pyFTSex/tutorial/pyFTS/developer/GOOGLE COLAB Chen - ConventionalFTS.ipynb

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  "metadata": {
    "colab": {
      "name": "Chen%20-%20ConventionalFTS.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/petroniocandido/pyFTS/blob/master/pyFTS/notebooks/GOOGLE%20COLAB%20Chen%20-%20ConventionalFTS.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "q_6DGrIna0wP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# First Order Weighted Fuzzy Time Series by Yu(2005)\n",
        "\n",
        "H.-K. Yu, “Weighted fuzzy time series models for TAIEX forecasting,” \n",
        "Phys. A Stat. Mech. its Appl., vol. 349, no. 3, pp. 609–624, 2005."
      ]
    },
    {
      "metadata": {
        "id": "MwYpk5PVa0wR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Environment Setup"
      ]
    },
    {
      "metadata": {
        "id": "FSeYkqdJa0wT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Library install/update"
      ]
    },
    {
      "metadata": {
        "id": "1Dk0hhPia0wW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "outputId": "e0b16387-58cd-4c72-8244-22511478ea33"
      },
      "cell_type": "code",
      "source": [
        "!pip3 install -U git+https://github.com/petroniocandido/pyFTS\n",
        "!pip3 install dill"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/petroniocandido/pyFTS\r\n",
            "  Cloning https://github.com/petroniocandido/pyFTS to /tmp/pip-req-build-2nfx68ta\n",
            "Building wheels for collected packages: pyFTS\n",
            "  Running setup.py bdist_wheel for pyFTS ... \u001b[?25l-\b \b\\\b \bdone\n",
            "\u001b[?25h  Stored in directory: /tmp/pip-ephem-wheel-cache-ts2ity0t/wheels/84/d7/1e/a333c7128f25b347640740859808db094c4478e98663cd2297\n",
            "Successfully built pyFTS\n",
            "Installing collected packages: pyFTS\n",
            "  Found existing installation: pyFTS 1.2.2\n",
            "    Uninstalling pyFTS-1.2.2:\n",
            "      Successfully uninstalled pyFTS-1.2.2\n",
            "Successfully installed pyFTS-1.2.2\n",
            "Collecting dill\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/91/a0/19d4d31dee064fc553ae01263b5c55e7fb93daff03a69debbedee647c5a0/dill-0.2.7.1.tar.gz (64kB)\n",
            "\u001b[K    100% |████████████████████████████████| 71kB 2.4MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: dill\n",
            "  Running setup.py bdist_wheel for dill ... \u001b[?25l-\b \bdone\n",
            "\u001b[?25h  Stored in directory: /content/.cache/pip/wheels/99/c4/ed/1b64d2d5809e60d5a3685530432f6159d6a9959739facb61f2\n",
            "Successfully built dill\n",
            "Installing collected packages: dill\n",
            "Successfully installed dill-0.2.7.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "zWHClZPOa0we",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### External libraries import"
      ]
    },
    {
      "metadata": {
        "id": "CYbYGUaIa0wg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6682aa12-84fd-4918-edb8-dffcac14b8f9"
      },
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pylab as plt\n",
        "import seaborn as sns\n",
        "\n",
        "%pylab inline"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "iw-8cPBfa0wr",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Common pyFTS imports "
      ]
    },
    {
      "metadata": {
        "id": "jFP_AhMAa0ws",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "ceae202d-0dba-4bec-c6c5-34e35244906d"
      },
      "cell_type": "code",
      "source": [
        "from pyFTS.common import Util as cUtil\n",
        "from pyFTS.benchmarks import benchmarks as bchmk, Util as bUtil\n",
        "from pyFTS.partitioners import Util as pUtil\n",
        "\n",
        "from pyFTS.models import yu\n"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wkxZ9snZa0wy",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Common data transformations"
      ]
    },
    {
      "metadata": {
        "id": "9bQdnNG9a0w0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "3a6fb8b0-dab9-490c-99e7-928fbc2b6d64"
      },
      "cell_type": "code",
      "source": [
        "from pyFTS.common import Transformations\n",
        "\n",
        "tdiff = Transformations.Differential(1)\n",
        "\n",
        "boxcox = Transformations.BoxCox(0)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oXa3wbI3a0w8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Datasets"
      ]
    },
    {
      "metadata": {
        "id": "Yu55rAZCa0w-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Data Loading"
      ]
    },
    {
      "metadata": {
        "id": "wY7sCz4ba0xA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "56376909-1f95-4899-91b9-b3574410e93a"
      },
      "cell_type": "code",
      "source": [
        "from pyFTS.data import TAIEX, NASDAQ, SP500\n",
        "\n",
        "dataset_names = [\"TAIEX\", \"SP500\",\"NASDAQ\"]\n",
        "\n",
        "def get_dataset(name):\n",
        "    if dataset_name == \"TAIEX\":\n",
        "        return TAIEX.get_data()\n",
        "    elif dataset_name == \"SP500\":\n",
        "        return SP500.get_data()[11500:16000]\n",
        "    elif dataset_name == \"NASDAQ\":\n",
        "        return NASDAQ.get_data()\n",
        "\n",
        "\n",
        "train_split = 2000\n",
        "test_length = 200"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6DMPncjTa0xF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Visualization"
      ]
    },
    {
      "metadata": {
        "id": "TVQoxoL5a0xH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "outputId": "b57b45fb-6694-452f-c4f4-e28632bf8780"
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(nrows=2, ncols=3, figsize=[10,5])\n",
        "\n",
        "for count,dataset_name in enumerate(dataset_names):\n",
        "    dataset = get_dataset(dataset_name)\n",
        "    dataset_diff = tdiff.apply(dataset)\n",
        "\n",
        "    ax[0][count].plot(dataset)\n",
        "    ax[1][count].plot(dataset_diff)\n",
        "    ax[0][count].set_title(dataset_name)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd490230908>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "5H0st-qfa0xR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Statistics"
      ]
    },
    {
      "metadata": {
        "id": "74fIGOQaa0xT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "1b268850-71d7-4547-9efa-c44f79ebafdb"
      },
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.stattools import adfuller\n",
        "\n",
        "rows =[]\n",
        "\n",
        "for count,dataset_name in enumerate(dataset_names):\n",
        "    row = [dataset_name]\n",
        "    dataset = get_dataset(dataset_name)\n",
        "    result = adfuller(dataset)\n",
        "    row.extend([result[0],result[1]])\n",
        "    row.extend([value for key, value in result[4].items()])\n",
        "    rows.append(row)\n",
        "    \n",
        "pd.DataFrame(rows,columns=['Dataset','ADF Statistic','p-value','Cr. Val. 1%','Cr. Val. 5%','Cr. Val. 10%'])\n"
      ],
      "execution_count": 10,
      "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>Dataset</th>\n",
              "      <th>ADF Statistic</th>\n",
              "      <th>p-value</th>\n",
              "      <th>Cr. Val. 1%</th>\n",
              "      <th>Cr. Val. 5%</th>\n",
              "      <th>Cr. Val. 10%</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TAIEX</td>\n",
              "      <td>-2.656728</td>\n",
              "      <td>0.081830</td>\n",
              "      <td>-3.431601</td>\n",
              "      <td>-2.862093</td>\n",
              "      <td>-2.567064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SP500</td>\n",
              "      <td>-1.747171</td>\n",
              "      <td>0.406987</td>\n",
              "      <td>-3.431811</td>\n",
              "      <td>-2.862186</td>\n",
              "      <td>-2.567114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NASDAQ</td>\n",
              "      <td>0.476224</td>\n",
              "      <td>0.984132</td>\n",
              "      <td>-3.432022</td>\n",
              "      <td>-2.862279</td>\n",
              "      <td>-2.567163</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Dataset  ADF Statistic   p-value  Cr. Val. 1%  Cr. Val. 5%  Cr. Val. 10%\n",
              "0   TAIEX      -2.656728  0.081830    -3.431601    -2.862093     -2.567064\n",
              "1   SP500      -1.747171  0.406987    -3.431811    -2.862186     -2.567114\n",
              "2  NASDAQ       0.476224  0.984132    -3.432022    -2.862279     -2.567163"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "id": "i-bNzLfta0xd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Partitioning\n",
        "\n",
        "The best number of partitions of the Universe of Discourse is an optimization problem. The know more about partitioning schemes please look on the Partitioners notebook. To know more about benchmarking look on the Benchmarks notebook."
      ]
    },
    {
      "metadata": {
        "id": "PwgWxxNCa0xg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "d022f8f5-b1b1-4cef-d66d-e773cb7997ee"
      },
      "cell_type": "code",
      "source": [
        "from pyFTS.partitioners import Grid, Util as pUtil\n",
        "from pyFTS.benchmarks import benchmarks as bchmk\n",
        "from pyFTS.models import chen\n",
        "\n",
        "tag = 'chen_partitioning'\n",
        "_type = 'point'\n",
        "\n",
        "for dataset_name in dataset_names:\n",
        "    dataset = get_dataset(dataset_name)\n",
        "\n",
        "    bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2,\n",
        "                                    methods=[chen.ConventionalFTS],\n",
        "                                    benchmark_models=False,\n",
        "                                    transformations=[None],\n",
        "                                    partitions=np.arange(10,100,2), \n",
        "                                    progress=False, type=_type,\n",
        "                                    file=\"benchmarks.db\", dataset=dataset_name, tag=tag)\n",
        "\n",
        "    bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2,\n",
        "                                    methods=[chen.ConventionalFTS],\n",
        "                                    benchmark_models=False,\n",
        "                                    transformations=[tdiff],\n",
        "                                    partitions=np.arange(3,30,1), \n",
        "                                    progress=False, type=_type,\n",
        "                                    file=\"benchmarks.db\", dataset=dataset_name, tag=tag)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "g7_VBSt5a0xl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "outputId": "4f94ca3b-91cb-4caf-de47-d74c29fb795c"
      },
      "cell_type": "code",
      "source": [
        "from pyFTS.benchmarks import Util as bUtil\n",
        "\n",
        "df1 = bUtil.get_dataframe_from_bd(\"benchmarks.db\",\n",
        "                                  \"tag = 'chen_partitioning' and measure = 'rmse'and transformation is null\")\n",
        "\n",
        "df2 = bUtil.get_dataframe_from_bd(\"benchmarks.db\",\n",
        "                                  \"tag = 'chen_partitioning' and measure = 'rmse' and transformation is not null\")\n",
        "\n",
        "fig, ax = plt.subplots(nrows=2, ncols=1, figsize=[15,7])\n",
        "\n",
        "g1 = sns.boxplot(x='Partitions', y='Value', hue='Dataset', data=df1, showfliers=False, ax=ax[0], \n",
        "                 palette=\"Set3\")\n",
        "box = g1.get_position()\n",
        "g1.set_position([box.x0, box.y0, box.width * 0.85, box.height]) \n",
        "g1.legend(loc='right', bbox_to_anchor=(1.15, 0.5), ncol=1)\n",
        "ax[0].set_title(\"Original data\")\n",
        "ax[0].set_ylabel(\"RMSE\")\n",
        "ax[0].set_xlabel(\"\")\n",
        "\n",
        "g2 = sns.boxplot(x='Partitions', y='Value', hue='Dataset', data=df2, showfliers=False, ax=ax[1], \n",
        "                 palette=\"Set3\")\n",
        "box = g2.get_position()\n",
        "g2.set_position([box.x0, box.y0, box.width * 0.85, box.height]) \n",
        "g2.legend(loc='right', bbox_to_anchor=(1.15, 0.5), ncol=1)\n",
        "ax[1].set_title(\"Differentiated data\")\n",
        "ax[1].set_ylabel(\"RMSE\")\n",
        "ax[1].set_xlabel(\"Number of partitions of the UoD\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5,0,'Number of partitions of the UoD')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2EAAAG4CAYAAADMou/xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCe7++zZ+tkhKw
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd4900b7d68>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "JlF5RWvKa0xs",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Comparing the partitioning schemas"
      ]
    },
    {
      "metadata": {
        "id": "WqxZA04Za0xt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "outputId": "8a210c60-84f8-425e-8597-b9b8cd7d0dc4"
      },
      "cell_type": "code",
      "source": [
        "from pyFTS.partitioners import Grid, Util as pUtil\n",
        "\n",
        "fig, ax = plt.subplots(nrows=2, ncols=3, figsize=[20,5])\n",
        "\n",
        "\n",
        "partitioners = {}\n",
        "partitioners_diff = {}\n",
        "\n",
        "for count,dataset_name in enumerate(dataset_names):\n",
        "    dataset = get_dataset(dataset_name)\n",
        "\n",
        "    partitioner = Grid.GridPartitioner(data=dataset, npart=30)\n",
        "    partitioners[dataset_name] = partitioner\n",
        "    partitioner_diff = Grid.GridPartitioner(data=dataset, npart=10, transformation=tdiff)\n",
        "    partitioners_diff[dataset_name] = partitioner_diff\n",
        "\n",
        "    pUtil.plot_sets(dataset, [partitioner.sets], titles=[dataset_name], axis=ax[0][count])\n",
        "    pUtil.plot_sets(dataset, [partitioner_diff.sets], titles=[''], axis=ax[1][count])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABH8AAAFJCAYAAAACMIGVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nl47rBX2EcagcQ
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd490227b38>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "k99jlRB5a0x0",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Fitting models"
      ]
    },
    {
      "metadata": {
        "id": "eCsrFI8Ma0x1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### With original data"
      ]
    },
    {
      "metadata": {
        "id": "ojs17_Fka0x2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "b4f3614b-c18f-4c2d-e1af-e124801ff0ff"
      },
      "cell_type": "code",
      "source": [
        "for count,dataset_name in enumerate(dataset_names):\n",
        "    dataset = get_dataset(dataset_name)\n",
        "\n",
        "    model1 = yu.WeightedFTS(partitioner=partitioners[dataset_name])\n",
        "    model1.name=dataset_name\n",
        "    model1.fit(dataset[:train_split], save_model=True, file_path='model1'+dataset_name, order=1)\n"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "metadata": {
        "id": "16bVx9F1a0x6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### With transformed data"
      ]
    },
    {
      "metadata": {
        "id": "k5JA2gOUa0x6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "98f3e3e2-30cf-453e-848b-6da13f07587c"
      },
      "cell_type": "code",
      "source": [
        "for count,dataset_name in enumerate(dataset_names):\n",
        "    dataset = get_dataset(dataset_name)\n",
        "\n",
        "    model2 = yu.WeightedFTS(partitioner=partitioners_diff[dataset_name])\n",
        "    model2.name=dataset_name\n",
        "    model2.append_transformation(tdiff)\n",
        "    model2.fit(dataset[:train_split], save_model=True, file_path='model2'+dataset_name, order=1)\n"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Dk_45XTda0yB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Predicting with the models"
      ]
    },
    {
      "metadata": {
        "id": "5DDHqD9ta0yE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 749
        },
        "outputId": "ce7ae9dc-8163-4f36-fc34-bdd88c9980d0"
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(nrows=3, ncols=1, figsize=[20,10])\n",
        "\n",
        "\n",
        "for count,dataset_name in enumerate(dataset_names):\n",
        "    dataset = get_dataset(dataset_name)\n",
        "    \n",
        "    ax[count].plot(dataset[train_split:train_split+200])\n",
        "\n",
        "    model1 = cUtil.load_obj('model1'+dataset_name)\n",
        "\n",
        "    forecasts = model1.predict(dataset[train_split:train_split+200])\n",
        "    \n",
        "    ax[count].plot(forecasts)\n",
        "    \n",
        "    ax[count].set_title(dataset_name)\n",
        "    \n",
        "plt.tight_layout()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABZgAAALICAYAAADyhJW9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9dDU6qGpzUNTi
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd48c948c88>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "H0j4d4SRhnO7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zYiVnuHva0yS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "6ab265ec-ed90-489b-c18a-b7f2f9a6baa9"
      },
      "cell_type": "code",
      "source": [
        "from pyFTS.benchmarks import Measures\n",
        "\n",
        "rows = []\n",
        "\n",
        "for count,dataset_name in enumerate(dataset_names):\n",
        "    row = [dataset_name]\n",
        "    \n",
        "    dataset = get_dataset(dataset_name)\n",
        "    \n",
        "    test = dataset[train_split:train_split+200]\n",
        "\n",
        "    model1 = cUtil.load_obj('model1'+dataset_name)\n",
        "    \n",
        "    row.extend(Measures.get_point_statistics(test, model1))\n",
        "    \n",
        "    rows.append(row)\n",
        "    \n",
        "    \n",
        "pd.DataFrame(rows,columns=[\"Dataset\",\"RMSE\",\"SMAPE\",\"Theil's U\"])\n",
        "    \n",
        "    \n",
        "    "
      ],
      "execution_count": 17,
      "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>Dataset</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>SMAPE</th>\n",
              "      <th>Theil's U</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TAIEX</td>\n",
              "      <td>92.76</td>\n",
              "      <td>0.79</td>\n",
              "      <td>1.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SP500</td>\n",
              "      <td>16.01</td>\n",
              "      <td>0.63</td>\n",
              "      <td>2.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NASDAQ</td>\n",
              "      <td>48.70</td>\n",
              "      <td>1.19</td>\n",
              "      <td>2.03</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Dataset   RMSE  SMAPE  Theil's U\n",
              "0   TAIEX  92.76   0.79       1.41\n",
              "1   SP500  16.01   0.63       2.88\n",
              "2  NASDAQ  48.70   1.19       2.03"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "1WHz2HSca0yc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 749
        },
        "outputId": "433f3d6d-de7e-4598-d280-a2300f5ad511"
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(nrows=3, ncols=1, figsize=[20,10])\n",
        "\n",
        "\n",
        "for count,dataset_name in enumerate(dataset_names):\n",
        "    dataset = get_dataset(dataset_name)\n",
        "    \n",
        "    ax[count].plot(dataset[train_split:train_split+200])\n",
        "\n",
        "    model1 = cUtil.load_obj('model2'+dataset_name)\n",
        "\n",
        "    forecasts = model1.predict(dataset[train_split:train_split+200])\n",
        "    \n",
        "    ax[count].plot(forecasts)\n",
        "    \n",
        "    ax[count].set_title(dataset_name)\n",
        "    \n",
        "plt.tight_layout()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABZgAAALICAYAAADyhJW9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDGaRmcCeil7CF
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd48af924a8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "HTkB2pt8a0ym",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "c8a2a240-7297-41ce-fb89-c9fee7bafce2"
      },
      "cell_type": "code",
      "source": [
        "from pyFTS.benchmarks import Measures\n",
        "\n",
        "rows = []\n",
        "\n",
        "for count,dataset_name in enumerate(dataset_names):\n",
        "    row = [dataset_name]\n",
        "    \n",
        "    dataset = get_dataset(dataset_name)\n",
        "    \n",
        "    test = dataset[train_split:train_split+200]\n",
        "\n",
        "    model1 = cUtil.load_obj('model2'+dataset_name)\n",
        "    \n",
        "    row.extend(Measures.get_point_statistics(test, model1))\n",
        "    \n",
        "    rows.append(row)\n",
        "    \n",
        "    \n",
        "pd.DataFrame(rows,columns=[\"Dataset\",\"RMSE\",\"SMAPE\",\"Theil's U\"])\n",
        "    \n",
        "    \n",
        "    "
      ],
      "execution_count": 19,
      "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>Dataset</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>SMAPE</th>\n",
              "      <th>Theil's U</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TAIEX</td>\n",
              "      <td>71.41</td>\n",
              "      <td>0.61</td>\n",
              "      <td>1.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SP500</td>\n",
              "      <td>6.93</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NASDAQ</td>\n",
              "      <td>24.51</td>\n",
              "      <td>0.60</td>\n",
              "      <td>1.02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Dataset   RMSE  SMAPE  Theil's U\n",
              "0   TAIEX  71.41   0.61       1.09\n",
              "1   SP500   6.93   0.25       1.25\n",
              "2  NASDAQ  24.51   0.60       1.02"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "id": "IfqcqhGEa0yx",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Residual Analysis"
      ]
    },
    {
      "metadata": {
        "id": "ABpKELU3a0yz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rp_lHXPua0y6",
        "colab_type": "code",
        "colab": {},
        "outputId": "0e831fcc-36c3-4183-a734-f52dde9c96a6"
      },
      "cell_type": "code",
      "source": [
        "from pyFTS.benchmarks import ResidualAnalysis as ra\n",
        "\n",
        "for count,dataset_name in enumerate(dataset_names):\n",
        "    dataset = get_dataset(dataset_name)\n",
        "    \n",
        "    model1 = cUtil.load_obj('model1'+dataset_name)\n",
        "    model1 = cUtil.load_obj('model2'+dataset_name)\n",
        "\n",
        "    ra.plot_residuals(dataset, [model1, model2])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f1145edefd0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAI4CAYAAABndZP2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nc851oyR9cCTdD
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f11461d7dd8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f1145991940>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "9xKhBiuQa0y-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "c9NCUKoQa0zB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}