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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "1ebef7cf",
+ "metadata": {},
+ "source": [
+ "# Filling Time Series package: An example"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a5afacb2",
+ "metadata": {},
+ "source": [
+ "## Developed by: Rolando Duarte and Erick Rivera \n",
+ "### Information about it:\n",
+ "\n",
+ " PyPI - \"pip install FillingTimeSeries\"
\n",
+ "\n",
+ "\n",
+ "\n",
+ "### Importing packages\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "f3dfd4de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd #Checking datasedt\n",
+ "from FillingTimeSeries.FillingMethods import PrincipalComponentAnalysis, Autoregression, ComponentsAutoregression #Importing methods\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "39b5e7bd",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Help on module FillingTimeSeries.FillingMethods in FillingTimeSeries:\n",
+ "\n",
+ "NAME\n",
+ " FillingTimeSeries.FillingMethods\n",
+ "\n",
+ "DESCRIPTION\n",
+ " Note: V 0.9.1 Originally, filling data methods was developed by Eric Alfaro and Javier Soley in SCILAB\n",
+ " Python version was developed by Rolando Duarte and Erick Rivera\n",
+ " Centro de Investigaciones Geofísicas (CIGEFI)\n",
+ " Universidad de Costa Rica (UCR)\n",
+ "\n",
+ "CLASSES\n",
+ " builtins.object\n",
+ " Autoregression\n",
+ " ComponentsAutoregression\n",
+ " PrincipalComponentAnalysis\n",
+ " \n",
+ " class Autoregression(builtins.object)\n",
+ " | Autoregression(df)\n",
+ " | \n",
+ " | Applies Ulrich & Clayton autoregression method\n",
+ " | \n",
+ " | Parameters\n",
+ " | ----------\n",
+ " | df: pandas dataframe\n",
+ " | Dataframe\n",
+ " | \n",
+ " | Methods defined here:\n",
+ " | \n",
+ " | ULCLMethod(self, lags=1, tol=0.1, itermax=10, valueMin=0.0)\n",
+ " | Ulrich & Clayton autoregression method and graphs with original and filled values\n",
+ " | \n",
+ " | Parameters\n",
+ " | ----------\n",
+ " | lags: int\n",
+ " | Lags value for autoregression\n",
+ " | tol: float\n",
+ " | Tolerance value of difference between previous filled serie and current filled serie\n",
+ " | itermax: int\n",
+ " | Maximum iterations to find a filled serie that complies tolerance condition\n",
+ " | valueMin: float\n",
+ " | The minimum value allowed after applying the autoregression method.\n",
+ " | \n",
+ " | Returns\n",
+ " | -------\n",
+ " | dfPF: pandas-dataframe\n",
+ " | Pandas dataframe using past and future values to fill missing values\n",
+ " | \n",
+ " | __init__(self, df)\n",
+ " | Initialize self. See help(type(self)) for accurate signature.\n",
+ " | \n",
+ " | simpleAR(self, serie, nanIndex, k)\n",
+ " | Applies a simple autoregression\n",
+ " | \n",
+ " | Parameters\n",
+ " | ----------\n",
+ " | serie: pandas serie\n",
+ " | pandas serie\n",
+ " | nanIndex: array\n",
+ " | array with missing value indexes\n",
+ " | k: int\n",
+ " | number of lags for AutoReg function\n",
+ " | \n",
+ " | Returns\n",
+ " | -------\n",
+ " | serie: pandas serie\n",
+ " | pandas serie changing the missing values using nanIndex\n",
+ " | \n",
+ " | ----------------------------------------------------------------------\n",
+ " | Data descriptors defined here:\n",
+ " | \n",
+ " | __dict__\n",
+ " | dictionary for instance variables (if defined)\n",
+ " | \n",
+ " | __weakref__\n",
+ " | list of weak references to the object (if defined)\n",
+ " \n",
+ " class ComponentsAutoregression(builtins.object)\n",
+ " | ComponentsAutoregression(df)\n",
+ " | \n",
+ " | First, this class applies the autoregression method, then, principal components method\n",
+ " | \n",
+ " | Parameters\n",
+ " | ----------\n",
+ " | df: pandas dataframe\n",
+ " | Dataframe\n",
+ " | \n",
+ " | Methods defined here:\n",
+ " | \n",
+ " | FullMethod(self, lags=1, components=1, tol=0.1, itermax=10, valueMin=0.0)\n",
+ " | Full method\n",
+ " | \n",
+ " | Parameters\n",
+ " | ----------\n",
+ " | lags: int\n",
+ " | Lags value for autoregression\n",
+ " | components: int\n",
+ " | principal component number\n",
+ " | tol: float\n",
+ " | Tolerance value of difference between previous filled dataframe and current filled dataframe\n",
+ " | itermax: int\n",
+ " | Maximum iterations to find a filled dataframe that complies tolerance condition\n",
+ " | valueMin: float\n",
+ " | The minimum value allowed after applying the autoregression and principal components methods.\n",
+ " | \n",
+ " | Returns\n",
+ " | -------\n",
+ " | dfPCA: pandas dataframe\n",
+ " | pandas dataframe using autoregression and principal components to fill missing values\n",
+ " | \n",
+ " | __init__(self, df)\n",
+ " | Initialize self. See help(type(self)) for accurate signature.\n",
+ " | \n",
+ " | checkPrincipalComponents(self)\n",
+ " | Graphs explained variance of principal components\n",
+ " | \n",
+ " | Returns\n",
+ " | -------\n",
+ " | upperError: int\n",
+ " | Maximum value to choose principal components\n",
+ " | \n",
+ " | ----------------------------------------------------------------------\n",
+ " | Data descriptors defined here:\n",
+ " | \n",
+ " | __dict__\n",
+ " | dictionary for instance variables (if defined)\n",
+ " | \n",
+ " | __weakref__\n",
+ " | list of weak references to the object (if defined)\n",
+ " \n",
+ " class PrincipalComponentAnalysis(builtins.object)\n",
+ " | PrincipalComponentAnalysis(df, **kwargs)\n",
+ " | \n",
+ " | Applies principal component method\n",
+ " | \n",
+ " | Parameters\n",
+ " | ----------\n",
+ " | df: pandas dataframe\n",
+ " | Dataframe\n",
+ " | \n",
+ " | Methods defined here:\n",
+ " | \n",
+ " | PCAMethod(self, components=1, tol=0.1, itermax=10, valueMin=0.0)\n",
+ " | Principal components method\n",
+ " | \n",
+ " | Parameters\n",
+ " | ----------\n",
+ " | components: int\n",
+ " | principal component number\n",
+ " | tol: float\n",
+ " | Tolerance value of difference between previous filled dataframe and current filled dataframe\n",
+ " | itermax: int\n",
+ " | Maximum iterations to find a filled dataframe that complies tolerance condition\n",
+ " | valueMin: float\n",
+ " | The minimum value allowed after applying the principal components method.\n",
+ " | \n",
+ " | Returns\n",
+ " | -------\n",
+ " | dfActual: pandas dataframe\n",
+ " | pandas dataframe using principal components to fill missing values\n",
+ " | \n",
+ " | __init__(self, df, **kwargs)\n",
+ " | Initialize self. See help(type(self)) for accurate signature.\n",
+ " | \n",
+ " | checkPrincipalComponents(self)\n",
+ " | Graphs explained variance of principal components\n",
+ " | \n",
+ " | Returns\n",
+ " | -------\n",
+ " | upperError: int\n",
+ " | Maximum value to choose principal components\n",
+ " | \n",
+ " | ----------------------------------------------------------------------\n",
+ " | Data descriptors defined here:\n",
+ " | \n",
+ " | __dict__\n",
+ " | dictionary for instance variables (if defined)\n",
+ " | \n",
+ " | __weakref__\n",
+ " | list of weak references to the object (if defined)\n",
+ "\n",
+ "FUNCTIONS\n",
+ " arange(...)\n",
+ " arange([start,] stop[, step,], dtype=None, *, like=None)\n",
+ " \n",
+ " Return evenly spaced values within a given interval.\n",
+ " \n",
+ " Values are generated within the half-open interval ``[start, stop)``\n",
+ " (in other words, the interval including `start` but excluding `stop`).\n",
+ " For integer arguments the function is equivalent to the Python built-in\n",
+ " `range` function, but returns an ndarray rather than a list.\n",
+ " \n",
+ " When using a non-integer step, such as 0.1, the results will often not\n",
+ " be consistent. It is better to use `numpy.linspace` for these cases.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " start : integer or real, optional\n",
+ " Start of interval. The interval includes this value. The default\n",
+ " start value is 0.\n",
+ " stop : integer or real\n",
+ " End of interval. The interval does not include this value, except\n",
+ " in some cases where `step` is not an integer and floating point\n",
+ " round-off affects the length of `out`.\n",
+ " step : integer or real, optional\n",
+ " Spacing between values. For any output `out`, this is the distance\n",
+ " between two adjacent values, ``out[i+1] - out[i]``. The default\n",
+ " step size is 1. If `step` is specified as a position argument,\n",
+ " `start` must also be given.\n",
+ " dtype : dtype\n",
+ " The type of the output array. If `dtype` is not given, infer the data\n",
+ " type from the other input arguments.\n",
+ " like : array_like\n",
+ " Reference object to allow the creation of arrays which are not\n",
+ " NumPy arrays. If an array-like passed in as ``like`` supports\n",
+ " the ``__array_function__`` protocol, the result will be defined\n",
+ " by it. In this case, it ensures the creation of an array object\n",
+ " compatible with that passed in via this argument.\n",
+ " \n",
+ " .. versionadded:: 1.20.0\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " arange : ndarray\n",
+ " Array of evenly spaced values.\n",
+ " \n",
+ " For floating point arguments, the length of the result is\n",
+ " ``ceil((stop - start)/step)``. Because of floating point overflow,\n",
+ " this rule may result in the last element of `out` being greater\n",
+ " than `stop`.\n",
+ " \n",
+ " See Also\n",
+ " --------\n",
+ " numpy.linspace : Evenly spaced numbers with careful handling of endpoints.\n",
+ " numpy.ogrid: Arrays of evenly spaced numbers in N-dimensions.\n",
+ " numpy.mgrid: Grid-shaped arrays of evenly spaced numbers in N-dimensions.\n",
+ " \n",
+ " Examples\n",
+ " --------\n",
+ " >>> np.arange(3)\n",
+ " array([0, 1, 2])\n",
+ " >>> np.arange(3.0)\n",
+ " array([ 0., 1., 2.])\n",
+ " >>> np.arange(3,7)\n",
+ " array([3, 4, 5, 6])\n",
+ " >>> np.arange(3,7,2)\n",
+ " array([3, 5])\n",
+ "\n",
+ "DATA\n",
+ " abs = \n",
+ " nan = nan\n",
+ " options = \n",
+ " sqrt = \n",
+ "\n",
+ "FILE\n",
+ " c:\\users\\rolando\\anaconda3\\lib\\site-packages\\fillingtimeseries\\fillingmethods.py\n",
+ "\n",
+ "\n",
+ "None\n"
+ ]
+ }
+ ],
+ "source": [
+ "import FillingTimeSeries.FillingMethods as fts\n",
+ "print(help(fts))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2a62ffff",
+ "metadata": {},
+ "source": [
+ "#### Function to plot dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "1ea15502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plotColumns(dfOriginal = None, dfFilled = None, rows = 1, columns = 1):\n",
+ " fig, axArray = plt.subplots(rows, columns, squeeze=False, sharex = True, figsize = (25, 10))\n",
+ " plt.title(\"Some filled columns\", size = \"xx-large\")\n",
+ " index = 0\n",
+ " for i,ax_row in enumerate(axArray):\n",
+ " for j,axes in enumerate(ax_row):\n",
+ " if type(dfFilled) == type(dfOriginal):\n",
+ " axes.plot(dfFilled.index, dfFilled[dfFilled.columns[index]], color = \"red\")\n",
+ " axes.plot(dfOriginal.index, dfOriginal[dfOriginal.columns[index]], color = \"blue\")\n",
+ " axes.set_title(\"Column \"+ str(dfOriginal.columns[index]) + \": original and filled values\", size = \"x-large\")\n",
+ " axes.set_xlabel(\"Index\", size = \"x-large\")\n",
+ " axes.set_ylabel(\"Column \" + str(dfOriginal.columns[index]) + \" magnitude\", size = \"x-large\")\n",
+ " index = index + 1\n",
+ " plt.legend([\"Predicted values\", \"Real values\"])\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1bcd725c",
+ "metadata": {},
+ "source": [
+ "## Principal-components-based Method\n",
+ "### Checking original time serie\n",
+ "#### Importing dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "0f4b2060",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "RangeIndex: 408 entries, 0 to 407\n",
+ "Data columns (total 6 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 0 400 non-null float64\n",
+ " 1 1 404 non-null float64\n",
+ " 2 2 394 non-null float64\n",
+ " 3 3 403 non-null float64\n",
+ " 4 4 137 non-null float64\n",
+ " 5 5 335 non-null float64\n",
+ "dtypes: float64(6)\n",
+ "memory usage: 19.2 KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "dfOriginal = pd.read_csv(\"estacionesch_nuevas.txt\", delimiter = \"\\s+\", header = None)\n",
+ "dfOriginal.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "ab6b13ee",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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