQ+6IhFpxcxsoX8+9Cv0y9jWKrcTnHEPbPoYnrsx6GpEJIMp6FuzPkfCiMth4cOw7B9BVyMiGUpB39od+z+w96Ew63vwxeqgqxGRDKSgb+1iOfCdP0MiAdMnQPW2oCsSkQyjoM8E3Q6Ab98Pn70Lz/xQXSSISKMo6DNF/xNh1A2weDq8fm/Q1YhIBlHQZ5KRV8PBp8DcG3RjcRFJmYI+k0Qi3iWXXfvB45OhNOWeKESkDVPQZ5rcTjDuUe+k7IyJUF0RdEUi0sop6DNRwcFey37tQnj2ap2cFZFdUtBnqgGne8fs33pE/deLyC4p6DPZqBvgwOPg2WthzRtBVyMirZSCPpNFovDtB6BTD5g+Eco+C7oiEWmFFPSZrl0XGPcYVG6GGZMgXhV0RSLSyijow2DvQ2DsH2HNAph7fdDViEgro6APi8Jvez1dvvkAvP1Y0NWISCuioA+T0TdDn5Ew50ewYVnQ1YhIK6GgD5NoDM68H7LawcwL9GMqEQEU9OHTcV/41j2w7j14/qdBVyMirYCCPoz6nwDf/D68cS988GzQ1YhIwBT0YXXczbDPIHj6++r8TKSNU9CHVSwHznrIu67+yYshURN0RSISEAV9mO11IJz6G1j9Csz/bdDViEhAFPRhN3g8HPodmPe/sPq1oKsRkQAo6MPODE79LXTuDU9cBFs3BV2RiLQwBX1bkNsRzvo/KP8Mnpmi/utF2piUgt7MTjKzZWa2wsyua2D+UWa2yMziZnZWvXmTzGy5/5iUrsKlkXocBqNvgqXPQPGDQVcjIi1ot0FvZlHgLuBkYCAw3swG1lvsE2Ay8Fi9dbsCNwHDgWHATWbWpellyx45/DI4YLR3c/F17wddjYi0kFRa9MOAFc65lc65KmAaMDZ5AefcKufcYiBRb90Tgeedc5ucc18AzwMnpaFu2RO1NxfP6QgzvwtVW4OuSERaQCpB3wNYkzRe4k9LRUrrmtnFZlZsZsUbNmxI8aVlj+R398J+wwdey15EQq9VnIx1zt3nnCtyzhUVFBQEXU74HTgaRkzx7jX77sygqxGRZpZK0K8FeiWN9/SnpaIp60pzOvYn0Gs4zPo+fPJ60NWISDNKJejfBA4ys75mlg2MA2an+PpzgRPMrIt/EvYEf5oELZYN4/7m3W922njY+FHQFYlIM9lt0Dvn4sBleAG9FJjhnFtiZlPNbAyAmX3DzEqAs4F7zWyJv+4m4Od4O4s3gan+NGkN2neD82Z619U/ejZs2Rh0RSLSDMy1sh/PFBUVueLi4qDLaFs+WQB/HgP7DYXzn4as3KArEpFGMrOFzrmihua1ipOxErD9v+ldibNmAcz6HiTqXyUrIpksFnQB0koUnglffgIv3ASd94fjfxZ0RSKSJgp62e6IH8IXq+A/d0KX3lB0QdAViUgaKOhlOzM45TdQWgJzroZOveCg44OuSkSaSMfoZUfRGJz9EOw9EB6fDJ8uDroiEWkiBb18VU4HOPdxyO0Ej33Ha+GLSMZS0EvDOu4L5z0OleXw6HegYnPQFYnIHlLQy87tfQic8xf4fBnMOB9qqoOuSET2gIJedu2AY+G0O2HlS/CPa4OuRkT2gK66kd37+kTYuBz+8zuvlf+Ni4KuSEQaQS16Sc3om+CgE+AfP4aP5wddjYg0goJeUhOJwrcfgK79vOP1mz4OuiIRSZGCXlKX2wnGTwNXA9POhcqyoCsSkRQo6KVxuh0AZz8MG5bBk5eoAzSRDKCgl8Y74Fg48VewbA7M+1XQ1YjIbuiqG9kzwy+Bde/By7dB9wFQ+O2gKxKRnVCLXvaMGZz6W+j1TZj1A/jvW0FXJCI7oaCXPRfLgXMegbxuMO08KFsXdEUi0gAFvTRNfncY/zfY9gVMnwDxyqArEpF6FPTSdPsOgm/9CUregL9f6d1sXERaDZ2MlfQ45AxYvxT+fSvsXQiHfz/oikTEpxa9pM/R18HXToPnboRl/wi6GhHxKeglfSIROONe2Hewd3eqT14PuiIRQUEv6ZaT792dqmMP7+5U65cGXZFIm6egl/TLL4CJT3qXX/7127oVoUjAFPTSPLr0gQlPeh2fPXImbN0UdEUibVZKQW9mJ5nZMjNbYWbXNTA/x8ym+/NfN7M+/vQ+ZrbNzN72H/ekt3xp1fYp9K6x/2KVdxinakvQFYm0SbsNejOLAncBJwMDgfFmNrDeYhcCXzjnDgTuAG5NmveRc26I/7g0TXVLpuhzpNeP/dqF8Ph3dd9ZkQCk0qIfBqxwzq10zlUB04Cx9ZYZC/zZH54JjDYzS1+ZktEGjvH6xVk+F2ZP0Q+qRFpYKkHfA1iTNF7iT2twGedcHCgFuvnz+prZW2b2bzMb2dAbmNnFZlZsZsUbNmxo1AZIhii6AI65Ad55DF64OehqRNqU5v5l7KfA/s65jWZ2GDDLzA5xzm1OXsg5dx9wH0BRUZGae2F19LVQvg7+c6fXR87hPwi6IpE2IZUW/VqgV9J4T39ag8uYWQzoBGx0zlU65zYCOOcWAh8B/ZtatGQoMzjlNhgwBubeAItnBF2RSJuQStC/CRxkZn3NLBsYB8yut8xsYJI/fBbwL+ecM7MC/2QuZtYPOAhYmZ7SJSNFonDm/dBnJMz6Hqx4MeiKREJvt0HvH3O/DJgLLAVmOOeWmNlUMxvjL/Z/QDczWwFcBdRegnkUsNjM3sY7SXupc04XVLd1Wbkw7lEoGOB1bfzhc0FXJBJq5lrZFRBFRUWuuLg46DKkJZStg8fOhs/ehZN/DcP+X9AViWQsM1vonCtqaJ5+GSvB6bA3fPcf0P8kePZq+Of1kKgJuiqR0FHQS7Cy28M5f4Vvfh8W/Mk7lKNf0IqklYJegheJwkn/CyffBh/+Ex46GTZ/GnRVIqGhoJfWY/jFMH4afL4CHhgNn70XdEUioaCgl9al/4lwwT+9bhIePBGWvxB0RSIZT0Evrc++g+D/vQhd+3q9Xr75QNAViWQ0Bb20Th33g+/+Ew48Dub8CObeqCtyRPaQgl5ar5x8rz/7YZfAa3+EaefBls+Drkok4yjopXWLROGUX3s/qFrxAtw1HJbMCroqkYyioJfMMPwSuORl6NQTHp8EMyZBubq0FkmFgl4yx94D4aIXYfRPYdmz8Kfh8N4TupGJyG4o6CWzRGMw8kde675zb5h5AcyYCOXrg65MpNVS0Etm6j4ALnwejvuZ1/vlXcPg3Zlq3Ys0QEEvmSsagyOvgEvnQ9cD4IkLvb5yytYFXZlIq6Kgl8xXcDBc+Bwc/3NY/rzXul/4Z4hXBV2ZSKugoJdwiEThiClw6Ste8D8zBe4shH/fBls2Bl2dSKAU9BIuBf3hgrkw4UnY51B46Rdwx0CYfTmsXxp0dSKBiAVdgEjamcGBo73HhmVeP/fvTINFf4F+o+DwH8ABoyGido60DfqXLuFWcDCc/ju48n049ideq/7Rs7xr8IsfhKqtQVco0uxCc8/YqniCc+57jeF9uzHq4AIO692FWFT7MaknXgVLnoIFd8Gn70C7LlB4FvQ7Bvoc4Y2LZKBd3TM2NEH/aek2rpr+Dm+u2kQ84eiQG+Oo/gWMOrg7R/cvoKBDTjNUKxnLOfjkNVhwt9eHTvVWwGDfwdDvaOh7FOx/uHerQ5EM0CaCvtbmimr+s/xzXlq2npeWbWBDWSUAg3t24piDuzPqa90Z1KMTkYilq2TJdPEqWFsMH7/sPda8AYlqiGRBzyIv9Pse7Q3H1GCQ1qlNBX2yRMLx/qebmbdsPf/6YD1vrfkS56Bb+2yOPGgv9umYS4fcGB1ys8jPidEhN0Z+boyO9cZzYtG01CMZomoLfLJge/B/+ja4BMTaQZ8jvbtgHXQCdOkddKUiddps0Ne3aUsV85dv4KUP1rNg5SY2ba2iKp7Y7XrZsQjts6PkZcfIy46SlxMjLytK+5ykadkx2udEaZcdJS/LG8+tG456w9lR8rJi5GZHyMuO0S4rSlTfLFq/bV/C6ldh5TxY8TxsWulNL/iaF/j9T4RewyGaFWiZ0rYp6HehMl5DeUWc8so4ZRVxNldUU17hDXvTqimriLO1qsZ/xNlSVcO2qjhbKmvYVl3Dlso426pq2FIVJ9HIP2d2LEJedpR2Wd5Ool3tjiFr+3C7uh1JjPbZUdrnxMjP8XYw+Tn+9Jyo/+zthHQiuhl9vgKWz4UP53o7gEQ15HbyLtnsf6J3V6z2ewVdpbQxCvoW4pyjMp5gW1UNW6u9ncHWqpqkcW9nsS15nj+9dpmKHZbxnrfW7liqUr+VXlbU6nYWtTuQ3NodR5b3DaNd1o47lrzs2h1LbPsOJqt2R+NNr10nNyuCmb6NULHZa+kvn+t1v1C+DjDveP6+Q6DbAdC1n9cXT+f9IZYddMWyM4lEy/22omIzbF4LpSXeo3Y4vzscP3WPXnJXQZ/SD6bM7CTgd0AUeMA5d0u9+TnAX4DDgI3AOc65Vf6864ELgRpginNu7h5tRQYw88I1NytKc1ykl0g4tlbXsLXS+7axtaqG8so4Wyq9ncAWf7h2B7GtuoaKuh3F9vHNFdV1O5faHUllCoew6ttxJ7L9cFRudpTcWIScLO85NytKTr3n3KwIObEoOTs8+8OxyPb5/rTsmDe/1Z1Ez+0IA8d4j0QCPnvH601zxQuweDpUbt6+rEWhcy8v9Lv2S9oJ9IP2BZDTMTw/4nIO4hXeI1EDNdWQiPuPGv+5ut54vIFlaofrLVOrrrFhSeNJwy4BleXe51CxGSpL/efNSc/+tJpKiOZ4t7DM9h85+d6VV9n5kNNh+3BWnv82BhbZ/r4NPVdvhdK1OwZ68r8L8P5tdNgXeo9olrD1yDwAAAdVSURBVI9jty16M4sCHwLHAyXAm8B459z7Sct8HxjknLvUzMYBZzjnzjGzgcDfgGHAfsALQH/n3E6bppncos9kNQnnh3683jeJ7YesKuq+ZSTqvpV4zwlvXu361Qkqq72dR4W/c6kdbuyhrfpiESMrGiE75j+i3g6g/rSsWITsqBGLeMNZESMW9db1HkasdjhiRKNGViRCzJ8ei1jde8X814n5y0XNiEaMiHmvGfHHo2ZEImwfNsiq/IKczR+TVbqKrNKPyfryY6Jffkzsi5VYVdkO2+Ys4l3Hn9sF8rpCuy5YXldo5w2T1wVyO0Mk5oVLJOqHTMQLCrN60yJe0LmEF7wNDeMPJ2r8aTXeDqtuuKbesIP4NqgsazgwK8u2T0tUN+3DTres9t6OObeTt1PN7bjjc1aev23lUFXunZSvLEsarp1evuPOJhV53by7o3XsCZ16+MM9oFMvbzx/H6831iZoaot+GLDCObfSf7FpwFjg/aRlxgI3+8MzgT+a971+LDDNOVcJfGxmK/zXe21PNkSaTzRi5PvH/puLc47qGkdlvIaK6oS/A/B2ApXxBJXVCapqtu8kvEcNldXbh6viCaprElTF/WXjia9Mq4on2LqthniNNz1e46jyn6trpyVqh1vq0OVe/uMbtX8NulJGH/uM3raOrlZGZyunc3U5XcrK6UwZnW29N41y8q2ihepMTQ0RttKOLZbHFvK8Z2tPOb3YYnlsjeZRHs2j0nKoIeo/IsSJUWORumlx/zlBhLjFqCFCwmJ18x0Rb3mrXc5b1lHXbgdc3fP29v32z3WrtWMLeSTMu3rOKoAKoJTGH37MBrIcERfH6t4vgdW9p8Oc22G8iiyqLAfK8B5rk19wG7DCf8CAfTvwp/MOa1xNKUjlf3UPYE3SeAkwfGfLOOfiZlYKdPOnL6i3bo/6b2BmFwMXA+y///6p1i4ZxszIjhnZsQgdcoOuxuOcoybh6oI/XuOoTiS8af6OIe4PxxPecCLhrVPjr1uTcCScoyZB0vD255qEwznqlk84/zUc/rM3P+Ecm4EvEw6HN55wgHNYTRU58VKy42WQSGDOa2m7hN/adgmstuXtHOZqcC5Bgmht/PgBaTgiJPDiqXaa9xwhYUnDREiY/+yMhHnTqy2byki7pMMmO/vb7naRrzDnHR+OkhTf/lGHuujeMeV3XL+Bad2Sa2JnI+Bw2M5eeDdvVn+t+juQVF+1T7e81N+/EVpFp2bOufuA+8A7dBNwOdKGmH/4JRaF3Cz9XkLCKZUzP2uBXknjPan35SN5GTOLAZ3wTsqmsq6IiDSjVIL+TeAgM+trZtnAOGB2vWVmA5P84bOAfznv+9ZsYJyZ5ZhZX+Ag4I30lC4iIqnY7aEb/5j7ZcBcvENnDzrnlpjZVKDYOTcb+D/gEf9k6ya8nQH+cjPwTtzGgR/s6oobERFJP/1gSkQkBHZ1eWVIfp0hIiI7o6AXEQk5Bb2ISMgp6EVEQq7VnYw1sw3A6ia8xF7A52kqpzXRdmWesG6btqt16u2cK2hoRqsL+qYys+KdnXnOZNquzBPWbdN2ZR4duhERCTkFvYhIyIUx6O8LuoBmou3KPGHdNm1XhgndMXoREdlRGFv0IiKSREEvIhJyoQl6MzvJzJaZ2Qozuy7oetLJzFaZ2btm9raZZWyPb2b2oJmtN7P3kqZ1NbPnzWy5/9wc91VvVjvZrpvNbK3/mb1tZqcEWeOeMrNeZvaSmb1vZkvM7If+9Iz+3HaxXaH43OoLxTH6VG5gnsnMbBVQ5JzL5B9zYGZHAeXAX5xzhf60XwObnHO3+DvoLs65HwdZZ2PtZLtuBsqdc78JsramMrN9gX2dc4vMrAOwEPgWMJkM/tx2sV3fIQSfW31hadHX3cDcOVcF1N7AXFoR59zLePcrSDYW+LM//Ge8/2wZZSfbFQrOuU+dc4v84TJgKd59nzP6c9vFdoVSWIK+oRuYh+lDc8BzZrbQv5F6mOztnPvUH/4M2DvIYtLsMjNb7B/ayahDGw0xsz7AUOB1QvS51dsuCNnnBuEJ+rA70jn3deBk4Af+oYLQ8W8/mfnHEj13AwcAQ4BPgd8GW07TmFk+8ARwhXNuc/K8TP7cGtiuUH1utcIS9KG+Cblzbq3/vB54Cu9QVVis84+X1h43XR9wPWnhnFvnnKtxziWA+8ngz8zMsvDC8FHn3JP+5Iz/3BrarjB9bsnCEvSp3MA8I5lZe/9kEWbWHjgBeG/Xa2WU5BvLTwKeDrCWtKkNQd8ZZOhnZmaGd0/opc6525NmZfTntrPtCsvnVl8orroB8C+DupPtNzD/ZcAlpYWZ9cNrxYN3M/fHMnXbzOxvwDF43cGuA24CZgEzgP3xuqf+jnMuo05s7mS7jsH7+u+AVcAlSce0M4aZHQnMB94FEv7kG/COZ2fs57aL7RpPCD63+kIT9CIi0rCwHLoREZGdUNCLiIScgl5EJOQU9CIiIaegFxEJOQW9iEjIKehFRELu/wMnRCtTZWhM/gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qRjzE5x7eQMr",
        "outputId": "59947d73-aa5c-45fc-c50f-e049b91f5eda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        }
      },
      "source": [
        "# 예측 실시\n",
        "tr_pred = model.predict(X_train)\n",
        "ts_pred = model.predict(X_test)\n",
        "\n",
        "ts_pred[:12]"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.4801558 ],\n",
              "       [0.46715194],\n",
              "       [0.5082551 ],\n",
              "       [0.5290622 ],\n",
              "       [0.56574196],\n",
              "       [0.6018218 ],\n",
              "       [0.61329234],\n",
              "       [0.6261123 ],\n",
              "       [0.59170187],\n",
              "       [0.6014843 ],\n",
              "       [0.6180155 ],\n",
              "       [0.6914711 ]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XnflOHKUeZh2",
        "outputId": "0dabd27e-34e9-4522-bf65-7f8fe66cd756",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        }
      },
      "source": [
        "# 0~1로 정규화했던 것을 원래의 값으로 환원\n",
        "tr_pred = scaler.inverse_transform(tr_pred)\n",
        "y_train = scaler.inverse_transform([y_train])\n",
        "\n",
        "ts_pred = scaler.inverse_transform(ts_pred)\n",
        "y_test = scaler.inverse_transform([y_test])\n",
        "\n",
        "ts_pred[:12]"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[626.9427 ],\n",
              "       [622.38354],\n",
              "       [636.79425],\n",
              "       [644.08923],\n",
              "       [656.94916],\n",
              "       [669.59875],\n",
              "       [673.6203 ],\n",
              "       [678.115  ],\n",
              "       [666.0507 ],\n",
              "       [669.4804 ],\n",
              "       [675.2763 ],\n",
              "       [701.0298 ]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zc0V4Roseazi",
        "outputId": "079633e7-ff88-4541-8602-dff98538619b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "# 예측값에 대한 평균제곱오차 계산\n",
        "tr_score = math.sqrt(mean_squared_error(y_train[0], tr_pred[:,0]))\n",
        "print('Train Score: %.2f RMSE' % (tr_score))\n",
        "\n",
        "ts_core = math.sqrt(mean_squared_error(y_test[0], ts_pred[:,0]))\n",
        "print('Test Score: %.2f RMSE' % (ts_core))"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Score: 9.03 RMSE\n",
            "Test Score: 24.01 RMSE\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "91kvP9BBe-iK"
      },
      "source": [
        "# 시각화를 위해 훈련 예측데이터를 재조정\n",
        "tr_pred_data = np.empty_like(dataset)\n",
        "tr_pred_data[:, :] = np.nan\n",
        "tr_pred_data[shift:len(tr_pred)+shift, :] = tr_pred"
      ],
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "obcVU0iGfHId"
      },
      "source": [
        "# 시각화를 위해 테스트 예측데이터를 재조정\n",
        "ts_pred_data = np.empty_like(dataset)\n",
        "ts_pred_data[:, :] = np.nan\n",
        "ts_pred_data[len(tr_pred)+(shift*2)+1:len(dataset)-1, :] = ts_pred"
      ],
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kyo1BiP7fIMT",
        "outputId": "f6a26c48-2084-423b-9614-eeaa78ca2fd5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "# 예측 결과 시각화\n",
        "plt.plot(scaler.inverse_transform(dataset))\n",
        "\n",
        "plt.plot(tr_pred_data)\n",
        "plt.plot(ts_pred_data)"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fb23aa8b0b8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}