Generate seasons in time series¶

In [1]:

import badgers
badgers.__version__

import badgers badgers.__version__

Out[1]:

'0.0.14'

In [2]:

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

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

Setup random generator¶

In [3]:

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 [4]:

X = rng.normal(loc=0, scale=0.1, size=(100, 2))

X = rng.normal(loc=0, scale=0.1, size=(100, 2))

In [5]:

fig, axes = plt.subplots(2, 1, sharex=True, figsize=(6,4))
axes[0].plot(X[:, 0])
axes[1].plot(X[:, 1])

fig, axes = plt.subplots(2, 1, sharex=True, figsize=(6,4)) axes[0].plot(X[:, 0]) axes[1].plot(X[:, 1])

Out[5]:

[<matplotlib.lines.Line2D at 0x2c732fa3110>]

Add sinusoidal season¶

In [6]:

generator = GlobalAdditiveSinusoidalSeasonGenerator(random_generator=rng)

generator = GlobalAdditiveSinusoidalSeasonGenerator(random_generator=rng)

In [7]:

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 [8]:

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

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