Generate seasons in time series¶

In [1]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from badgers.generators.time_series.seasons import GlobalAdditiveSinusoidalSeasonGenerator

import numpy as np import pandas as pd import matplotlib.pyplot as plt from badgers.generators.time_series.seasons import GlobalAdditiveSinusoidalSeasonGenerator

Setup random generator¶

In [2]:

from numpy.random import default_rng
seed = 0
rng = default_rng(seed)

from numpy.random import default_rng seed = 0 rng = default_rng(seed)

Generate data (gaussian white noise)¶

In [3]:

X = pd.DataFrame(data=rng.normal(loc=0, scale=0.1, size=(100, 2)), columns=['dimension_0', 'dimension_1'])

X = pd.DataFrame(data=rng.normal(loc=0, scale=0.1, size=(100, 2)), columns=['dimension_0', 'dimension_1'])

In [4]:

X.plot(subplots=True)

X.plot(subplots=True)

Out[4]:

array([<Axes: >, <Axes: >], dtype=object)

Add sinusoidal season¶

In [5]:

generator = GlobalAdditiveSinusoidalSeasonGenerator(random_generator=rng)

generator = GlobalAdditiveSinusoidalSeasonGenerator(random_generator=rng)

In [6]:

Xt, _ = generator.generate(X=X, y=None, period=np.array([10,50]))

Xt, _ = generator.generate(X=X, y=None, period=np.array([10,50]))

In [7]:

fig, axes = plt.subplots(2, sharex=True, sharey=True, figsize=(6,6))
X.plot(ax=axes[0])
axes[0].set_title('Original data')
Xt.plot(ax=axes[1])
axes[1].set_title('Transformed data')
plt.tight_layout();

fig, axes = plt.subplots(2, sharex=True, sharey=True, figsize=(6,6)) X.plot(ax=axes[0]) axes[0].set_title('Original data') Xt.plot(ax=axes[1]) axes[1].set_title('Transformed data') plt.tight_layout();

In [ ]: