"""
Seasonal Decomposition by Moving Averages
"""
from statsmodels.compat.pandas import deprecate_kwarg
import numpy as np
import pandas as pd
from pandas.core.nanops import nanmean as pd_nanmean
from statsmodels.tsa._stl import STL
from statsmodels.tools.validation import array_like, PandasWrapper
from statsmodels.tsa.tsatools import freq_to_period
from .filters.filtertools import convolution_filter
__all__ = ['STL', 'seasonal_decompose', 'seasonal_mean', 'DecomposeResult']
def _extrapolate_trend(trend, npoints):
"""
Replace nan values on trend's end-points with least-squares extrapolated
values with regression considering npoints closest defined points.
"""
front = next(i for i, vals in enumerate(trend)
if not np.any(np.isnan(vals)))
back = trend.shape[0] - 1 - next(i for i, vals in enumerate(trend[::-1])
if not np.any(np.isnan(vals)))
front_last = min(front + npoints, back)
back_first = max(front, back - npoints)
k, n = np.linalg.lstsq(
np.c_[np.arange(front, front_last), np.ones(front_last - front)],
trend[front:front_last], rcond=-1)[0]
extra = (np.arange(0, front) * np.c_[k] + np.c_[n]).T
if trend.ndim == 1:
extra = extra.squeeze()
trend[:front] = extra
k, n = np.linalg.lstsq(
np.c_[np.arange(back_first, back), np.ones(back - back_first)],
trend[back_first:back], rcond=-1)[0]
extra = (np.arange(back + 1, trend.shape[0]) * np.c_[k] + np.c_[n]).T
if trend.ndim == 1:
extra = extra.squeeze()
trend[back + 1:] = extra
return trend
@deprecate_kwarg('freq', 'period')
def seasonal_mean(x, period):
"""
Return means for each period in x. period is an int that gives the
number of periods per cycle. E.g., 12 for monthly. NaNs are ignored
in the mean.
"""
return np.array([pd_nanmean(x[i::period], axis=0) for i in range(period)])
[docs]@deprecate_kwarg('freq', 'period')
def seasonal_decompose(x, model="additive", filt=None, period=None,
two_sided=True, extrapolate_trend=0):
"""
Seasonal decomposition using moving averages.
Parameters
----------
x : array_like
Time series. If 2d, individual series are in columns. x must contain 2
complete cycles.
model : {"additive", "multiplicative"}, optional
Type of seasonal component. Abbreviations are accepted.
filt : array_like, optional
The filter coefficients for filtering out the seasonal component.
The concrete moving average method used in filtering is determined by
two_sided.
period : int, optional
Period of the series. Must be used if x is not a pandas object or if
the index of x does not have a frequency. Overrides default
periodicity of x if x is a pandas object with a timeseries index.
two_sided : bool, optional
The moving average method used in filtering.
If True (default), a centered moving average is computed using the
filt. If False, the filter coefficients are for past values only.
extrapolate_trend : int or 'freq', optional
If set to > 0, the trend resulting from the convolution is
linear least-squares extrapolated on both ends (or the single one
if two_sided is False) considering this many (+1) closest points.
If set to 'freq', use `freq` closest points. Setting this parameter
results in no NaN values in trend or resid components.
Returns
-------
DecomposeResult
A object with seasonal, trend, and resid attributes.
See Also
--------
statsmodels.tsa.filters.bk_filter.bkfilter
Baxter-King filter.
statsmodels.tsa.filters.cf_filter.cffilter
Christiano-Fitzgerald asymmetric, random walk filter.
statsmodels.tsa.filters.hp_filter.hpfilter
Hodrick-Prescott filter.
statsmodels.tsa.filters.convolution_filter
Linear filtering via convolution.
statsmodels.tsa.seasonal.STL
Season-Trend decomposition using LOESS.
Notes
-----
This is a naive decomposition. More sophisticated methods should
be preferred.
The additive model is Y[t] = T[t] + S[t] + e[t]
The multiplicative model is Y[t] = T[t] * S[t] * e[t]
The seasonal component is first removed by applying a convolution
filter to the data. The average of this smoothed series for each
period is the returned seasonal component.
"""
pfreq = period
pw = PandasWrapper(x)
if period is None:
pfreq = getattr(getattr(x, 'index', None), 'inferred_freq', None)
x = array_like(x, 'x', maxdim=2)
nobs = len(x)
if not np.all(np.isfinite(x)):
raise ValueError("This function does not handle missing values")
if model.startswith('m'):
if np.any(x <= 0):
raise ValueError("Multiplicative seasonality is not appropriate "
"for zero and negative values")
if period is None:
if pfreq is not None:
pfreq = freq_to_period(pfreq)
period = pfreq
else:
raise ValueError("You must specify a period or x must be a "
"pandas object with a DatetimeIndex with "
"a freq not set to None")
if x.shape[0] < 2 * pfreq:
raise ValueError('x must have 2 complete cycles requires {0} '
'observations. x only has {1} '
'observation(s)'.format(2 * pfreq, x.shape[0]))
if filt is None:
if period % 2 == 0: # split weights at ends
filt = np.array([.5] + [1] * (period - 1) + [.5]) / period
else:
filt = np.repeat(1. / period, period)
nsides = int(two_sided) + 1
trend = convolution_filter(x, filt, nsides)
if extrapolate_trend == 'freq':
extrapolate_trend = period - 1
if extrapolate_trend > 0:
trend = _extrapolate_trend(trend, extrapolate_trend + 1)
if model.startswith('m'):
detrended = x / trend
else:
detrended = x - trend
period_averages = seasonal_mean(detrended, period)
if model.startswith('m'):
period_averages /= np.mean(period_averages, axis=0)
else:
period_averages -= np.mean(period_averages, axis=0)
seasonal = np.tile(period_averages.T, nobs // period + 1).T[:nobs]
if model.startswith('m'):
resid = x / seasonal / trend
else:
resid = detrended - seasonal
results = []
for s, name in zip((seasonal, trend, resid, x),
('seasonal', 'trend', 'resid', None)):
results.append(pw.wrap(s.squeeze(), columns=name))
return DecomposeResult(seasonal=results[0], trend=results[1],
resid=results[2], observed=results[3])
[docs]class DecomposeResult(object):
"""
Results class for seasonal decompositions
Parameters
----------
observed : array_like
The data series that has been decomposed.
seasonal : array_like
The seasonal component of the data series.
trend : array_like
The trend component of the data series.
resid : array_like
The residual component of the data series.
weights : array_like, optional
The weights used to reduce outlier influence.
"""
def __init__(self, observed, seasonal, trend, resid, weights=None):
self._seasonal = seasonal
self._trend = trend
if weights is None:
weights = np.ones_like(observed)
if isinstance(observed, pd.Series):
weights = pd.Series(weights, index=observed.index,
name='weights')
self._weights = weights
self._resid = resid
self._observed = observed
@property
def observed(self):
"""Observed data"""
return self._observed
@property
def seasonal(self):
"""The estimated seasonal component"""
return self._seasonal
@property
def trend(self):
"""The estimated trend component"""
return self._trend
@property
def resid(self):
"""The estimated residuals"""
return self._resid
@property
def weights(self):
"""The weights used in the robust estimation"""
return self._weights
@property
def nobs(self):
"""Number of observations"""
return self._observed.shape
[docs] def plot(self, observed=True, seasonal=True, trend=True, resid=True,
weights=False):
"""
Plot estimated components
Parameters
----------
observed : bool
Include the observed series in the plot
seasonal : bool
Include the seasonal component in the plot
trend : bool
Include the trend component in the plot
resid : bool
Include the residual in the plot
weights : bool
Include the weights in the plot (if any)
Returns
-------
matplotlib.figure.Figure
The figure instance that containing the plot.
"""
from statsmodels.graphics.utils import _import_mpl
from pandas.plotting import register_matplotlib_converters
plt = _import_mpl()
register_matplotlib_converters()
series = [(self._observed, 'Observed')] if observed else []
series += [(self.trend, 'trend')] if trend else []
series += [(self.seasonal, 'seasonal')] if seasonal else []
series += [(self.resid, 'residual')] if resid else []
series += [(self.weights, 'weights')] if weights else []
if isinstance(self._observed, (pd.DataFrame, pd.Series)):
nobs = self._observed.shape[0]
xlim = self._observed.index[0], self._observed.index[nobs - 1]
else:
xlim = (0, self._observed.shape[0] - 1)
fig, axs = plt.subplots(len(series), 1)
for i, (ax, (series, def_name)) in enumerate(zip(axs, series)):
if def_name != 'residual':
ax.plot(series)
else:
ax.plot(series, marker='o', linestyle='none')
ax.plot(xlim, (0, 0), color='#000000', zorder=-3)
name = getattr(series, 'name', def_name)
if def_name != 'Observed':
name = name.capitalize()
title = ax.set_title if i == 0 and observed else ax.set_ylabel
title(name)
ax.set_xlim(xlim)
fig.tight_layout()
return fig