Ceemdan-lstm代码

以下是使用Ceemdan和LSTM结合的代码示例：

import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM
import ceemdan

# 准备数据
data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
n_steps = 3

# 将数据转换成监督学习问题
X, y = list(), list()
for i in range(len(data)):
    end_ix = i + n_steps
    if end_ix > len(data)-1:
        break
    seq_x, seq_y = data[i:end_ix], data[end_ix]
    X.append(seq_x)
    y.append(seq_y)
X, y = np.array(X), np.array(y)
X = X.reshape((X.shape[0], X.shape[1], 1))

# 将数据进行CEEMDAN分解
emd = ceemdan.ceemdan(data, num_sifts=4)
imfs = emd.get_imfs_and_residue()

# 将每个IMF转换成监督学习问题
X_emd, y_emd = list(), list()
for i in range(len(imfs)):
    emd_data = imfs[i]
    for j in range(len(emd_data)):
        end_ix = j + n_steps
        if end_ix > len(emd_data)-1:
            break
        seq_x, seq_y = emd_data[j:end_ix], emd_data[end_ix]
        X_emd.append(seq_x)
        y_emd.append(seq_y)
X_emd, y_emd = np.array(X_emd), np.array(y_emd)
X_emd = X_emd.reshape((X_emd.shape[0], X_emd.shape[1], 1))

# 训练模型
model = Sequential()
model.add(LSTM(50, activation='relu', input_shape=(n_steps, 1)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
model.fit(X, y, epochs=200, verbose=0)

model_emd = Sequential()
model_emd.add(LSTM(50, activation='relu', input_shape=(n_steps, 1)))
model_emd.add(Dense(1))
model_emd.compile(optimizer='adam', loss='mse')
model_emd.fit(X_emd, y_emd, epochs=200, verbose=0)

# 预测
x_input = np.array([8, 9, 10])
x_input = x_input.reshape((1, n_steps, 1))
yhat = model.predict(x_input, verbose=0)
print(yhat[0][0])

emd_input = ceemdan.ceemdan(x_input[0], num_sifts=4)
emd_data = emd_input.get_imfs_and_residue()
emd_input = np.array(emd_data).reshape((1, n_steps, 4))
yhat_emd = model_emd.predict(emd_input, verbose=0)
yhat = yhat_emd[-1][0]
for i in range(1, len(emd_data)):
    yhat += yhat_emd[-i-1][0]
print(yhat)

在这个例子中，我们使用CEEMDAN分解将原始数据分解成多个IMF。然后，我们将每个IMF转换成监督学习问题，并使用LSTM模型进行训练。最后，我们可以使用训练好的模型进行预测。