Generate patterns in time series¶
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import numpy as np
import matplotlib.pyplot as plt
from badgers.generators.time_series.patterns import RandomConstantPatterns, RandomLinearPatterns
import matplotlib.patches as patches
import numpy as np
import matplotlib.pyplot as plt
from badgers.generators.time_series.patterns import RandomConstantPatterns, RandomLinearPatterns
import matplotlib.patches as patches
Import data (using sktime)¶
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from sktime.datasets import load_airline
from sktime.datasets import load_airline
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X = load_airline()
t = X.index.to_timestamp()
X = load_airline()
t = X.index.to_timestamp()
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plt.plot(t, X.values)
plt.plot(t, X.values)
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[<matplotlib.lines.Line2D at 0x1303623ccd0>]
Randomly generate patterns (subsequences) with constant values¶
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generator = RandomConstantPatterns(n_patterns=5, patterns_width=5, constant_value=0)
generator = RandomConstantPatterns(n_patterns=5, patterns_width=5, constant_value=0)
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Xt, _ = generator.generate(X.copy().values.reshape(-1, 1), None)
Xt, _ = generator.generate(X.copy().values.reshape(-1, 1), None)
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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();
Generate patterns with constant slope (linear)¶
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generator = RandomLinearPatterns(n_patterns=5, patterns_width=5)
generator = RandomLinearPatterns(n_patterns=5, patterns_width=5)
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Xt, _ = generator.generate(X.copy().values.reshape(-1, 1), None)
Xt, _ = generator.generate(X.copy().values.reshape(-1, 1), None)
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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();
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