Stationarity

Example using your package

(4.4, 0.95, {'10%': 1.2888, '5%': 1.1412, '2.5%': 1.0243, '1%': 0.907})

Series:
stationarity-adf_ct_statistic                      -2.58
stationarity-adf_ct_pvalue                        0.2892
stationarity-adf_ct_nlags                              0
stationarity-adf_ct_nobs                              99
stationarity-adf_ct_criticalvalue_1%             -4.0533
stationarity-adf_ct_criticalvalue_5%             -3.4558
stationarity-adf_ct_criticalvalue_10%            -3.1536
stationarity-adf_c_statistic                     -0.8462
stationarity-adf_c_pvalue                         0.8052
stationarity-adf_c_nlags                               1
stationarity-adf_c_nobs                               98
stationarity-adf_c_criticalvalue_1%              -3.4989
stationarity-adf_c_criticalvalue_5%              -2.8915
stationarity-adf_c_criticalvalue_10%             -2.5828
stationarity-kpss_ct_statistic                    0.1709
stationarity-kpss_ct_pvalue                       0.0292
stationarity-kpss_ct_nlags                             5
stationarity-kpss_ct_criticalvalue_10%             0.119
stationarity-kpss_ct_criticalvalue_5%              0.146
stationarity-kpss_ct_criticalvalue_2.5%            0.176
stationarity-kpss_ct_criticalvalue_1%              0.216
stationarity-kpss_c_statistic                     1.5602
stationarity-kpss_c_pvalue                          0.01
stationarity-kpss_c_nlags                              5
stationarity-kpss_c_criticalvalue_10%              0.347
stationarity-kpss_c_criticalvalue_5%               0.463
stationarity-kpss_c_criticalvalue_2.5%             0.574
stationarity-kpss_c_criticalvalue_1%               0.739
stationarity-root_ct_stationary                    False
stationarity-root_c_stationary                     False
stationarity-trend_ct_stationary                   False
stationarity-trend_c_stationary                    False
stationarity-rur_statistic                           4.4
stationarity-rur_pvalue                             0.95
stationarity-rur_criticalvalue_10%                1.2888
stationarity-rur_criticalvalue_5%                 1.1412
stationarity-rur_criticalvalue_2.5%               1.0243
stationarity-rur_criticalvalue_1%                  0.907
stationarity-model                         {'x': [100...
stationarity-id                             Stationarity
dtype: object

Identifier:
Stationarity
(0.9, 0.01, {'10%': 1.2888, '5%': 1.1412, '2.5%': 1.0243, '1%': 0.907})
(9.9, 0.95, {'10%': 1.2888, '5%': 1.1412, '2.5%': 1.0243, '1%': 0.907})
(5.9, 0.95, {'10%': 1.2888, '5%': 1.1412, '2.5%': 1.0243, '1%': 0.907})
(4.4, 0.95, {'10%': 1.2888, '5%': 1.1412, '2.5%': 1.0243, '1%': 0.907})
(2.2, 0.9, {'10%': 1.2888, '5%': 1.1412, '2.5%': 1.0243, '1%': 0.907})

# Libraries
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt

# Import pyAMR
from pyamr.datasets.load import make_timeseries
from pyamr.core.stats.stationarity import StationarityWrapper

# ----------------------------
# set basic configuration
# ----------------------------
# Set pandas configuration.
pd.set_option('display.max_colwidth', 14)
pd.set_option('display.width', 150)
pd.set_option('display.precision', 4)

# Set default parameters.
mpl.rc('lines', linewidth=0.35)
mpl.rc('xtick', labelsize=6)
mpl.rc('ytick', labelsize=6)
mpl.rc('legend', fontsize=6)
mpl.rc('grid')
mpl.rc('figure')
mpl.rc('axes')
mpl.rc('font', size=7)

# Font type.
font = {
'family': 'monospace',
'weight': 'normal',
'size': 6,
}

# ----------------------------
# create data
# ----------------------------
# Constants
length = 100
offset = 100
slope = 4

# Create variables.
x = np.arange(length)
n = np.random.rand(length)

# Create timeseries.
y_n = n
y_c = np.ones(length)*offset
y_t = x*slope+n
y_ct = x*slope+offset+n*20
y_r = np.concatenate((y_ct[:50], y_ct[50:]-offset))
x_s, y_s, f_s = make_timeseries()

# ----------------------------
# Example of stationarity
# ----------------------------
# Single example
stationarity = StationarityWrapper()\
    .fit(y_r, adf_kwargs={}, kpss_kwargs={})

# Show
print("\nSeries:")
print(stationarity.as_series())

print("\nIdentifier:")
print(stationarity._identifier())


# ---------------------------
# Plot
# ---------------------------
# .. note:: Including in the timeseries variable the value
#           y_c produces the following error:
#           ValueError: cannot convert float NaN to integer.

# Create array of time series
timeseries = [y_n, y_c, y_t, y_ct, y_r, y_s]
timeseries = [y_n, y_t, y_ct, y_r, y_s]

# Create figure
fig, axes = plt.subplots(3,2, figsize=(10,8))
axes = axes.flatten()

# Loop
for i,ts in enumerate(timeseries):
    #print("Stationarity... %s" % i)

    # Create stationarity wrapper.
    stationarity = StationarityWrapper().fit(x=ts)

    # Plot
    axes[i].plot(ts, color='#A6CEE3', alpha=0.5, marker='o',
                 markeredgecolor='k', markeredgewidth=0.5,
                 markersize=2, linewidth=0.75,
                 label=stationarity.as_summary())

    # Set grid
    axes[i].grid(color='gray', linestyle='--',
                 linewidth=0.2, alpha=0.5)

    # Set legend
    axes[i].legend(prop=font, loc=4)

# Study of Stationarity
plt.suptitle("Study of Stationarity")
plt.tight_layout()

# Show
plt.show()