# 获取股票数据 df = ts.get_k_data('600519', start='2001-01-01', end='2015-12-31') # 将收盘价转换为numpy数组 close_price = np.array(df['close']) # 进行小波变换 coeffs = pywt.wavedec(close_price, 'db4', level=4) coeffs[1:] = (pywt.threshold(i, value=100, mode='soft') for i in coeffs[1:]) reconstructed_signal = pywt.waverec(coeffs, 'db4') 将这段代码进行续写完成数据的预处理，从而使得该小波变换后的数据可以作为lstm模型的输入

接下来可以对预处理后的数据进行归一化，然后将数据切分成训练集和测试集，最后将数据转换为LSTM模型所需的格式。

1. 归一化

from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(0, 1))
reconstructed_signal = scaler.fit_transform(reconstructed_signal.reshape(-1, 1))

2. 切分数据

train_size = int(len(reconstructed_signal) * 0.7)
test_size = len(reconstructed_signal) - train_size
train_data, test_data = reconstructed_signal[0:train_size,:], reconstructed_signal[train_size:len(reconstructed_signal),:]

3. 将数据转换为LSTM模型所需的格式

def create_dataset(dataset, look_back=1):
    dataX, dataY = [], []
    for i in range(len(dataset)-look_back-1):
        a = dataset[i:(i+look_back), 0]
        dataX.append(a)
        dataY.append(dataset[i + look_back, 0])
    return np.array(dataX), np.array(dataY)

look_back = 10
trainX, trainY = create_dataset(train_data, look_back)
testX, testY = create_dataset(test_data, look_back)

# 将数据重塑为LSTM模型的输入格式 [样本数, 时间步, 特征数]
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))

最终，预处理完成的数据可以作为LSTM模型的输入，进行模型训练和预测。