def decompose(x, type='additive'):
'''
Performs a classical seasonal decomposition of a time series into
season, trend and remainder components.
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().
The series should be seasonal.
type: Type of seasonal decomposition to perform.
Default is 'additive', other option is 'multiplicative'.
Returns:
If x is an R ts object, an R object of class 'decomposed.ts' is returned.
If x is a Pandas Series, a Pandas Data Frame is returned.
'''
x, is_pandas = converters.to_ts(x)
out = stats.decompose(x, type=type)
return converters.decomposition_out(out, is_pandas)
def decompose(x, type='additive'):
'''
Performs a classical seasonal decomposition of a time series into
season, trend and remainder components.
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().
The series should be seasonal.
type: Type of seasonal decomposition to perform.
Default is 'additive', other option is 'multiplicative'.
Returns:
If x is an R ts object, an R object of class 'decomposed.ts' is returned.
If x is a Pandas Series, a Pandas Data Frame is returned.
'''
x, is_pandas = converters.to_ts(x)
out = stats.decompose(x, type=type)
return converters.decomposition_out(out, is_pandas)
def stl(x, s_window, **kwargs):
'''
Perform a decomposition of the time series x into seasonal, trend and
remainder components using loess. Most of the arguments listed below are
in **kwargs, and all of those arguments have sensible defaults. Usually
only the mandatory s_window paramter has to be set.
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().
s_window : either 'periodic' or the span (in lags) of the
loess window for seasonal extraction, which should be odd.
This has no default.
s_degree : Default 0, should be 0 or 1. Degree of local polynomial
for seasonal extraction.
t_window : The span (in lags) of the loess window for trend extraction,
which should be odd. Default is a sensible, data-dependent value.
See the R docs for the details.
t_degree : Default 0, should be 0 or 1. Degree of local polynomial
for trend extraction.
l_window : Span in lags of the loess window used to low-pass filter each
seasonal subseries. The default is first odd number greater than or
equal to frequency, which is recommmended.
s_jump, t_jump, l_jump : integer parameters (min. 1) to increase speed of
each smoother by skipping data points.
l_degree : Default is t.window, must be 0 or 1. Degree of local polynomial
for subseries low-pass filter.
robust : Default is False. If True, robust loess fitting used.
inner : number of backfitting iterations
outer : number of outer robustness iterations
na_action : Default is na.fail, which means that the user has to fill or
remove any missing values. If used, it must be an object that maps to
an R function, obtained from rpy2.
Returns:
If x is an R ts object, an R object of class 'stl' is returned.
If x is a Pandas Series, a Pandas Data Frame is returned.
'''
x, is_pandas = converters.to_ts(x)
kwargs['s.window'] = s_window
kwargs = converters.translate_kwargs(**kwargs)
out = stats.stl(x, **kwargs)
return converters.decomposition_out(out, is_pandas)
def stl(x, s_window, **kwargs):
'''
Perform a decomposition of the time series x into seasonal, trend and
remainder components using loess. Most of the arguments listed below are
in **kwargs, and all of those arguments have sensible defaults. Usually
only the mandatory s_window paramter has to be set.
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().
s_window : either 'periodic' or the span (in lags) of the
loess window for seasonal extraction, which should be odd.
This has no default.
s_degree : Default 0, should be 0 or 1. Degree of local polynomial
for seasonal extraction.
t_window : The span (in lags) of the loess window for trend extraction,
which should be odd. Default is a sensible, data-dependent value.
See the R docs for the details.
t_degree : Default 0, should be 0 or 1. Degree of local polynomial
for trend extraction.
l_window : Span in lags of the loess window used to low-pass filter each
seasonal subseries. The default is first odd number greater than or
equal to frequency, which is recommmended.
s_jump, t_jump, l_jump : integer parameters (min. 1) to increase speed of
each smoother by skipping data points.
l_degree : Default is t.window, must be 0 or 1. Degree of local polynomial
for subseries low-pass filter.
robust : Default is False. If True, robust loess fitting used.
inner : number of backfitting iterations
outer : number of outer robustness iterations
na_action : Default is na.fail, which means that the user has to fill or
remove any missing values. If used, it must be an object that maps to
an R function, obtained from rpy2.
Returns:
If x is an R ts object, an R object of class 'stl' is returned.
If x is a Pandas Series, a Pandas Data Frame is returned.
'''
x, is_pandas = converters.to_ts(x)
kwargs['s.window'] = s_window
kwargs = converters.translate_kwargs(**kwargs)
out = stats.stl(x, **kwargs)
return converters.decomposition_out(out, is_pandas)
