1073 lines
741 KiB
Plaintext
1073 lines
741 KiB
Plaintext
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"r": {
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"window_display": false
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},
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"colab": {
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"name": "Sadaei et Al - ExponentialyWeightedFTS.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/Sadaei%20et%20Al%20-%20ExponentialyWeightedFTS.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": "lM8qQ6ut_iw6",
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"colab_type": "text"
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},
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"source": [
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"# First Order Exponentialy Weighted Fuzzy Time Series by Sadaei et al. (2013)\n",
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"\n",
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"H. J. Sadaei, R. Enayatifar, A. H. Abdullah, and A. Gani, “Short-term load forecasting using a hybrid model with a \n",
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"refined exponentially weighted fuzzy time series and an improved harmony search,” Int. J. Electr. Power Energy Syst., vol. 62, no. from 2005, pp. 118–129, 2014.\n"
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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": "HJgv4ar5_iw_",
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"colab_type": "text"
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},
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"source": [
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|
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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": "eoko3gLq_ixC",
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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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|||
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"id": "hVxSF07M_ixE",
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"colab_type": "code",
|
|||
|
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"colab": {
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|||
|
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"base_uri": "https://localhost:8080/",
|
|||
|
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"height": 211
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},
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"outputId": "1e00bf2c-1daa-4389-d72d-6e1b17ca317d"
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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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|||
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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-n4jrx5s1\n",
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|
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" Running command git clone -q https://github.com/PYFTS/pyFTS /tmp/pip-req-build-n4jrx5s1\n",
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|
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"Building wheels for collected packages: pyFTS\n",
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|
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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=d833e69f2afc243355ca38841ce4f8568c09022c9ab779f1a15b1e62b9225841\n",
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" Stored in directory: /tmp/pip-ephem-wheel-cache-e_w61r7a/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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|||
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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": "I7IiQVlM_ixN",
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|||
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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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{
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"cell_type": "code",
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|||
|
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"metadata": {
|
|||
|
|
"id": "SlHwZLh4_ixQ",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
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"height": 34
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},
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|||
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"outputId": "cf0537a4-0cb9-4ad1-9044-a86cf93ebe03"
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|||
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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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|
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"import numpy as np\n",
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|
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"import pandas as pd\n",
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"import matplotlib.pylab as plt\n",
|
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|
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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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],
|
|||
|
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"execution_count": 2,
|
|||
|
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"outputs": [
|
|||
|
|
{
|
|||
|
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"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Populating the interactive namespace from numpy and matplotlib\n"
|
|||
|
|
],
|
|||
|
|
"name": "stdout"
|
|||
|
|
}
|
|||
|
|
]
|
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},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "NBXP-XrZ_ixW",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"### Common pyFTS imports "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "t9pMQ1zi_ixZ",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {}
|
|||
|
|
},
|
|||
|
|
"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 sadaei\n"
|
|||
|
|
],
|
|||
|
|
"execution_count": 0,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "wd5jfo-V_ixd",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"## Common data transformations"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "sJhW5IqG_ixf",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {}
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"from pyFTS.common import Transformations\n",
|
|||
|
|
"\n",
|
|||
|
|
"tdiff = Transformations.Differential(1)\n",
|
|||
|
|
"\n",
|
|||
|
|
"boxcox = Transformations.BoxCox(0)"
|
|||
|
|
],
|
|||
|
|
"execution_count": 0,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "kRyTrAxH_ixl",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"## Datasets"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "Cq94YDtt_ixp",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"### Data Loading"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "Kz-5aDEk_ixq",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {}
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"from pyFTS.data import TAIEX, NASDAQ, SP500\n",
|
|||
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"\n",
|
|||
|
|
"dataset_names = [\"TAIEX\", \"SP500\",\"NASDAQ\"]\n",
|
|||
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"\n",
|
|||
|
|
"def get_dataset(name):\n",
|
|||
|
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" if dataset_name == \"TAIEX\":\n",
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|||
|
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" 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",
|
|||
|
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"\n",
|
|||
|
|
"\n",
|
|||
|
|
"train_split = 2000\n",
|
|||
|
|
"test_length = 200"
|
|||
|
|
],
|
|||
|
|
"execution_count": 0,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "3LrIaNnW_ixv",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"### Visualization"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "_-Zl3wfq_ixw",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {},
|
|||
|
|
"outputId": "07943492-9272-4f97-b1d4-d9f7b21d3e3b"
|
|||
|
|
},
|
|||
|
|
"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": 0,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAmoAAAE/CAYAAAD2ee+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkEP7iTB/N7Eez
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.figure.Figure at 0x7f0b8afeaa58>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "jkywresy_ix2",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"### Statistics"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "JoAd_qlg_ix2",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {},
|
|||
|
|
"outputId": "d1144315-b01b-4bae-dec3-e79cf4e8bcb3"
|
|||
|
|
},
|
|||
|
|
"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": [
|
|||
|
|
{
|
|||
|
|
"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": 7
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "kehPNFlF_ix7",
|
|||
|
|
"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": "ZTR4x0PF_ix9",
|
|||
|
|
"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",
|
|||
|
|
" orders=[2,3],\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",
|
|||
|
|
" orders=[2,3],\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": "oDiV1KtT_iyD",
|
|||
|
|
"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": "PtJli61J_iyH",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"### Comparing the partitioning schemas"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "04QAgOWu_iyI",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 367
|
|||
|
|
},
|
|||
|
|
"outputId": "9920ecf6-1c9e-4145-90f6-0d653c32f805"
|
|||
|
|
},
|
|||
|
|
"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": 6,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABIQAAAFKCAYAAABsCIRVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndbBZlqbW8RBw8
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1440x360 with 6 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "jwC6e5az_iyN",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"## Fitting models"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "tfMzI8g0_iyO",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"### With original data"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "W0mDBSuR_iyP",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {}
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"for count,dataset_name in enumerate(dataset_names):\n",
|
|||
|
|
" dataset = get_dataset(dataset_name)\n",
|
|||
|
|
"\n",
|
|||
|
|
" model1 = sadaei.ExponentialyWeightedFTS(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": "0-d2nsVl_iyU",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"### With transformed data"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "AX2iqUZx_iyW",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {}
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"for count,dataset_name in enumerate(dataset_names):\n",
|
|||
|
|
" dataset = get_dataset(dataset_name)\n",
|
|||
|
|
"\n",
|
|||
|
|
" model2 = sadaei.ExponentialyWeightedFTS(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": "rrFyhOHi_iya",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"## Predicting with the models"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "8z3s0TQX_iyb",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 749
|
|||
|
|
},
|
|||
|
|
"outputId": "eef31864-3ce1-49c8-f1eb-eb0f0b6a64c2"
|
|||
|
|
},
|
|||
|
|
"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": 9,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABZgAAALICAYAAADyhJW9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUVlTS1H6SgD8Y
|
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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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},
|
|||
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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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|
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"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "QvQ00WtC_iyi",
|
|||
|
|
"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": "b2a91c40-df47-4004-9003-c21b61e28300"
|
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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",
|
|||
|
|
" 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",
|
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|
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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": 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",
|
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|
" }\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>92.41</td>\n",
|
|||
|
|
" <td>1.58</td>\n",
|
|||
|
|
" <td>1.41</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <td>SP500</td>\n",
|
|||
|
|
" <td>18.38</td>\n",
|
|||
|
|
" <td>1.37</td>\n",
|
|||
|
|
" <td>3.30</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>NASDAQ</td>\n",
|
|||
|
|
" <td>48.72</td>\n",
|
|||
|
|
" <td>2.35</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 92.41 1.58 1.41\n",
|
|||
|
|
"1 SP500 18.38 1.37 3.30\n",
|
|||
|
|
"2 NASDAQ 48.72 2.35 2.03"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"execution_count": 10
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "SQ2DArZF_iyn",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 749
|
|||
|
|
},
|
|||
|
|
"outputId": "7aff627b-b18f-48d9-d024-3a6a51f8a01e"
|
|||
|
|
},
|
|||
|
|
"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": 11,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABZgAAALICAYAAADyhJW9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nL8+KOYzP8Z008
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1440x720 with 3 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "8_8RAsX4_iyr",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 143
|
|||
|
|
},
|
|||
|
|
"outputId": "cc8e54e1-963d-4725-e456-cb0fa18ec097"
|
|||
|
|
},
|
|||
|
|
"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": 12,
|
|||
|
|
"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>68.71</td>\n",
|
|||
|
|
" <td>1.17</td>\n",
|
|||
|
|
" <td>1.05</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <td>SP500</td>\n",
|
|||
|
|
" <td>5.27</td>\n",
|
|||
|
|
" <td>0.39</td>\n",
|
|||
|
|
" <td>0.95</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>NASDAQ</td>\n",
|
|||
|
|
" <td>25.80</td>\n",
|
|||
|
|
" <td>1.24</td>\n",
|
|||
|
|
" <td>1.08</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" Dataset RMSE SMAPE Theil's U\n",
|
|||
|
|
"0 TAIEX 68.71 1.17 1.05\n",
|
|||
|
|
"1 SP500 5.27 0.39 0.95\n",
|
|||
|
|
"2 NASDAQ 25.80 1.24 1.08"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"execution_count": 12
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "jQvzck1V_iyv",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"## Residual Analysis"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "Pi_N3H4d_iyw",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 1000
|
|||
|
|
},
|
|||
|
|
"outputId": "69866ed3-b356-484b-a94b-a9b01647b249"
|
|||
|
|
},
|
|||
|
|
"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": 14,
|
|||
|
|
"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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\nZtlNow25EegVr
|
|||
|
|
"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": "X0uI3zPT_iy0",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {}
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
""
|
|||
|
|
],
|
|||
|
|
"execution_count": 0,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "FfkE8noZ_iy3",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {}
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
""
|
|||
|
|
],
|
|||
|
|
"execution_count": 0,
|
|||
|
|
"outputs": []
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
}
|