파이토치로 배우는 LLM & AI

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10

장

시퀀스를 예측하는 머신러닝 모델 만들기

return np.where(season_time < 0.4,

np.cos(season_time * 2 * np.pi),

1 / np.exp(3 * season_time))

def seasonality(time, period, amplitude=1, phase=0):

season_time = ((time + phase) % period) / period

return amplitude * seasonal_pattern(season_time)

def noise(time, noise_level=1, seed=None):

rnd = np.random.RandomState(seed)

return rnd.randn(len(time)) * noise_level

time = np.arange(4 * 365 + 1, dtype="float32")

series = trend(time, 0.1)

baseline = 10

amplitude = 20

slope = 0.09

noise_level = 5

series = baseline + trend(time, slope)

series += seasonality(time, period=365, amplitude=amplitude)

series +=