837 lines
713 KiB
Plaintext
837 lines
713 KiB
Plaintext
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "Cheng%20-%20TrendWeightedFTS.ipynb",
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"version": "0.3.2",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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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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"[View in Colaboratory](https://colab.research.google.com/github/petroniocandido/pyFTS/blob/master/GOOGLE%20COLAB%20Cheng%20-%20TrendWeightedFTS.ipynb)"
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]
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},
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{
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"metadata": {
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"id": "fv3g4xdNiPhV",
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"colab_type": "text"
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},
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"cell_type": "markdown",
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"source": [
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"# Trend Weighted Fuzzy Time Series by Cheng, Chen and Wu (2009)\n",
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"\n",
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"C.-H. Cheng, Y.-S. Chen, and Y.-L. Wu, “Forecasting innovation diffusion of products using trend-weighted fuzzy time-series model,” \n",
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"Expert Syst. Appl., vol. 36, no. 2, pp. 1826–1832, 2009."
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]
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},
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{
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"metadata": {
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"id": "IAiBsikgiPhY",
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"colab_type": "text"
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},
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"cell_type": "markdown",
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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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"metadata": {
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"id": "VugE64AIiPhd",
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"colab_type": "text"
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},
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"cell_type": "markdown",
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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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"metadata": {
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"id": "B_YO5b1yiPhg",
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"colab_type": "code",
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"colab": {}
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},
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"cell_type": "code",
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"source": [
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"!pip3 install -U git+https://github.com/petroniocandido/pyFTS\n",
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"!pip3 install dill"
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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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"metadata": {
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"id": "q3hDVCskiPhp",
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"colab_type": "text"
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},
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"cell_type": "markdown",
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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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"metadata": {
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"id": "TuawpGrtiPhr",
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"colab_type": "code",
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"colab": {},
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"outputId": "2c24d40d-4622-4dbc-8a08-e39bb1b3aefb"
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},
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"cell_type": "code",
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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": 0,
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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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"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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"metadata": {
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"id": "SYy_OZUwiPh1",
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"colab_type": "text"
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},
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"cell_type": "markdown",
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"source": [
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"### Common pyFTS imports "
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]
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},
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{
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"metadata": {
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"id": "KXUdwKyfiPh3",
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"colab_type": "code",
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"colab": {}
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},
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"cell_type": "code",
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"source": [
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"from pyFTS.common import Util as cUtil\n",
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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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"from pyFTS.models import cheng\n"
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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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"metadata": {
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"id": "l5M4ousciPh-",
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"colab_type": "text"
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},
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"cell_type": "markdown",
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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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"metadata": {
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"id": "iTpP26KjiPh_",
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"colab_type": "code",
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"colab": {}
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},
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"cell_type": "code",
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"source": [
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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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"execution_count": 0,
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"outputs": []
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},
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{
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"metadata": {
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"id": "L9AMR7d1iPiG",
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"colab_type": "text"
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},
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"cell_type": "markdown",
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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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"metadata": {
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"id": "sreJLRRgiPiJ",
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"colab_type": "text"
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},
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"cell_type": "markdown",
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"source": [
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"### Data Loading"
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]
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},
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{
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"metadata": {
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"id": "3e-A5rN9iPiK",
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"colab_type": "code",
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"colab": {}
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},
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"cell_type": "code",
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"source": [
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"from pyFTS.data import TAIEX, NASDAQ, SP500\n",
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"\n",
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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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"test_length = 200"
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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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"metadata": {
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"id": "nMeCAWv5iPiT",
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"colab_type": "text"
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},
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"cell_type": "markdown",
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"source": [
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"### Visualization"
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]
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},
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{
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"metadata": {
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"id": "lZOWiOloiPiV",
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"colab_type": "code",
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"colab": {},
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"outputId": "2a0505b6-0055-4109-d2f3-1d6aa0e98544"
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},
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"cell_type": "code",
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"source": [
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"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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"\n",
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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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],
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"execution_count": 0,
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"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 0x7fdb3b6d4eb8>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "J7Rb2JlWiPif",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"### Statistics"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "QO7RegoZiPii",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {},
|
|||
|
|
"outputId": "3d8330e7-5d83-41f1-90c9-8627faa16177"
|
|||
|
|
},
|
|||
|
|
"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": 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": 6
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "8RNhM8gsiPit",
|
|||
|
|
"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": "1UyPiXLEiPiv",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {}
|
|||
|
|
},
|
|||
|
|
"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 = '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=[cheng.TrendWeightedFTS],\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=[cheng.TrendWeightedFTS],\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": "NH2uIY6ciPiz",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {}
|
|||
|
|
},
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"from pyFTS.benchmarks import Util as bUtil\n",
|
|||
|
|
"\n",
|
|||
|
|
"df1 = bUtil.get_dataframe_from_bd(\"benchmarks.db\",\n",
|
|||
|
|
" \"tag = 'partitioning' and measure = 'rmse'and transformation is null\")\n",
|
|||
|
|
"\n",
|
|||
|
|
"df2 = bUtil.get_dataframe_from_bd(\"benchmarks.db\",\n",
|
|||
|
|
" \"tag = '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": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "T14H7dPliPi-",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"### Comparing the partitioning schemas"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "IC8s1rM_iPi_",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {},
|
|||
|
|
"outputId": "0b792e48-7037-4809-8176-c96cd2a7f494"
|
|||
|
|
},
|
|||
|
|
"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": 0,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABIQAAAFKCAYAAABsCIRVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndbBZlqbW8RBw8
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.figure.Figure at 0x7fdb3aac56d8>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "vSrtkXZCiPjG",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"## Fitting models"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "fNFi9mNTiPjG",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"### With original data"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "PLt_m6tdiPjH",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {}
|
|||
|
|
},
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"for count,dataset_name in enumerate(dataset_names):\n",
|
|||
|
|
" dataset = get_dataset(dataset_name)\n",
|
|||
|
|
"\n",
|
|||
|
|
" model1 = cheng.TrendWeightedFTS(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": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "wB851cwviPjL",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"### With transformed data"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "ibGfjsHNiPjN",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {}
|
|||
|
|
},
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"for count,dataset_name in enumerate(dataset_names):\n",
|
|||
|
|
" dataset = get_dataset(dataset_name)\n",
|
|||
|
|
"\n",
|
|||
|
|
" model2 = cheng.TrendWeightedFTS(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": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "ahzbYUH4iPjc",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"## Predicting with the models"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "N4k1lA5giPji",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {},
|
|||
|
|
"outputId": "bdc64748-ee9a-4794-ed84-0c83491293bc"
|
|||
|
|
},
|
|||
|
|
"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": 0,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABZgAAALICAYAAADyhJW9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlzcWb2dbXhFfr
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.figure.Figure at 0x7fdafe599438>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "gliWqlZwiPjr",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {}
|
|||
|
|
},
|
|||
|
|
"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"
|
|||
|
|
],
|
|||
|
|
"execution_count": 0,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "v2PGRzkZiPjy",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {},
|
|||
|
|
"outputId": "914bc04d-497f-4a82-a7e6-0f8bdeaa61a6"
|
|||
|
|
},
|
|||
|
|
"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": 0,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABZgAAALICAYAAADyhJW9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/TQAJ9dPp3zWY
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.figure.Figure at 0x7fdafea31da0>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "vFgL2O3KiPj7",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {}
|
|||
|
|
},
|
|||
|
|
"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": 0,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "77ttQ0YviPkA",
|
|||
|
|
"colab_type": "text"
|
|||
|
|
},
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"## Residual Analysis"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "VN0y9mo2iPkC",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {},
|
|||
|
|
"outputId": "d993c80f-fad7-4f49-9db3-bca7eebf6605"
|
|||
|
|
},
|
|||
|
|
"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": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAjkAAAI4CAYAAACIt/jIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZjKzSE4KckREp
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.figure.Figure at 0x7fdafeb6ff98>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAI4CAYAAABndZP2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzjnnOlGSPjiSb
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.figure.Figure at 0x7fdafcbf46d8>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
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"text/plain": [
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|||
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"<matplotlib.figure.Figure at 0x7fdafcc343c8>"
|
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|
]
|
|||
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|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "fYFPL3EHiPkS",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {}
|
|||
|
|
},
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
""
|
|||
|
|
],
|
|||
|
|
"execution_count": 0,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "WBlAz7l0iPkW",
|
|||
|
|
"colab_type": "code",
|
|||
|
|
"colab": {}
|
|||
|
|
},
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
""
|
|||
|
|
],
|
|||
|
|
"execution_count": 0,
|
|||
|
|
"outputs": []
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
}
|