Generate noise in time series¶

In [1]:

import numpy as np
import matplotlib.pyplot as plt
from badgers.generators.time_series.noise import LocalGaussianNoiseGenerator, GlobalGaussianNoiseGenerator
import matplotlib.patches as patches

import numpy as np import matplotlib.pyplot as plt from badgers.generators.time_series.noise import LocalGaussianNoiseGenerator, GlobalGaussianNoiseGenerator import matplotlib.patches as patches

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)

Import data (using sktime)¶

In [3]:

from sktime.datasets import load_airline

from sktime.datasets import load_airline

In [4]:

X = load_airline()
t = X.index.to_timestamp()

X = load_airline() t = X.index.to_timestamp()

In [5]:

plt.plot(t,X)

plt.plot(t,X)

Out[5]:

[<matplotlib.lines.Line2D at 0x21e3b5cd550>]

Adding Gaussian white noise on the full time series¶

In [6]:

generator = GlobalGaussianNoiseGenerator(random_generator=rng)

generator = GlobalGaussianNoiseGenerator(random_generator=rng)

In [7]:

Xt, _ = generator.generate(X.copy(), y=None, noise_std=0.25)

Xt, _ = generator.generate(X.copy(), y=None, noise_std=0.25)

In [8]:

fig, axes = plt.subplots(2, sharex=True, sharey=True, figsize=(6,6))
axes[0].plot(t, X.values)
axes[0].set_title('Original data')
axes[1].plot(t, 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(t, X.values) axes[0].set_title('Original data') axes[1].plot(t, Xt) axes[1].set_title('Transformed data') plt.tight_layout();

Adding Gaussian white noise on randomly chosen subsequences¶

In [9]:

generator = LocalGaussianNoiseGenerator(random_generator=rng)

generator = LocalGaussianNoiseGenerator(random_generator=rng)

In [10]:

Xt, _ = generator.generate(X.copy(), y=None, n_patterns=5, noise_std=0.25, min_width_pattern=8, max_width_patterns=12)

Xt, _ = generator.generate(X.copy(), y=None, n_patterns=5, noise_std=0.25, min_width_pattern=8, max_width_patterns=12)

In [11]:

fig, axes = plt.subplots(2, sharex=True, sharey=True, figsize=(6,6))
axes[0].plot(t, X.values)
axes[0].set_title('Original data')
axes[1].plot(t, Xt)
axes[1].set_title('Transformed data')
# show where the patterns are located
bottom = np.min(Xt)
height = np.max(Xt) - np.min(Xt)
for start, end in generator.patterns_indices_:
    width = end-start
    left = X.index[start]
    rect = plt.Rectangle((left, bottom), width, height,
                         facecolor="red", alpha=0.1)
    axes[1].add_patch(rect)
plt.tight_layout();

fig, axes = plt.subplots(2, sharex=True, sharey=True, figsize=(6,6)) axes[0].plot(t, X.values) axes[0].set_title('Original data') axes[1].plot(t, Xt) axes[1].set_title('Transformed data') # show where the patterns are located bottom = np.min(Xt) height = np.max(Xt) - np.min(Xt) for start, end in generator.patterns_indices_: width = end-start left = X.index[start] rect = plt.Rectangle((left, bottom), width, height, facecolor="red", alpha=0.1) axes[1].add_patch(rect) plt.tight_layout();

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