# -*- coding: utf-8 -*-
from pmdarima.datasets import load_lynx
from pmdarima.arima import ARIMA
from unittest.mock import patch
import pytest
lynx = load_lynx()
class MockMPLFigure:
def __init__(self, fig, figsize):
self.fig = fig
self.figsize = figsize
self.subplots = []
def add_subplot(self, *args):
ax = MockMPLAxis(*args)
self.subplots.append(ax)
return ax
class MockMPLAxis:
def __init__(self, *args):
pass
def hist(self, *args, **kwargs):
pass
def hlines(self, *args, **kwargs):
# x = [ -6., -2., 7., 25.] (4-10), (2-4), (9-2), (34-9) x # 2 x_lag = x[1:] # second lag x_lag x[:-1] x = x_lag - x[:-1] # x = [ 4., 9., 18.] (-2 - (-6)), (7 - (-2)), (18-7) #check this #%%% Stationary import pmdarima as pm from pmdarima import datasets y = datasets.load_lynx() pm.plot_acf(y) from pmdarima.arima.stationarity import ADFTest # Test whether we should difference at the alpha=0.05 # significance level adf_test = ADFTest(alpha=0.05) p_val, should_diff = adf_test.should_diff(y) # (0.01, False) p_val #The verdict, per the ADF test, is that we should not difference. Pmdarima also provides a more handy interface for estimating your d parameter more directly. This is the preferred public method for accessing tests of stationarity: from pmdarima.arima.utils import ndiffs # Estimate the number of differences using an ADF test: n_adf = ndiffs(y, test='adf') # -> 0
.. raw:: html
<br/>
"""
print(__doc__)
# Author: Taylor Smith <*****@*****.**>
from pmdarima.datasets import load_lynx
from pmdarima.arima import auto_arima
import matplotlib.pyplot as plt
import numpy as np
# #############################################################################
# Load the data and split it into separate pieces
data = load_lynx()
train, test = data[:100], data[100:]
# #############################################################################
# Fit with some validation (cv) samples
arima = auto_arima(train,
start_p=1,
start_q=1,
d=0,
max_p=5,
max_q=5,
out_of_sample_size=10,
suppress_warnings=True,
stepwise=True,
error_action='ignore')
# %% modelo_busca2.fit(cresc_p1['Preco'].values) # %% valores_preditos2 = modelo_busca2.predict(n_periods=10) # %% plt.plot(valores_preditos2) plt.plot(np.linspace(0, 9, 10), cresc_p2['Preco']) # %% from pmdarima.datasets import load_lynx # %% dado_lynx = load_lynx() # %% dado_lynx.shape # %% from pmdarima import model_selection # %% treino, teste = model_selection.train_test_split(dado_lynx, train_size=100) # %% teste1 = teste[:10] teste2 = teste[10:] # %%
