# pyFTS.data package

## Module contents

Module for pyFTS standard datasets facilities

## Submodules

## pyFTS.data.common module

`pyFTS.data.common.``get_dataframe`(filename, url, sep=';', compression='infer')

  This method check if filename already exists, read the file and return its data. If the file don’t already exists, it will be downloaded and decompressed.

  Parameters: | filename – dataset local filename url – dataset internet URL sep – CSV field separator compression – type of compression
  Returns: | Pandas dataset

## Datasets

## Artificial and synthetic data generators

Facilities to generate synthetic stochastic processes

class `pyFTS.data.artificial.``SignalEmulator`(**kwargs)

  Bases: `object`

  Emulate a complex signal built from several additive and non-additive components

  `blip`(**kwargs)

    Creates an outlier greater than the maximum or lower then the minimum previous values of the signal, and insert it on a random location of the signal.

    Returns: | the current SignalEmulator instance, for method chaining

  `components` = None

    Components of the signal

  `incremental_gaussian`(mu, sigma, **kwargs)

    Creates an additive gaussian interference on a previous signal

    Parameters: | mu – increment on mean sigma – increment on variance start – lag index to start this signal, the default value is 0 it – Number of iterations, the default value is 1 length – Number of samples generated on each iteration, the default value is 100 vmin – Lower bound value of generated data, the default value is None vmax – Upper bound value of generated data, the default value is None
    Returns: | the current SignalEmulator instance, for method chaining

  `periodic_gaussian`(type, period, mu_min, sigma_min, mu_max, sigma_max, **kwargs)

    Creates an additive periodic gaussian interference on a previous signal

    Parameters: | type – ‘linear’ or ‘sinoidal’ period – the period of recurrence mu – increment on mean sigma – increment on variance start – lag index to start this signal, the default value is 0 it – Number of iterations, the default value is 1 length – Number of samples generated on each iteration, the default value is 100 vmin – Lower bound value of generated data, the default value is None vmax – Upper bound value of generated data, the default value is None
    Returns: | the current SignalEmulator instance, for method chaining

  `run`()

    Render the signal

    Returns: | a list of float values

  `stationary_gaussian`(mu, sigma, **kwargs)

    Creates a continuous Gaussian signal with mean mu and variance sigma.

    Parameters: | mu – mean sigma – variance additive – If False it cancels the previous signal and start this one, if True this signal is added to the previous one start – lag index to start this signal, the default value is 0 it – Number of iterations, the default value is 1 length – Number of samples generated on each iteration, the default value is 100 vmin – Lower bound value of generated data, the default value is None vmax – Upper bound value of generated data, the default value is None
    Returns: | the current SignalEmulator instance, for method chaining

`pyFTS.data.artificial.``generate_gaussian_linear`(mu_ini, sigma_ini, mu_inc, sigma_inc, it=100, num=10, vmin=None, vmax=None)

  Generate data sampled from Gaussian distribution, with constant or linear changing parameters

  Parameters: | mu_ini – Initial mean sigma_ini – Initial variance mu_inc – Mean increment after ‘num’ samples sigma_inc – Variance increment after ‘num’ samples it – Number of iterations num – Number of samples generated on each iteration vmin – Lower bound value of generated data vmax – Upper bound value of generated data
  Returns: | A list of it*num float values

`pyFTS.data.artificial.``generate_linear_periodic_gaussian`(period, mu_min, sigma_min, mu_max, sigma_max, it=100, num=10, vmin=None, vmax=None)

  Generates a periodic linear variation on mean and variance

  Parameters: | period – the period of recurrence mu_min – initial (and minimum) mean of each period sigma_min – initial (and minimum) variance of each period mu_max – final (and maximum) mean of each period sigma_max – final (and maximum) variance of each period it – Number of iterations num – Number of samples generated on each iteration vmin – Lower bound value of generated data vmax – Upper bound value of generated data
  Returns: | A list of it*num float values

`pyFTS.data.artificial.``generate_sinoidal_periodic_gaussian`(period, mu_min, sigma_min, mu_max, sigma_max, it=100, num=10, vmin=None, vmax=None)

  Generates a periodic sinoidal variation on mean and variance

  Parameters: | period – the period of recurrence mu_min – initial (and minimum) mean of each period sigma_min – initial (and minimum) variance of each period mu_max – final (and maximum) mean of each period sigma_max – final (and maximum) variance of each period it – Number of iterations num – Number of samples generated on each iteration vmin – Lower bound value of generated data vmax – Upper bound value of generated data
  Returns: | A list of it*num float values

`pyFTS.data.artificial.``generate_uniform_linear`(min_ini, max_ini, min_inc, max_inc, it=100, num=10, vmin=None, vmax=None)

  Generate data sampled from Uniform distribution, with constant or linear changing bounds

  Parameters: | mu_ini – Initial mean sigma_ini – Initial variance mu_inc – Mean increment after ‘num’ samples sigma_inc – Variance increment after ‘num’ samples it – Number of iterations num – Number of samples generated on each iteration vmin – Lower bound value of generated data vmax – Upper bound value of generated data
  Returns: | A list of it*num float values

`pyFTS.data.artificial.``random_walk`(n=500, type='gaussian')

  Simple random walk

  Parameters: | n – number of samples type – ‘gaussian’ or ‘uniform’
  Returns: |

`pyFTS.data.artificial.``white_noise`(n=500)

  Simple Gaussian noise signal :param n: number of samples :return:

## AirPassengers dataset

Monthly totals of a airline passengers from USA, from January 1949 through December 1960.

Source: Hyndman, R.J., Time Series Data Library, http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

`pyFTS.data.AirPassengers.``get_data`()

  Get a simple univariate time series data.

  Returns: | numpy array

`pyFTS.data.AirPassengers.``get_dataframe`()

  Get the complete multivariate time series data.

  Returns: | Pandas DataFrame

## Bitcoin dataset

Bitcoin to USD quotations

Daily averaged index, by business day, from 2010 to 2018.

Source: https://finance.yahoo.com/quote/BTC-USD?p=BTC-USD

`pyFTS.data.Bitcoin.``get_data`(field='AVG')

  Get the univariate time series data.

  Parameters: | field – dataset field to load
  Returns: | numpy array

`pyFTS.data.Bitcoin.``get_dataframe`()

  Get the complete multivariate time series data.

  Returns: | Pandas DataFrame

## DowJones dataset

DJI - Dow Jones

Daily averaged index, by business day, from 1985 to 2017.

Source: https://finance.yahoo.com/quote/%5EGSPC/history?p=%5EGSPC

`pyFTS.data.DowJones.``get_data`(field='AVG')

  Get the univariate time series data.

  Parameters: | field – dataset field to load
  Returns: | numpy array

`pyFTS.data.DowJones.``get_dataframe`()

  Get the complete multivariate time series data.

  Returns: | Pandas DataFrame

## Enrollments dataset

Yearly University of Alabama enrollments from 1971 to 1992.

`pyFTS.data.Enrollments.``get_data`()

  Get a simple univariate time series data.

  Returns: | numpy array

`pyFTS.data.Enrollments.``get_dataframe`()

## Ethereum dataset

Ethereum to USD quotations

Daily averaged index, by business day, from 2016 to 2018.

Source: https://finance.yahoo.com/quote/ETH-USD?p=ETH-USD

`pyFTS.data.Ethereum.``get_data`(field='AVG')

  Get the univariate time series data.

  Parameters: | field – dataset field to load
  Returns: | numpy array

`pyFTS.data.Ethereum.``get_dataframe`()

  Get the complete multivariate time series data.

  Returns: | Pandas DataFrame

## EUR-GBP dataset

FOREX market EUR-GBP pair.

Daily averaged quotations, by business day, from 2016 to 2018.

`pyFTS.data.EURGBP.``get_data`(field='avg')

  Get the univariate time series data.

  Parameters: | field – dataset field to load
  Returns: | numpy array

`pyFTS.data.EURGBP.``get_dataframe`()

  Get the complete multivariate time series data.

  Returns: | Pandas DataFrame

## EUR-USD dataset

FOREX market EUR-USD pair.

Daily averaged quotations, by business day, from 2016 to 2018.

`pyFTS.data.EURUSD.``get_data`(field='avg')

  Get the univariate time series data.

  Parameters: | field – dataset field to load
  Returns: | numpy array

`pyFTS.data.EURUSD.``get_dataframe`()

  Get the complete multivariate time series data.

  Returns: | Pandas DataFrame

## GBP-USD dataset

FOREX market GBP-USD pair.

Daily averaged quotations, by business day, from 2016 to 2018.

`pyFTS.data.GBPUSD.``get_data`(field='avg')

  Get the univariate time series data.

  Parameters: | field – dataset field to load
  Returns: | numpy array

`pyFTS.data.GBPUSD.``get_dataframe`()

  Get the complete multivariate time series data.

  Returns: | Pandas DataFrame

## INMET dataset

INMET - Instituto Nacional Meteorologia / Brasil

Belo Horizonte station, from 2000-01-01 to 31/12/2012

Source: http://www.inmet.gov.br

`pyFTS.data.INMET.``get_dataframe`()

  Get the complete multivariate time series data.

  Returns: | Pandas DataFrame

## Malaysia dataset

Hourly Malaysia eletric load and tempeature

`pyFTS.data.Malaysia.``get_data`(field='load')

  Get the univariate time series data.

  Parameters: | field – dataset field to load
  Returns: | numpy array

`pyFTS.data.Malaysia.``get_dataframe`()

  Get the complete multivariate time series data.

  Returns: | Pandas DataFrame

## NASDAQ module

National Association of Securities Dealers Automated Quotations - Composite Index (NASDAQ IXIC)

Daily averaged index by business day, from 2000 to 2016.

Source: http://www.nasdaq.com/aspx/flashquotes.aspx?symbol=IXIC&selected=IXIC

`pyFTS.data.NASDAQ.``get_data`(field='avg')

  Get a simple univariate time series data.

  Parameters: | field – the dataset field name to extract
  Returns: | numpy array

`pyFTS.data.NASDAQ.``get_dataframe`()

  Get the complete multivariate time series data.

  Returns: | Pandas DataFrame

## SONDA dataset

SONDA - Sistema de Organização Nacional de Dados Ambientais, from INPE - Instituto Nacional de Pesquisas Espaciais, Brasil.

Brasilia station

Source: http://sonda.ccst.inpe.br/

`pyFTS.data.SONDA.``get_data`(field)

  Get a simple univariate time series data.

  Parameters: | field – the dataset field name to extract
  Returns: | numpy array

`pyFTS.data.SONDA.``get_dataframe`()

  Get the complete multivariate time series data.

  Returns: | Pandas DataFrame

## S&P 500 dataset

S&P500 - Standard & Poor’s 500

Daily averaged index, by business day, from 1950 to 2017.

Source: https://finance.yahoo.com/quote/%5EGSPC/history?p=%5EGSPC

`pyFTS.data.SP500.``get_data`()

  Get the univariate time series data.

  Returns: | numpy array

`pyFTS.data.SP500.``get_dataframe`()

  Get the complete multivariate time series data.

  Returns: | Pandas DataFrame

## TAIEX dataset

The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX)

Daily averaged index by business day, from 1995 to 2014.

Source: http://www.twse.com.tw/en/products/indices/Index_Series.php

`pyFTS.data.TAIEX.``get_data`()

  Get the univariate time series data.

  Returns: | numpy array

`pyFTS.data.TAIEX.``get_dataframe`()

  Get the complete multivariate time series data.

  Returns: | Pandas DataFrame

## Henon chaotic time series

13. Hénon. “A two-dimensional mapping with a strange attractor”. Commun. Math. Phys. 50, 69-77 (1976)

dx/dt = a + by(t-1) - x(t-1)^2 dy/dt = x

`pyFTS.data.henon.``get_data`(var, a=1.4, b=0.3, initial_values=[1, 1], iterations=1000)

  Get a simple univariate time series data.

  Parameters: | var – the dataset field name to extract
  Returns: | numpy array

`pyFTS.data.henon.``get_dataframe`(a=1.4, b=0.3, initial_values=[1, 1], iterations=1000)

  Return a dataframe with the bivariate Henon Map time series (x, y).

  Parameters: | a – Equation coefficient b – Equation coefficient initial_values – numpy array with the initial values of x and y. Default: [1, 1] iterations – number of iterations. Default: 1000
  Returns: | Panda dataframe with the x and y values

## Logistic_map chaotic time series

May, Robert M. (1976). “Simple mathematical models with very complicated dynamics”. Nature. 261 (5560): 459–467. doi:10.1038/261459a0.

x(t) = r * x(t-1) * (1 - x(t -1) )

`pyFTS.data.logistic_map.``get_data`(r=4, initial_value=0.3, iterations=100)

  Return a list with the logistic map chaotic time series.

  Parameters: | r – Equation coefficient initial_value – Initial value of x. Default: 0.3 iterations – number of iterations. Default: 100
  Returns: |

## Lorentz chaotic time series

Lorenz, Edward Norton (1963). “Deterministic nonperiodic flow”. Journal of the Atmospheric Sciences. 20 (2): 130–141. https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2

dx/dt = a(y -x) dy/dt = x(b - z) - y dz/dt = xy - cz

`pyFTS.data.lorentz.``get_data`(var, a=10.0, b=28.0, c=2.6666666666666665, dt=0.01, initial_values=[0.1, 0, 0], iterations=1000)

  Get a simple univariate time series data.

  Parameters: | var – the dataset field name to extract
  Returns: | numpy array

`pyFTS.data.lorentz.``get_dataframe`(a=10.0, b=28.0, c=2.6666666666666665, dt=0.01, initial_values=[0.1, 0, 0], iterations=1000)

  Return a dataframe with the multivariate Lorenz Map time series (x, y, z).

  Parameters: | a – Equation coefficient. Default value: 10 b – Equation coefficient. Default value: 28 c – Equation coefficient. Default value: 8.0/3.0 dt – Time differential for continuous time integration. Default value: 0.01 initial_values – numpy array with the initial values of x,y and z. Default: [0.1, 0, 0] iterations – number of iterations. Default: 1000
  Returns: | Panda dataframe with the x, y and z values

## Mackey-Glass chaotic time series

Mackey, M. C. and Glass, L. (1977). Oscillation and chaos in physiological control systems. Science, 197(4300):287-289.

dy/dt = -by(t)+ cy(t - tau) / 1+y(t-tau)^10

`pyFTS.data.mackey_glass.``get_data`(b=0.1, c=0.2, tau=17, initial_values=array([0.5, 0.55882353, 0.61764706, 0.67647059, 0.73529412, 0.79411765, 0.85294118, 0.91176471, 0.97058824, 1.02941176, 1.08823529, 1.14705882, 1.20588235, 1.26470588, 1.32352941, 1.38235294, 1.44117647, 1.5 ]), iterations=1000)

  Return a list with the Mackey-Glass chaotic time series.

  Parameters: | b – Equation coefficient c – Equation coefficient tau – Lag parameter, default: 17 initial_values – numpy array with the initial values of y. Default: np.linspace(0.5,1.5,18) iterations – number of iterations. Default: 1000
  Returns: |

## Rossler chaotic time series

15. 5. Rössler, Phys. Lett. 57A, 397 (1976).

dx/dt = -z - y dy/dt = x + ay dz/dt = b + z( x - c )

`pyFTS.data.rossler.``get_data`(var, a=0.2, b=0.2, c=5.7, dt=0.01, initial_values=[0.001, 0.001, 0.001], iterations=5000)

  Get a simple univariate time series data.

  Parameters: | var – the dataset field name to extract
  Returns: | numpy array

`pyFTS.data.rossler.``get_dataframe`(a=0.2, b=0.2, c=5.7, dt=0.01, initial_values=[0.001, 0.001, 0.001], iterations=5000)

  Return a dataframe with the multivariate Rössler Map time series (x, y, z).

  Parameters: | a – Equation coefficient. Default value: 0.2 b – Equation coefficient. Default value: 0.2 c – Equation coefficient. Default value: 5.7 dt – Time differential for continuous time integration. Default value: 0.01 initial_values – numpy array with the initial values of x,y and z. Default: [0.001, 0.001, 0.001] iterations – number of iterations. Default: 5000
  Returns: | Panda dataframe with the x, y and z values

## Sunspots dataset

Monthly sunspot numbers from 1749 to May 2016

Source: https://www.esrl.noaa.gov/psd/gcos_wgsp/Timeseries/SUNSPOT/

`pyFTS.data.sunspots.``get_data`()

  Get a simple univariate time series data.

  Returns: | numpy array

`pyFTS.data.sunspots.``get_dataframe`()

  Get the complete multivariate time series data.

  Returns: | Pandas DataFrame