"""
Run x12/x13-arima specs in a subprocess from Python and curry results back
into python.
Notes
-----
Many of the functions are called x12. However, they are also intended to work
for x13. If this is not the case, it's a bug.
"""
from statsmodels.compat.pandas import deprecate_kwarg
import os
import subprocess
import tempfile
import re
from warnings import warn
import pandas as pd
from statsmodels.compat.python import iteritems
from statsmodels.tools.tools import Bunch
from statsmodels.tools.sm_exceptions import (X13NotFoundError,
IOWarning, X13Error,
X13Warning)
__all__ = ["x13_arima_select_order", "x13_arima_analysis"]
_binary_names = ('x13as.exe', 'x13as', 'x12a.exe', 'x12a')
class _freq_to_period:
def __getitem__(self, key):
if key.startswith('M'):
return 12
elif key.startswith('Q'):
return 4
elif key.startswith('W'):
return 52
_freq_to_period = _freq_to_period()
_period_to_freq = {12: 'M', 4: 'Q'}
_log_to_x12 = {True: 'log', False: 'none', None: 'auto'}
_bool_to_yes_no = lambda x: 'yes' if x else 'no' # noqa:E731
def _find_x12(x12path=None, prefer_x13=True):
"""
If x12path is not given, then either x13as[.exe] or x12a[.exe] must
be found on the PATH. Otherwise, the environmental variable X12PATH or
X13PATH must be defined. If prefer_x13 is True, only X13PATH is searched
for. If it is false, only X12PATH is searched for.
"""
global _binary_names
if x12path is not None and x12path.endswith(_binary_names):
# remove binary from path if given
x12path = os.path.dirname(x12path)
if not prefer_x13: # search for x12 first
_binary_names = _binary_names[::-1]
if x12path is None:
x12path = os.getenv("X12PATH", "")
if not x12path:
x12path = os.getenv("X13PATH", "")
elif x12path is None:
x12path = os.getenv("X13PATH", "")
if not x12path:
x12path = os.getenv("X12PATH", "")
for binary in _binary_names:
x12 = os.path.join(x12path, binary)
try:
subprocess.check_call(x12, stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
return x12
except OSError:
pass
else:
return False
def _check_x12(x12path=None):
x12path = _find_x12(x12path)
if not x12path:
raise X13NotFoundError("x12a and x13as not found on path. Give the "
"path, put them on PATH, or set the "
"X12PATH or X13PATH environmental variable.")
return x12path
def _clean_order(order):
"""
Takes something like (1 1 0)(0 1 1) and returns a arma order, sarma
order tuple. Also accepts (1 1 0) and return arma order and (0, 0, 0)
"""
order = re.findall(r"\([0-9 ]*?\)", order)
def clean(x):
return tuple(map(int, re.sub("[()]", "", x).split(" ")))
if len(order) > 1:
order, sorder = map(clean, order)
else:
order = clean(order[0])
sorder = (0, 0, 0)
return order, sorder
def run_spec(x12path, specpath, outname=None, meta=False, datameta=False):
if meta and datameta:
raise ValueError("Cannot specify both meta and datameta.")
if meta:
args = [x12path, "-m " + specpath]
elif datameta:
args = [x12path, "-d " + specpath]
else:
args = [x12path, specpath]
if outname:
args += [outname]
return subprocess.Popen(args, stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
def _make_automdl_options(maxorder, maxdiff, diff):
options = "\n"
options += "maxorder = ({0} {1})\n".format(maxorder[0], maxorder[1])
if maxdiff is not None: # maxdiff always takes precedence
options += "maxdiff = ({0} {1})\n".format(maxdiff[0], maxdiff[1])
else:
options += "diff = ({0} {1})\n".format(diff[0], diff[1])
return options
def _make_var_names(exog):
if hasattr(exog, "name"):
var_names = exog.name
elif hasattr(exog, "columns"):
var_names = exog.columns
else:
raise ValueError("exog is not a Series or DataFrame or is unnamed.")
try:
var_names = " ".join(var_names)
except TypeError: # cannot have names that are numbers, pandas default
from statsmodels.base.data import _make_exog_names
if exog.ndim == 1:
var_names = "x1"
else:
var_names = " ".join(_make_exog_names(exog))
return var_names
def _make_regression_options(trading, exog):
if not trading and exog is None: # start regression spec
return ""
reg_spec = "regression{\n"
if trading:
reg_spec += " variables = (td)\n"
if exog is not None:
var_names = _make_var_names(exog)
reg_spec += " user = ({0})\n".format(var_names)
reg_spec += " data = ({0})\n".format("\n".join(map(str,
exog.values.ravel().tolist())))
reg_spec += "}\n" # close out regression spec
return reg_spec
def _make_forecast_options(forecast_periods):
if forecast_periods is None:
return ""
forecast_spec = "forecast{\n"
forecast_spec += "maxlead = ({0})\n}}\n".format(forecast_periods)
return forecast_spec
def _check_errors(errors):
errors = errors[errors.find("spc:")+4:].strip()
if errors and 'ERROR' in errors:
raise X13Error(errors)
elif errors and 'WARNING' in errors:
warn(errors, X13Warning)
def _convert_out_to_series(x, dates, name):
"""
Convert x to a DataFrame where x is a string in the format given by
x-13arima-seats output.
"""
from io import StringIO
from pandas import read_csv
out = read_csv(StringIO(x), skiprows=2,
header=None, sep='\t', engine='python')
return out.set_index(dates).rename(columns={1: name})[name]
def _open_and_read(fname):
# opens a file, reads it, and make sure it's closed
with open(fname, 'r') as fin:
fout = fin.read()
return fout
class Spec(object):
@property
def spec_name(self):
return self.__class__.__name__.replace("Spec", "")
def create_spec(self, **kwargs):
spec = """{name} {{
{options}
}}
"""
return spec.format(name=self.spec_name,
options=self.options)
def set_options(self, **kwargs):
options = ""
for key, value in iteritems(kwargs):
options += "{0}={1}\n".format(key, value)
self.__dict__.update({key: value})
self.options = options
class SeriesSpec(Spec):
"""
Parameters
----------
data
appendbcst : bool
appendfcst : bool
comptype
compwt
decimals
modelspan
name
period
precision
to_print
to_save
span
start
title
type
Notes
-----
Rarely used arguments
divpower
missingcode
missingval
saveprecision
trimzero
"""
def __init__(self, data, name='Unnamed Series', appendbcst=False,
appendfcst=False,
comptype=None, compwt=1, decimals=0, modelspan=(),
period=12, precision=0, to_print=[], to_save=[], span=(),
start=(1, 1), title='', series_type=None, divpower=None,
missingcode=-99999, missingval=1000000000):
appendbcst, appendfcst = map(_bool_to_yes_no, [appendbcst,
appendfcst,
])
series_name = "\"{0}\"".format(name[:64]) # trim to 64 characters
title = "\"{0}\"".format(title[:79]) # trim to 79 characters
self.set_options(data=data, appendbcst=appendbcst,
appendfcst=appendfcst, period=period, start=start,
title=title, name=series_name,
)
def pandas_to_series_spec(x):
# from statsmodels.tools.data import _check_period_index
# check_period_index(x)
if hasattr(x, 'columns'): # convert to series
if len(x.columns) > 1:
raise ValueError("Does not handle DataFrame with more than one "
"column")
x = x[x.columns[0]]
data = "({0})".format("\n".join(map(str, x.values.tolist())))
# get periodicity
# get start / first data
# give it a title
try:
period = _freq_to_period[x.index.freqstr]
except (AttributeError, ValueError):
from pandas.tseries.api import infer_freq
period = _freq_to_period[infer_freq(x.index)]
start_date = x.index[0]
if period == 12:
year, stperiod = start_date.year, start_date.month
elif period == 4:
year, stperiod = start_date.year, start_date.quarter
else: # pragma: no cover
raise ValueError("Only monthly and quarterly periods are supported."
" Please report or send a pull request if you want "
"this extended.")
if hasattr(x, 'name'):
name = x.name or "Unnamed Series"
else:
name = 'Unnamed Series'
series_spec = SeriesSpec(data=data, name=name, period=period,
title=name, start="{0}.{1}".format(year,
stperiod))
return series_spec
[docs]@deprecate_kwarg('forecast_years', 'forecast_periods')
def x13_arima_analysis(endog, maxorder=(2, 1), maxdiff=(2, 1), diff=None,
exog=None, log=None, outlier=True, trading=False,
forecast_periods=None, retspec=False,
speconly=False, start=None, freq=None,
print_stdout=False, x12path=None, prefer_x13=True):
"""
Perform x13-arima analysis for monthly or quarterly data.
Parameters
----------
endog : array_like, pandas.Series
The series to model. It is best to use a pandas object with a
DatetimeIndex or PeriodIndex. However, you can pass an array-like
object. If your object does not have a dates index then ``start`` and
``freq`` are not optional.
maxorder : tuple
The maximum order of the regular and seasonal ARMA polynomials to
examine during the model identification. The order for the regular
polynomial must be greater than zero and no larger than 4. The
order for the seasonal polynomial may be 1 or 2.
maxdiff : tuple
The maximum orders for regular and seasonal differencing in the
automatic differencing procedure. Acceptable inputs for regular
differencing are 1 and 2. The maximum order for seasonal differencing
is 1. If ``diff`` is specified then ``maxdiff`` should be None.
Otherwise, ``diff`` will be ignored. See also ``diff``.
diff : tuple
Fixes the orders of differencing for the regular and seasonal
differencing. Regular differencing may be 0, 1, or 2. Seasonal
differencing may be 0 or 1. ``maxdiff`` must be None, otherwise
``diff`` is ignored.
exog : array_like
Exogenous variables.
log : bool or None
If None, it is automatically determined whether to log the series or
not. If False, logs are not taken. If True, logs are taken.
outlier : bool
Whether or not outliers are tested for and corrected, if detected.
trading : bool
Whether or not trading day effects are tested for.
forecast_periods : int
Number of forecasts produced. The default is None.
retspec : bool
Whether to return the created specification file. Can be useful for
debugging.
speconly : bool
Whether to create the specification file and then return it without
performing the analysis. Can be useful for debugging.
start : str, datetime
Must be given if ``endog`` does not have date information in its index.
Anything accepted by pandas.DatetimeIndex for the start value.
freq : str
Must be givein if ``endog`` does not have date information in its
index. Anything accepted by pandas.DatetimeIndex for the freq value.
print_stdout : bool
The stdout from X12/X13 is suppressed. To print it out, set this
to True. Default is False.
x12path : str or None
The path to x12 or x13 binary. If None, the program will attempt
to find x13as or x12a on the PATH or by looking at X13PATH or
X12PATH depending on the value of prefer_x13.
prefer_x13 : bool
If True, will look for x13as first and will fallback to the X13PATH
environmental variable. If False, will look for x12a first and will
fallback to the X12PATH environmental variable. If x12path points
to the path for the X12/X13 binary, it does nothing.
Returns
-------
Bunch
A bunch object containing the listed attributes.
- results : str
The full output from the X12/X13 run.
- seasadj : pandas.Series
The final seasonally adjusted ``endog``.
- trend : pandas.Series
The trend-cycle component of ``endog``.
- irregular : pandas.Series
The final irregular component of ``endog``.
- stdout : str
The captured stdout produced by x12/x13.
- spec : str, optional
Returned if ``retspec`` is True. The only thing returned if
``speconly`` is True.
Notes
-----
This works by creating a specification file, writing it to a temporary
directory, invoking X12/X13 in a subprocess, and reading the output
directory, invoking exog12/X13 in a subprocess, and reading the output
back in.
"""
x12path = _check_x12(x12path)
if not isinstance(endog, (pd.DataFrame, pd.Series)):
if start is None or freq is None:
raise ValueError("start and freq cannot be none if endog is not "
"a pandas object")
endog = pd.Series(endog, index=pd.DatetimeIndex(start=start,
periods=len(endog),
freq=freq))
spec_obj = pandas_to_series_spec(endog)
spec = spec_obj.create_spec()
spec += "transform{{function={0}}}\n".format(_log_to_x12[log])
if outlier:
spec += "outlier{}\n"
options = _make_automdl_options(maxorder, maxdiff, diff)
spec += "automdl{{{0}}}\n".format(options)
spec += _make_regression_options(trading, exog)
spec += _make_forecast_options(forecast_periods)
spec += "x11{ save=(d11 d12 d13) }"
if speconly:
return spec
# write it to a tempfile
# TODO: make this more robust - give the user some control?
ftempin = tempfile.NamedTemporaryFile(delete=False, suffix='.spc')
ftempout = tempfile.NamedTemporaryFile(delete=False)
try:
ftempin.write(spec.encode('utf8'))
ftempin.close()
ftempout.close()
# call x12 arima
p = run_spec(x12path, ftempin.name[:-4], ftempout.name)
p.wait()
stdout = p.stdout.read()
if print_stdout:
print(p.stdout.read())
# check for errors
errors = _open_and_read(ftempout.name + '.err')
_check_errors(errors)
# read in results
results = _open_and_read(ftempout.name + '.out')
seasadj = _open_and_read(ftempout.name + '.d11')
trend = _open_and_read(ftempout.name + '.d12')
irregular = _open_and_read(ftempout.name + '.d13')
finally:
try: # sometimes this gives a permission denied error?
# not sure why. no process should have these open
os.remove(ftempin.name)
os.remove(ftempout.name)
except OSError:
if os.path.exists(ftempin.name):
warn("Failed to delete resource {0}".format(ftempin.name),
IOWarning)
if os.path.exists(ftempout.name):
warn("Failed to delete resource {0}".format(ftempout.name),
IOWarning)
seasadj = _convert_out_to_series(seasadj, endog.index, 'seasadj')
trend = _convert_out_to_series(trend, endog.index, 'trend')
irregular = _convert_out_to_series(irregular, endog.index, 'irregular')
# NOTE: there is not likely anything in stdout that's not in results
# so may be safe to just suppress and remove it
if not retspec:
res = X13ArimaAnalysisResult(observed=endog, results=results,
seasadj=seasadj, trend=trend,
irregular=irregular, stdout=stdout)
else:
res = X13ArimaAnalysisResult(observed=endog, results=results,
seasadj=seasadj, trend=trend,
irregular=irregular, stdout=stdout,
spec=spec)
return res
[docs]@deprecate_kwarg('forecast_years', 'forecast_periods')
def x13_arima_select_order(endog, maxorder=(2, 1), maxdiff=(2, 1), diff=None,
exog=None, log=None, outlier=True, trading=False,
forecast_periods=None,
start=None, freq=None, print_stdout=False,
x12path=None, prefer_x13=True):
"""
Perform automatic seasonal ARIMA order identification using x12/x13 ARIMA.
Parameters
----------
endog : array_like, pandas.Series
The series to model. It is best to use a pandas object with a
DatetimeIndex or PeriodIndex. However, you can pass an array-like
object. If your object does not have a dates index then ``start`` and
``freq`` are not optional.
maxorder : tuple
The maximum order of the regular and seasonal ARMA polynomials to
examine during the model identification. The order for the regular
polynomial must be greater than zero and no larger than 4. The
order for the seasonal polynomial may be 1 or 2.
maxdiff : tuple
The maximum orders for regular and seasonal differencing in the
automatic differencing procedure. Acceptable inputs for regular
differencing are 1 and 2. The maximum order for seasonal differencing
is 1. If ``diff`` is specified then ``maxdiff`` should be None.
Otherwise, ``diff`` will be ignored. See also ``diff``.
diff : tuple
Fixes the orders of differencing for the regular and seasonal
differencing. Regular differencing may be 0, 1, or 2. Seasonal
differencing may be 0 or 1. ``maxdiff`` must be None, otherwise
``diff`` is ignored.
exog : array_like
Exogenous variables.
log : bool or None
If None, it is automatically determined whether to log the series or
not. If False, logs are not taken. If True, logs are taken.
outlier : bool
Whether or not outliers are tested for and corrected, if detected.
trading : bool
Whether or not trading day effects are tested for.
forecast_periods : int
Number of forecasts produced. The default is None.
start : str, datetime
Must be given if ``endog`` does not have date information in its index.
Anything accepted by pandas.DatetimeIndex for the start value.
freq : str
Must be givein if ``endog`` does not have date information in its
index. Anything accepted by pandas.DatetimeIndex for the freq value.
print_stdout : bool
The stdout from X12/X13 is suppressed. To print it out, set this
to True. Default is False.
x12path : str or None
The path to x12 or x13 binary. If None, the program will attempt
to find x13as or x12a on the PATH or by looking at X13PATH or X12PATH
depending on the value of prefer_x13.
prefer_x13 : bool
If True, will look for x13as first and will fallback to the X13PATH
environmental variable. If False, will look for x12a first and will
fallback to the X12PATH environmental variable. If x12path points
to the path for the X12/X13 binary, it does nothing.
Returns
-------
Bunch
A bunch object containing the listed attributes.
- order : tuple
The regular order.
- sorder : tuple
The seasonal order.
- include_mean : bool
Whether to include a mean or not.
- results : str
The full results from the X12/X13 analysis.
- stdout : str
The captured stdout from the X12/X13 analysis.
Notes
-----
This works by creating a specification file, writing it to a temporary
directory, invoking X12/X13 in a subprocess, and reading the output back
in.
"""
results = x13_arima_analysis(endog, x12path=x12path, exog=exog, log=log,
outlier=outlier, trading=trading,
forecast_periods=forecast_periods,
maxorder=maxorder, maxdiff=maxdiff, diff=diff,
start=start, freq=freq, prefer_x13=prefer_x13)
model = re.search("(?<=Final automatic model choice : ).*",
results.results)
order = model.group()
if re.search("Mean is not significant", results.results):
include_mean = False
elif re.search("Constant", results.results):
include_mean = True
else:
include_mean = False
order, sorder = _clean_order(order)
res = Bunch(order=order, sorder=sorder, include_mean=include_mean,
results=results.results, stdout=results.stdout)
return res
class X13ArimaAnalysisResult(object):
def __init__(self, **kwargs):
for key, value in iteritems(kwargs):
setattr(self, key, value)
def plot(self):
from statsmodels.graphics.utils import _import_mpl
plt = _import_mpl()
fig, axes = plt.subplots(4, 1, sharex=True)
self.observed.plot(ax=axes[0], legend=False)
axes[0].set_ylabel('Observed')
self.seasadj.plot(ax=axes[1], legend=False)
axes[1].set_ylabel('Seas. Adjusted')
self.trend.plot(ax=axes[2], legend=False)
axes[2].set_ylabel('Trend')
self.irregular.plot(ax=axes[3], legend=False)
axes[3].set_ylabel('Irregular')
fig.tight_layout()
return fig