def InvBoxCox(x, lam):
'''
Invert a BoxCox transformation. The return value is a timeseries with
values of x transformed back to the original scale
Args:
x: an R time series, obtained from converters.ts(), or a Pandas Series
with the correct index (e.g. from converters.sequence_as_series().
Its values that should be on the scale of a BoxCox transformation
with parameter lambda=lam.
lam: BoxCox transformation parameter.
Returns:
If x is an R ts object, an R time series is returned.
If x is a Pandas Series, a Pandas Series is returned.
'''
x, is_pandas = converters.to_ts(x)
out = fc.InvBoxCox(x, **{'lambda': lam})
return converters.series_out(out, is_pandas)
def InvBoxCox(x, lam):
'''
Invert a BoxCox transformation. The return value is a timeseries with
values of x transformed back to the original scale
Args:
x: an R time series, obtained from converters.ts(), or a Pandas Series
with the correct index (e.g. from converters.sequence_as_series().
Its values that should be on the scale of a BoxCox transformation
with parameter lambda=lam.
lam: BoxCox transformation parameter.
Returns:
If x is an R ts object, an R time series is returned.
If x is a Pandas Series, a Pandas Series is returned.
'''
x, is_pandas = converters.to_ts(x)
out = fc.InvBoxCox(x, **{'lambda' : lam})
return converters.series_out(out, is_pandas)
def BoxCox(x, lam):
'''
Applies a Box-Cox transformation to the data in x. This can stabilize the
variance of x, so that forecast model assumptions are more nearly satisfied.
For x != 0, this is (x^lambda - 1) / lambda.
For x = 0, it is log(x).
Args:
x: an R time series, obtained from converters.ts(), or a Pandas Series
with the correct index (e.g. from converters.sequence_as_series().
lam: BoxCox transformation parameter.
Returns:
If x is an R ts object, an R time series is returned.
If x is a Pandas Series, a Pandas Series is returned.
'''
x, is_pandas = converters.to_ts(x)
out = fc.BoxCox(x, **{'lambda': lam})
return converters.series_out(out, is_pandas)
def BoxCox(x, lam):
'''
Applies a Box-Cox transformation to the data in x. This can stabilize the
variance of x, so that forecast model assumptions are more nearly satisfied.
For x != 0, this is (x^lambda - 1) / lambda.
For x = 0, it is log(x).
Args:
x: an R time series, obtained from converters.ts(), or a Pandas Series
with the correct index (e.g. from converters.sequence_as_series().
lam: BoxCox transformation parameter.
Returns:
If x is an R ts object, an R time series is returned.
If x is a Pandas Series, a Pandas Series is returned.
'''
x, is_pandas = converters.to_ts(x)
out = fc.BoxCox(x, **{'lambda' : lam})
return converters.series_out(out, is_pandas)
def tsclean(x, **kwargs):
'''
Identify and replace outliers. Uses loess for non-seasonal series and
an STL decomposition for seasonal series. Optionally fills missing values.
Args:
x: an R time series, obtained from converters.ts(), or a Pandas Series
with the correct index (e.g. from converters.sequence_as_series().
replace_missing: Default True.
If True, use na_interp to fill missing values in x.
lam: optional BoxCox transformation parameter.
Returns:
If x is an R ts object, an R time series is returned. If x is a Pandas
Series, a Pandas Series is returned. In either case, outliers are replaced
and optionally, missing values are filled.
'''
x, is_pandas = converters.to_ts(x)
kwargs = converters.translate_kwargs(**kwargs)
out = fc.tsclean(x, **kwargs)
return converters.series_out(out, is_pandas)
def tsclean(x, **kwargs):
'''
Identify and replace outliers. Uses loess for non-seasonal series and
an STL decomposition for seasonal series. Optionally fills missing values.
Args:
x: an R time series, obtained from converters.ts(), or a Pandas Series
with the correct index (e.g. from converters.sequence_as_series().
replace_missing: Default True.
If True, use na_interp to fill missing values in x.
lam: optional BoxCox transformation parameter.
Returns:
If x is an R ts object, an R time series is returned. If x is a Pandas
Series, a Pandas Series is returned. In either case, outliers are replaced
and optionally, missing values are filled.
'''
x, is_pandas = converters.to_ts(x)
kwargs = converters.translate_kwargs(**kwargs)
out = fc.tsclean(x, **kwargs)
return converters.series_out(out, is_pandas)
def na_interp(x, lam=NULL):
'''
Funtction for interpolating missing values in R time series. This function
uses linear interpolation for non-seasonal data. For seasonal data, it
uses an STL decomposition, imputing the seasonal value.
Args:
x: an R time series, obtained from converters.ts(), or a Pandas Series
with the correct index (e.g. from converters.sequence_as_series().
If lam is used, its values should be on the scale of a BoxCox
transformation with parameter lambda=lam.
lam: BoxCox transformation parameter. The default is R's NULL value.
If NULL, no transformation is applied. Otherwise, a Box-Cox
transformation is applied before forecasting and inverted after.
Returns:
If x is an R ts object, an R time series is returned.
If x is a Pandas Series, a Pandas Series is returned.
In either case, missing values are filled.
'''
x, is_pandas = converters.to_ts(x)
out = fc.na_interp(x, **{'lambda': lam})
return converters.series_out(out, is_pandas)
def na_interp(x, lam=NULL):
'''
Funtction for interpolating missing values in R time series. This function
uses linear interpolation for non-seasonal data. For seasonal data, it
uses an STL decomposition, imputing the seasonal value.
Args:
x: an R time series, obtained from converters.ts(), or a Pandas Series
with the correct index (e.g. from converters.sequence_as_series().
If lam is used, its values should be on the scale of a BoxCox
transformation with parameter lambda=lam.
lam: BoxCox transformation parameter. The default is R's NULL value.
If NULL, no transformation is applied. Otherwise, a Box-Cox
transformation is applied before forecasting and inverted after.
Returns:
If x is an R ts object, an R time series is returned.
If x is a Pandas Series, a Pandas Series is returned.
In either case, missing values are filled.
'''
x, is_pandas = converters.to_ts(x)
out = fc.na_interp(x, **{'lambda' : lam})
return converters.series_out(out, is_pandas)
def read_ts(ts_name, pkgname=None, as_pandas=True):
'''
Function reads a time series in from R. If needed, it can load a package
containing the time series. The output can be provided as an R object or
as a Pandas Series.
Args:
ts_name: the name of the time series in R
pkgname: Default None. The name of an R package with the time series.
as_pandas: Default True. If true, return a Pandas Series.
Returns:
the time series as an R time series or a Pandas Series
'''
if pkgname is not None:
try:
importr(pkgname)
except RRuntimeError:
raise IOError('Package %s not found in R.' % pkgname)
try:
tsout = robjects.r(ts_name)
except RRuntimeError:
raise IOError('Time series %s not found in R.' % ts_name)
return converters.series_out(tsout, as_pandas)
