# seasonality tests
from pmdarima.arima.seasonality import CHTest, decompose, OCSBTest
from pmdarima.arima.utils import nsdiffs
from pmdarima.compat.pytest import pytest_error_str
from pmdarima.datasets import \
load_airpassengers, load_ausbeer, load_austres, load_wineind
import numpy as np
from numpy.testing import assert_almost_equal, assert_array_equal
from sklearn.utils.validation import check_random_state
import pytest
from unittest import mock
airpassengers = load_airpassengers()
austres = load_austres()
ausbeer = load_ausbeer()
wineind = load_wineind()
# change the length to be longer so we can actually test the large case
aus_list = austres.tolist() # type: list
austres_long = np.asarray(aus_list * 10) # type: np.ndarray
@pytest.mark.parametrize('x,type_,m,filter_', [
pytest.param(ausbeer, 'additive', 4, None),
pytest.param(airpassengers, 'multiplicative', 12, None),
pytest.param(wineind, 'additive', 12, None),
pytest.param(np.array([1., 2., 3., 4., 5., 6.]), 'additive', 3, None)
])
#1,2,3; 2,3,4 .... ma3 = data.rolling(window=3, center=True).mean() ma3.head() pd.concat([data, ma3], axis=1) ma3.plot(figsize=(10,7)) fig, ax = plt.subplots(figsize=(10,7)) ax.plot(data) ax.plot(ma3) plt.show(); #different combinations of windows #%%% Exponential Smoothening import pmdarima.datasets as pm data2= pm.load_airpassengers(True) data2 from statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt fit2 = SimpleExpSmoothing(np.asarray(data)).fit(smoothing_level=0.6,optimized=False) data.tail(6) fit2.forecast(5) np.vstack([data.tail(10), fit2.forecast(10)]) #%%% fit3 = ExponentialSmoothing(np.asarray(data) ,seasonal_periods=7 , trend='add', seasonal='add',).fit() fit3.forecast(5)
head_index = 17 * 4 + 2
tail_index = 17 * 4 - 4
first_index = head_index - tail_index
last_index = head_index
ausbeer = datasets.load_ausbeer()
timeserie_beer = ausbeer[first_index:last_index]
decomposed = arima.decompose(timeserie_beer, 'additive', m=4)
# Plot the decomposed signal of ausbeer as a subplot
axes = utils.decomposed_plot(decomposed,
figure_kwargs=figure_kwargs,
show=False)
axes[0].set_title("Ausbeer Seasonal Decomposition")
#
# MULTIPLICATIVE EXAMPLE: airpassengers
#
# Decompose the airpassengers dataset into trend, seasonal and random parts.
decomposed = arima.decompose(datasets.load_airpassengers(),
'multiplicative',
m=12)
# Plot the decomposed signal of airpassengers as a subplot
axes = utils.decomposed_plot(decomposed,
figure_kwargs=figure_kwargs,
show=False)
axes[0].set_title("Airpassengers Seasonal Decomposition")
