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()
