import numpy as np import pandas as pd from sklearn.preprocessing import MinMaxScaler from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM, Dense from sklearn.metrics import mean_squared_error import matplotlib.pyplot as plt # 将 starttime 转换为 datetime 类型 data['starttime'] = pd.to_datetime(data['starttime']) # 提取特征和目标变量 features = data[['starttime', 'hour', 'is_weekend']] target = data['volume'] # 将 starttime 转换为时间戳 features['starttime'] = features['starttime'].map(pd.Timestamp.timestamp) # 归一化特征和目标变量 scaler_features = MinMaxScaler(feature_range=(0, 1)) scaled_features = scaler_features.fit_transform(features) scaler_target = MinMaxScaler(feature_range=(0, 1)) scaled_target = scaler_target.fit_transform(target.values.reshape(-1, 1)) # 准备时间序列数据 def create_sequences(data, seq_length): X = [] y = [] for i in range(len(data) - seq_length): X.append(data[i:i + seq_length, :]) y.append(data[i + seq_length, 0]) return np.array(X), np.array(y) seq_length = 10 X, y = create_sequences(np.concatenate((scaled_features, scaled_target), axis=1), seq_length) # 划分训练集和测试集 train_size = int(len(X) * 0.8) X_train, X_test = X[:train_size], X[train_size:] y_train, y_test = y[:train_size], y[train_size:] # 构建 LSTM 模型 model = Sequential() model.add(LSTM(50, activation='relu', input_shape=(seq_length, X_train.shape[2]))) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse') # 训练模型 model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=1) # 进行预测 y_pred = model.predict(X_test) # 反归一化预测结果和真实值 y_pred = scaler_target.inverse_transform(y_pred) y_test = scaler_target.inverse_transform(y_test.reshape(-1, 1)) # 计算均方误差 mse = mean_squared_error(y_test, y_pred) print(f'均方误差: {mse}') # 设置图片清晰度 plt.rcParams['figure.dpi'] = 300 # 绘制预测结果和真实值的对比图 plt.plot(y_test, label='真实值') plt.plot(y_pred, label='预测值') plt.title('交通流量预测') plt.xlabel('时间步') plt.ylabel('交通流量') plt.legend() plt.show()这是造成以上错误的代码，请提供具体的解决方案