Implement series-to-series transformers

[mloning]

Describe the solution you'd like
Here's a simple example of a series-to-series log transformer transformer:

All of these transforms are series-to-series transforms. For an overview of different kinds of transformations, see this document.

import numpy as np
import pandas as pd

from sktime.transformers.base import _SeriesToSeriesTransformer
from sktime.utils.validation.series import 

class LogTransformer(_SeriesToSeriesTransformer):

    _tags = {"transform-returns-same-time-index": True}

    def transform(self, Z, X=None):
        self.check_is_fitted()
        Z = check_series(Z)
        return np.log(Z)

    def inverse_transform(self, Z, X=None):
        self.check_is_fitted()
        Z = check_series(Z)
        return np.exp(Z)

List of transformations we'd like to include in sktime

Transform	Package/Function
FFT/DFT	https://docs.scipy.org/doc/numpy-1.15.0/reference/routines.fft.html
Autocorrelation function (ACF)	https://www.statsmodels.org/0.9.0/generated/statsmodels.tsa.stattools.acf.html
Partial autocorrelation function (autoregressive coefficients) (PACF)	https://www.statsmodels.org/0.9.0/generated/statsmodels.tsa.stattools.pacf.html
Auto Regressive Moving Average (ARMA)	https://www.statsmodels.org/0.9.0/generated/statsmodels.tsa.arima_model.ARMA.html
Power Spectrum	https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.signal.periodogram.html
Cepstrum	https://github.com/python-acoustics/python-acoustics/blob/master/acoustics/cepstrum.py
Cosine	https://docs.scipy.org/doc/numpy/reference/generated/numpy.cos.html

Related issues
see #6, #224 and #297

[mloning]

Hi @Afzal-Ind, could you tell me which part isn't clear? You can message me here or on Gitter.

[afzal442]

Could you tell me which part isn't clear?

Thanks. I am not clear about series-to-series log transformer is how it works basically.

[mloning]

@Afzal-Ind There is an example in the linked document. Does that make it clearer?

[afzal442]

Does that make it clearer?

Thanks again. It may take some time but I will keep up with this.