1007 lines
726 KiB
Plaintext
1007 lines
726 KiB
Plaintext
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.3"
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"toc": {
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": true
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},
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"varInspector": {
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"lenName": 16,
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"kernels_config": {
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"python": {
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"delete_cmd_postfix": "",
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"delete_cmd_prefix": "del ",
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"library": "var_list.py",
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"varRefreshCmd": "print(var_dic_list())"
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},
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"r": {
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"delete_cmd_postfix": ") ",
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"delete_cmd_prefix": "rm(",
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"library": "var_list.r",
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"varRefreshCmd": "cat(var_dic_list()) "
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}
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},
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"types_to_exclude": [
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"module",
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"function",
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"builtin_function_or_method",
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"instance",
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"_Feature"
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],
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"window_display": false
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},
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"colab": {
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"name": "Song - ConventionalFTS.ipynb",
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"provenance": [],
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"include_colab_link": true
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/PYFTS/notebooks/blob/master/Song%20-%20ConventionalFTS.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "vJzk53mqEAW0",
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"colab_type": "text"
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},
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"source": [
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"# First Order Traditional Fuzzy Time Series method by Song & Chissom (1993)\n",
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"\n",
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"Q. Song and B. S. Chissom, “Fuzzy time series and its models,” Fuzzy Sets Syst., vol. 54, no. 3, pp. 269–277, 1993."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "n5_F4UtyEAW4",
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"colab_type": "text"
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},
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"source": [
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"## Environment Setup"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "MvFGutscEAW7",
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"colab_type": "text"
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},
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"source": [
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"### Library install/update"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "-wFbyFjmEAW9",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 211
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},
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"outputId": "4d0351bf-2ffe-4419-dc34-e1c14f3289f8"
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},
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"source": [
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"!pip3 install -U git+https://github.com/PYFTS/pyFTS"
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],
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"execution_count": 1,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Collecting git+https://github.com/PYFTS/pyFTS\n",
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" Cloning https://github.com/PYFTS/pyFTS to /tmp/pip-req-build-v3ka3owc\n",
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" Running command git clone -q https://github.com/PYFTS/pyFTS /tmp/pip-req-build-v3ka3owc\n",
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"Building wheels for collected packages: pyFTS\n",
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" Building wheel for pyFTS (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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" Created wheel for pyFTS: filename=pyFTS-1.6-cp36-none-any.whl size=197025 sha256=05585985a4d79ef4ef3ae92b46edf4e7fbaa0cfacc1bc1c35f6e9fe1a69b8628\n",
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" Stored in directory: /tmp/pip-ephem-wheel-cache-de1vqkdf/wheels/e7/32/a9/230470113df5a73242a5a6d05671cb646db97abf14bbce2644\n",
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"Successfully built pyFTS\n",
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"Installing collected packages: pyFTS\n",
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"Successfully installed pyFTS-1.6\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "O7sY9wGiEAXG",
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"colab_type": "text"
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},
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"source": [
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"### External libraries import"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "FjTS_QmgEAXJ",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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},
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"outputId": "998f7b83-5eb2-440f-ba75-c303c6cd4554"
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},
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"source": [
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"import warnings\n",
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"warnings.filterwarnings('ignore')\n",
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"\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pylab as plt\n",
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"import seaborn as sns\n",
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"\n",
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"%pylab inline"
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],
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"execution_count": 2,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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|
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"Populating the interactive namespace from numpy and matplotlib\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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|||
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"id": "FACM8bWjEAXQ",
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"colab_type": "text"
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},
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"source": [
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|||
|
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"### Common pyFTS imports "
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]
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},
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{
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"cell_type": "code",
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|||
|
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"metadata": {
|
|||
|
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"id": "XFcgU2PmEAXS",
|
|||
|
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"colab_type": "code",
|
|||
|
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"colab": {}
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|||
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},
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|||
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"source": [
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|||
|
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"from pyFTS.common import Util as cUtil\n",
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|||
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"from pyFTS.benchmarks import benchmarks as bchmk, Util as bUtil\n",
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"from pyFTS.partitioners import Util as pUtil\n",
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"\n",
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|||
|
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"from pyFTS.models import song\n"
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],
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|||
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"execution_count": 0,
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|||
|
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"outputs": []
|
|||
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},
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|||
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{
|
|||
|
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"cell_type": "markdown",
|
|||
|
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"metadata": {
|
|||
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"id": "xLiqnuylEAXX",
|
|||
|
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"colab_type": "text"
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|||
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},
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|||
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"source": [
|
|||
|
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"## Common data transformations"
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|||
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]
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|||
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},
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|||
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{
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|||
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"cell_type": "code",
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|||
|
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"metadata": {
|
|||
|
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"id": "GYhNkIGxEAXY",
|
|||
|
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"colab_type": "code",
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|||
|
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"colab": {}
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|||
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},
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|||
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"source": [
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|||
|
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"from pyFTS.common import Transformations\n",
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"\n",
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"tdiff = Transformations.Differential(1)\n",
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"\n",
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"boxcox = Transformations.BoxCox(0)"
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],
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|||
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"execution_count": 0,
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|||
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"outputs": []
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},
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{
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|||
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"cell_type": "markdown",
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|||
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"metadata": {
|
|||
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"id": "Jnx1bOjoEAXe",
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|||
|
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"colab_type": "text"
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},
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|||
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"source": [
|
|||
|
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"## Datasets"
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|||
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]
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|||
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},
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|||
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{
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|||
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"cell_type": "markdown",
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|||
|
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"metadata": {
|
|||
|
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"id": "jxCuWP20EAXg",
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|||
|
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"colab_type": "text"
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},
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|||
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"source": [
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|||
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"### Data Loading"
|
|||
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]
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|||
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},
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|||
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{
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|||
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"cell_type": "code",
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|||
|
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"metadata": {
|
|||
|
|
"id": "2elozp_mEAXi",
|
|||
|
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"colab_type": "code",
|
|||
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"colab": {}
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|||
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},
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|||
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"source": [
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|||
|
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"from pyFTS.data import TAIEX, NASDAQ, SP500\n",
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"\n",
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|||
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"dataset_names = [\"TAIEX\", \"SP500\",\"NASDAQ\"]\n",
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"\n",
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"def get_dataset(name):\n",
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" if dataset_name == \"TAIEX\":\n",
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" return TAIEX.get_data()\n",
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" elif dataset_name == \"SP500\":\n",
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" return SP500.get_data()[11500:16000]\n",
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" elif dataset_name == \"NASDAQ\":\n",
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" return NASDAQ.get_data()\n",
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"\n",
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"\n",
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"train_split = 2000\n",
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|
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"test_length = 200"
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|
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],
|
|||
|
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"execution_count": 0,
|
|||
|
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"outputs": []
|
|||
|
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},
|
|||
|
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{
|
|||
|
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"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "kcq5Rwd7EAXn",
|
|||
|
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"colab_type": "text"
|
|||
|
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},
|
|||
|
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"source": [
|
|||
|
|
"### Visualization"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
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"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "cKUjQwaYEAXp",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {},
|
|||
|
|
"outputId": "0e605a2e-6310-4ffc-b939-e8ee58a290ca"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"fig, ax = plt.subplots(nrows=2, ncols=3, figsize=[10,5])\n",
|
|||
|
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"\n",
|
|||
|
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"for count,dataset_name in enumerate(dataset_names):\n",
|
|||
|
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" dataset = get_dataset(dataset_name)\n",
|
|||
|
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" dataset_diff = tdiff.apply(dataset)\n",
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|||
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"\n",
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|||
|
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" ax[0][count].plot(dataset)\n",
|
|||
|
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" ax[1][count].plot(dataset_diff)\n",
|
|||
|
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" ax[0][count].set_title(dataset_name)"
|
|||
|
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],
|
|||
|
|
"execution_count": 0,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
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"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAmoAAAE/CAYAAAD2ee+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzsnXd4FOX2x79n0yD0EjoYCAEEpIug\nFBGkesUuVvSiXK+9XQV7F7tiwWvB8ruKoGIF6V167yWE0EsIEEhC6p7fH/PO7ruzs32TnV3ez/Pk\nYeadsrMhM3PeU76HmBkKhUKhUCgUCuthi/QFKBQKhUKhUCjMUYaaQqFQKBQKhUVRhppCoVAoFAqF\nRVGGmkKhUCgUCoVFUYaaQqFQKBQKhUVRhppCoVAoFAqFRVGGmkKhUCgUCoVFUYZalEJEedKPnYjO\nSuu3SPvdQURMRDcajr+UiA5I6wuIqNBw3j/Etn8Q0REiqi3tP5yIDhJRjYr4vgqFPxBRLyJaSkS5\nRHSCiP4mogvFfVAm/q5PE9F6IrpCHJMq7hH5b/9Z6ZxJRDRRHHeEiB41fGZ/ItpORAVENJ+Izqvo\n761QKGIXZahFKcxcVf8BsA/AP6Sx76RdRwI4AeB2P057v3xeZv6H+Kw/AMwD8B4AEFFNABMA/JuZ\nc8P5vRSKYCGi6gD+BPAhgNoAGgN4EUCR2GWZuF9qAvgSwBQiqiWdoqb0t/+yNP4CgHQA5wHoB+AJ\nIhosPrMugKkAnhWfuRrA5PL5hgqFO0SURUTHiKiKNHYXES2Q1omIMoloq8nx7YholpjYnCKiNUQ0\nVGy7VDgC9AnMASKaQkQXmpzH42eI7VcQ0UoiyieiHCL6HxE1DssvIcZRhloMI2b2fQGMBjCIiBqE\ncLoHAQwhokHQDLaFzPx7GC5ToQgXrQCAmScxcxkzn2XmWcy8Ud6Jme0AJgKoDCDNj/OOBPAyM59k\n5m0APgdwh9h2DYAtzPwjMxdCM+o6ElGbsHwjhcI/4gA85GV7HwD1ALQwMbL+ADAbQAOxz4MATkvb\nD4kJTjUAPQBsB7CYiPr7+xlEdB2A7wG8D6AugHYAisV5avr7Jc9VlKEW29wOYDUz/wxgG4BbfOzv\nEWY+Du1B8B2AK6DdzAqFldgJoIyIviGiIQZvmQMiigdwF4A8ALukTXuFx+Ar4SmDOEdDABuk/TZA\ne9FA/OvYxsz5AHZL2xWKiuAtAI97MXpGAvgNwHSxDMDhEW4O4HNmLhY/fzPzEuMJWOMAMz8H4AsA\nb/j5GQTgHQCvMPP3YgJ1BNo9WADvBqYCylCLdW6HNouB+NdX+HO8cH3rPy8bti8HUAPALGbODvO1\nKhQhwcynAfQCwNC8XtlE9DsR1Re79CCiUwCOALgJwNUidH8cwIXQQptdoXkO9PSBquJfOcSfK/bR\ntxvD//J2haIiWA1gAYDHjRuIKBnAddD+pr8DMIKIEsXmHAAZAP5HRFdJ94ovpgLooodbfXxGawDN\nAPwon0B4tn8GMNDPzzxnUYZajEJEl0CbKf0ghr4HcAERdfJy2IPMXFP6edaw/TMA3wIYSkQ9w3/V\nCkVoMPM2Zr6DmZsAaA+gEbRwCwAsF3/XdZm5BzPPEcfkMfNqZi5l5qMA7gcwkIiqQfO6AUB16WOq\nAzgjlvMM24zbFYqK4jkADxBRimH8Gmh5mrMATAOQAGAYoHnJoOVdZkHzeh0mokVElO7jsw4BIGj5\nnl4/A1qoEwAOm5znMADj9SoMKEMtdhkJ7UZaT0RHAKyQxgOGiEYBaArgXgBPAfhCmjEpFJaDmbcD\n+BqawRbQoeJfGzOfhPYy6Sht7whgi1jeIm8THoY0abtCUSEw82ZoxTRjDJtGApgiJiKF0LxYI6Xj\nDjDz/cycBs2rnA9tQu6NxtDuk1N+fMZx8W9Dk/M0lLYrPKAMtRiEiCoBuAFaEUEn6ecBADeLHJ1A\nztcIWg7E3cxcBOBTaC7zp8N53QpFKBBRGyJ6jIiaiPWm0EKcy30cdxERtSYiGxHVATAewAKpovlb\nAM8QUS1RJHA3NAMQAH4B0J6IrhX33XMANgojUaGoaJ6H9vfZGADEvXAZgFuFtMwRaCHKoXoepgwz\n7wfwMXxPbq4GsJaZ8/34jB0ADgC4Xj4BEdkAXAstZKvwgjLUYpOrAJwF8C0zH9F/oFW6xQMY7OG4\njwxaUmvE+CcAfmDmxYDDXX43gIeJSCVNK6zCGQAXAVhBRPnQDLTNAB7zcVwLADPE8ZuhhXBukrY/\nD61AYC+AhQDeYuYZACByNa8F8CqAk+LzR4Tp+ygUAcHMGdDkYfRir9ugFdm0hnPC3gqa4XSTmHy8\nSEQtxUSlLoB/wmRyI+Q3GhPR89AKAZ7y5zPE++JxaJOdm4moklAg+AJaWPTDsP8iYgzSfocKhUKh\nUCiiDSLKAnCXnnMpPMm7oBlbDQB8zMwfGo55AlrUpS+0iXhfaEZTHoD5AB5n5oNEdCk0Dc0CaKk0\nuQCWAnibmZeLc2339hnM3E2sDwfwDLSK6MoANgG4npl3hPP3EYsoQ02hUCgUCkWFQUQDoRW4DWDm\n9ZG+HqujDDWFQqFQKBQVChH9A0BjZv400tdidZShplAoFAqFQmFRVDGBQhEmSGvcfYyINktjk0lr\nAL6etJ5868V4KhGdlbZ9Kh3TlYg2EVEGEY0Xyt4KhUKhOAcJSKZBoVB45WsAH0HSIGLmG/VlInoH\nrir2u5nZTIB4ArSq2hXQ2rEMBvBXOVyvQqFQKCxO1BpqdevW5dTU1EhfhkIBAFizZs1xZk4holSz\n7cIrdgM0vSGPEFFDANWliqpvocmt+DTU1D2hsBL6PRGpz1f3g8JqBHtPRK2hlpqaitWrV0f6MhQK\nAAAR7fWxS28AR5lZbgLenIjWATgN4BmhU9cYmv6QzgEx5ulzR0MTNkazZs3UPaGwDH7cE+WKekco\nrEaw94TKUVMoKoabAEyS1g8DaMbMnQE8CuB7IjL2jPQJM3/GzN2YuVtKimqZp1AoFLFG1HrUFIpo\nQbTsugZAV31MtOIqEstriGg3NDXvgwCaSIc3EWMKhUKhOAdRHrUKpLCkLNKXoIgMAwBsZ2ZHSJOI\nUogoTiy3AJAOIJOZDwM4TUQ9RF7b7QB+i8RFKxSeyD5ThIOnzkb6MhQKy1BaZkdxqb1czq0MtQoi\nMzsPbZ6dgV/WHfC9syIqIaJJAJYBaE1EB4holNg0Aq5hTwDoA2CjkOv4CcA9zHxCbLsXWh+8DGg9\nJlXFp8JSfDRvF4Z+sDjSl6FQlDtvztiOzxbt9rnfNROWotUz5fOo9mmoedCGqk1Es4lol/i3lhgn\nofuUQUQbiaiLdMxIsf8uIhopjVteM2rzwVzc8OkyFBSXBn2OiX/vAQA8MnkDjp0pDNelKSwEM9/E\nzA2ZOYGZmzDzl2L8DqP6NjP/zMztmLkTM3dh5j+kbauZuT0zpzHz/axUqRUW45tle5F7tiTSl6FQ\nlCvMjE8W7MZr07f73HfjgVyf+wSLPx61r6HpOMmMATCXmdMBzBXrADAEWggnHVol2gRAM+wAPA/g\nIgDdATyvG3dwakbpxxk/K+Jc8eESrMw6gfnbs4M+x9S1zjSj/1sW0WIohUKhCBo1b1CcK7zw+xbH\n8sFTZ3HLF8uROmYadh0947Jfed8TPg01Zl4E4IRheDiAb8TyN9B0nvTxb1ljOYCaQhdqEIDZzHyC\nmU8CmA1gsKwZJbwG30rnshxxtuCdfXbpP9JyLkOFQqHwA2bGij3a66Bzs5p+HSM6cmwSHThWi7Gw\nRWUUivJg7raj+EZyqmw+mIu/M3IAAJe/t8hl31MFmnd5YNv65XItweao1RdJzwBwBIB+dY0B7Jf2\n0zWgvI0HpBlFRKuJaHV2dvDerWAJwU6DbHCr+ai1eX36NkxcsifSl6FQWI4pq/djxGfLAQA3Xdgs\nkEP7iTB/N7Eez
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.figure.Figure at 0x7f1d9d525908>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "2vH6Rs_3EAXw",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"### Statistics"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "RlymHBC_EAXy",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {}
|
|||
|
|
},
|
|||
|
|
"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": 0,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "w2N9i_FDEAX2",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"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."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "pKF_XL8AEAX3",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {}
|
|||
|
|
},
|
|||
|
|
"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": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "S5SYQaXYEAX9",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {}
|
|||
|
|
},
|
|||
|
|
"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": 0,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "4uQw3zRCEAYA",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"### Comparing the partitioning schemas"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "btbXowDtEAYD",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 367
|
|||
|
|
},
|
|||
|
|
"outputId": "cbc2daa8-6532-4f4d-878c-305bd5b3c757"
|
|||
|
|
},
|
|||
|
|
"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=33)\n",
|
|||
|
|
" partitioners[dataset_name] = partitioner\n",
|
|||
|
|
" partitioner_diff = Grid.GridPartitioner(data=dataset, npart=15, 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": 10,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABIQAAAFKCAYAAABsCIRVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJEltHIuTGHFSE
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1440x360 with 6 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "WRPf2Y1IEAYH",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"## Fitting models"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "cbVBost5EAYI",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"### With original data"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "1KODZMNXEAYK",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {}
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"for count,dataset_name in enumerate(dataset_names):\n",
|
|||
|
|
" dataset = get_dataset(dataset_name)\n",
|
|||
|
|
"\n",
|
|||
|
|
" model1 = song.ConventionalFTS(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": 0,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "oIqek9SDEAYO",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"### With transformed data"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "fMQddS8mEAYP",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {}
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"for count,dataset_name in enumerate(dataset_names):\n",
|
|||
|
|
" dataset = get_dataset(dataset_name)\n",
|
|||
|
|
"\n",
|
|||
|
|
" model2 = song.ConventionalFTS(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": 0,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "jXQ8iOjuEAYU",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"## Predicting with the models"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "eGmCuKpREAYV",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 749
|
|||
|
|
},
|
|||
|
|
"outputId": "9130003c-33b7-4973-b284-6cb1f82784ec"
|
|||
|
|
},
|
|||
|
|
"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": 13,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
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|
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"text/plain": [
|
|||
|
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"<Figure size 1440x720 with 3 Axes>"
|
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]
|
|||
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},
|
|||
|
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"metadata": {
|
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"tags": []
|
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}
|
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}
|
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]
|
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},
|
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{
|
|||
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"cell_type": "code",
|
|||
|
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"metadata": {
|
|||
|
|
"id": "td318onIEAYZ",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
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|
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"height": 143
|
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|
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},
|
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|
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"outputId": "4b675d99-89be-4460-d248-6d28f4ac4448"
|
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|
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},
|
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|
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"source": [
|
|||
|
|
"from pyFTS.benchmarks import Measures\n",
|
|||
|
|
"\n",
|
|||
|
|
"rows = []\n",
|
|||
|
|
"\n",
|
|||
|
|
"for count,dataset_name in enumerate(dataset_names):\n",
|
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|
|
" row = [dataset_name]\n",
|
|||
|
|
" \n",
|
|||
|
|
" dataset = get_dataset(dataset_name)\n",
|
|||
|
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" \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",
|
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|
|
" \n",
|
|||
|
|
" rows.append(row)\n",
|
|||
|
|
" \n",
|
|||
|
|
" \n",
|
|||
|
|
"pd.DataFrame(rows,columns=[\"Dataset\",\"RMSE\",\"SMAPE\",\"Theil's U\"])\n",
|
|||
|
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" \n",
|
|||
|
|
" \n",
|
|||
|
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" "
|
|||
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],
|
|||
|
|
"execution_count": 14,
|
|||
|
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"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
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|
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" vertical-align: middle;\n",
|
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|
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" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
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"\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>2464.05</td>\n",
|
|||
|
|
" <td>53.58</td>\n",
|
|||
|
|
" <td>37.43</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <td>SP500</td>\n",
|
|||
|
|
" <td>96.15</td>\n",
|
|||
|
|
" <td>7.96</td>\n",
|
|||
|
|
" <td>17.41</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>NASDAQ</td>\n",
|
|||
|
|
" <td>1597.12</td>\n",
|
|||
|
|
" <td>94.78</td>\n",
|
|||
|
|
" <td>66.67</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" Dataset RMSE SMAPE Theil's U\n",
|
|||
|
|
"0 TAIEX 2464.05 53.58 37.43\n",
|
|||
|
|
"1 SP500 96.15 7.96 17.41\n",
|
|||
|
|
"2 NASDAQ 1597.12 94.78 66.67"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"execution_count": 14
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "2LdopGFNEAYe",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 749
|
|||
|
|
},
|
|||
|
|
"outputId": "cd213827-7b8e-464b-a602-b7e9db6e51eb"
|
|||
|
|
},
|
|||
|
|
"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": 15,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABZgAAALICAYAAADyhJW9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncHInDHg/y1Afb
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1440x720 with 3 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "uzEjl0cnEAYi",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 143
|
|||
|
|
},
|
|||
|
|
"outputId": "2222d916-2144-49ed-aeaa-6d5f19a07dea"
|
|||
|
|
},
|
|||
|
|
"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": 16,
|
|||
|
|
"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>120.04</td>\n",
|
|||
|
|
" <td>2.25</td>\n",
|
|||
|
|
" <td>1.82</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <td>SP500</td>\n",
|
|||
|
|
" <td>6.76</td>\n",
|
|||
|
|
" <td>0.52</td>\n",
|
|||
|
|
" <td>1.22</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>NASDAQ</td>\n",
|
|||
|
|
" <td>90.85</td>\n",
|
|||
|
|
" <td>5.14</td>\n",
|
|||
|
|
" <td>3.79</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" Dataset RMSE SMAPE Theil's U\n",
|
|||
|
|
"0 TAIEX 120.04 2.25 1.82\n",
|
|||
|
|
"1 SP500 6.76 0.52 1.22\n",
|
|||
|
|
"2 NASDAQ 90.85 5.14 3.79"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"execution_count": 16
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "0rCs_lQdEAYo",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"## Residual Analysis"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "osgNXFz-EAYp",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {}
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
""
|
|||
|
|
],
|
|||
|
|
"execution_count": 0,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "8UP_KLTwEAYr",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 1000
|
|||
|
|
},
|
|||
|
|
"outputId": "67b12ff3-0da0-438a-b4c6-63a1b9723625"
|
|||
|
|
},
|
|||
|
|
"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_by_model(dataset, [model1, model2])"
|
|||
|
|
],
|
|||
|
|
"execution_count": 18,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 576x576 with 8 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAkAAAAI4CAYAAACC61aaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfH7tALyU8NzTC
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 576x576 with 8 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 576x576 with 8 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "aqkE0lj2EAYw",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {}
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
""
|
|||
|
|
],
|
|||
|
|
"execution_count": 0,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "cV19oXyNEAYz",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {}
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
""
|
|||
|
|
],
|
|||
|
|
"execution_count": 0,
|
|||
|
|
"outputs": []
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
}
|