6. 循环神经网络（RNN）预测时间序列数据

import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, SimpleRNN
import matplotlib.pyplot as plt

# 生成时间序列数据
def generate_time_series(batch_size, n_steps):
    freq1, freq2, offsets1, offsets2 = np.random.rand(4, batch_size, 1)
    time = np.linspace(0, 1, n_steps)
    series = 0.5 * np.sin((time - offsets1) * (freq1 * 10 + 10))  # wave 1
    series += 0.2 * np.sin((time - offsets2) * (freq2 * 20 + 20))  # + wave 2
    series += 0.1 * (np.random.rand(batch_size, n_steps) - 0.5)  # + noise
    return series[..., np.newaxis].astype(np.float32)

n_steps = 50
series = generate_time_series(10000, n_steps + 1)
X_train, y_train = series[:7000, :n_steps], series[:7000, -1]
X_valid, y_valid = series[7000:9000, :n_steps], series[7000:9000, -1]
X_test, y_test = series[9000:, :n_steps], series[9000:, -1]

# 构建RNN模型
model = Sequential([
    SimpleRNN(20, return_sequences=True, input_shape=[None, 1]),
    SimpleRNN(20),
    Dense(1)
])

# 编译模型
model.compile(optimizer='adam', loss='mse')

# 训练模型
history = model.fit(X_train, y_train, epochs=20, validation_data=(X_valid, y_valid))

# 预测
y_pred = model.predict(X_test)

# 可视化结果
plt.plot(y_test, label='True')
plt.plot(y_pred, label='Predicted')
plt.legend()
plt.show()
  • 1.
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30.
  • 31.
  • 32.
  • 33.
  • 34.
  • 35.
  • 36.
  • 37.
  • 38.
  • 39.
  • 40.
  • 41.
  • 42.