python -读取Keras LSTM的多个CSV

import pandas as pd
from keras.preprocessing.sequence import TimeseriesGenerator
from keras.models import Sequential
from keras.layers import LSTM, Dense

data = pd.DataFrame()
file_paths = ['file1.csv', 'file2.csv', 'file3.csv']  # 替换为实际的文件路径
for file_path in file_paths:
    df = pd.read_csv(file_path)
    data = pd.concat([data, df], ignore_index=True)

# 假设CSV文件中的数据按时间顺序排列，且包含'timestamp'和'value'两列
data['timestamp'] = pd.to_datetime(data['timestamp'])
data = data.set_index('timestamp')
data = data.resample('H').mean()  # 将数据按小时重采样，可根据实际需求调整

sequence_length = 10  # 序列长度，可根据实际需求调整
generator = TimeseriesGenerator(data['value'].values, data['value'].values, length=sequence_length, batch_size=1)

model = Sequential()
model.add(LSTM(64, input_shape=(sequence_length, 1)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
model.fit_generator(generator, epochs=10)  # 可根据实际需求调整训练轮数