cnn-bls时间序列预测

您好，CNN-BLS是另一种用于时间序列预测的深度学习模型，它结合了卷积神经网络（CNN）和基于学习的稳健回归（BLS）的优势。

在CNN-BLS模型中，首先使用卷积神经网络提取时间序列数据的局部空间特征，并将其转换为高维特征向量。然后，使用基于学习的稳健回归算法对这些特征向量进行降维处理，减少特征向量的维度，去除噪声和冗余信息。最后，使用线性回归模型对降维后的特征向量进行建模，以预测时间序列数据的未来趋势。

相对于传统的时间序列预测方法，CNN-BLS模型具有更好的预测性能和更高的鲁棒性，能够处理噪声和异常数据，并具有更快的训练和预测速度。

希望我的回答能够解决您的问题。

相关问题

cnn-bls时间序列预测python代码

好的，这里给您提供一个基于Python的CNN-BLS时间序列预测的示例代码，供参考：

首先，我们需要导入必要的库：

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv1D, MaxPooling1D
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from BLS import BLS

接下来，我们可以定义一个函数用于读取并预处理时间序列数据，以便进行模型训练和预测：

def load_data(filename, look_back):
    # 读取数据文件
    raw_data = np.loadtxt(filename, delimiter=',')
    # 数据归一化处理
    scaler = MinMaxScaler(feature_range=(0, 1))
    scaled_data = scaler.fit_transform(raw_data.reshape(-1, 1))
    # 构造输入和输出序列
    X = []
    Y = []
    for i in range(len(scaled_data) - look_back):
        X.append(scaled_data[i:i+look_back, 0])
        Y.append(scaled_data[i+look_back, 0])
    X = np.array(X)
    Y = np.array(Y)
    # 将数据集分为训练集和测试集
    train_size = int(len(X) * 0.7)
    X_train, X_test = X[:train_size], X[train_size:]
    Y_train, Y_test = Y[:train_size], Y[train_size:]
    return X_train, Y_train, X_test, Y_test, scaler

然后，我们可以使用卷积神经网络对时间序列数据进行特征提取：

def create_cnn_model(look_back):
    model = Sequential()
    model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(look_back, 1)))
    model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
    model.add(MaxPooling1D(pool_size=2))
    model.add(Conv1D(filters=128, kernel_size=3, activation='relu'))
    model.add(Conv1D(filters=128, kernel_size=3, activation='relu'))
    model.add(MaxPooling1D(pool_size=2))
    model.add(Conv1D(filters=256, kernel_size=3, activation='relu'))
    model.add(Conv1D(filters=256, kernel_size=3, activation='relu'))
    model.add(MaxPooling1D(pool_size=2))
    model.add(Flatten())
    model.add(Dense(256, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(64, activation='relu'))
    return model

接着，我们可以使用基于学习的稳健回归算法对特征向量进行降维处理：

def bls_feature(X_train, Y_train, X_test, Y_test, hidden_neurons, sparsity):
    # 构造BLS模型
    bls = BLS(hidden_neurons, sparsity)
    # 训练BLS模型
    bls.fit(X_train, Y_train, X_test, Y_test)
    # 使用BLS模型进行特征提取
    X_train_features = bls.predict(X_train)
    X_test_features = bls.predict(X_test)
    return X_train_features, X_test_features

最后，我们可以使用线性回归模型对降维后的特征向量进行建模，以预测时间序列数据的未来趋势：

def train_and_predict(X_train_features, Y_train, X_test_features, Y_test):
    # 构造线性回归模型
    model = Sequential()
    model.add(Dense(1, input_dim=X_train_features.shape[1], activation='linear'))
    model.compile(loss='mean_squared_error', optimizer='adam')
    # 训练线性回归模型
    model.fit(X_train_features, Y_train, epochs=100, batch_size=64, verbose=0)
    # 使用线性回归模型进行预测
    Y_train_pred = model.predict(X_train_features)
    Y_test_pred = model.predict(X_test_features)
    # 计算预测误差
    train_rmse = np.sqrt(mean_squared_error(Y_train, Y_train_pred))
    test_rmse = np.sqrt(mean_squared_error(Y_test, Y_test_pred))
    print('Train RMSE: %.3f' % train_rmse)
    print('Test RMSE: %.3f' % test_rmse)

完整代码如下所示：

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv1D, MaxPooling1D
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from BLS import BLS

def load_data(filename, look_back):
    # 读取数据文件
    raw_data = np.loadtxt(filename, delimiter=',')
    # 数据归一化处理
    scaler = MinMaxScaler(feature_range=(0, 1))
    scaled_data = scaler.fit_transform(raw_data.reshape(-1, 1))
    # 构造输入和输出序列
    X = []
    Y = []
    for i in range(len(scaled_data) - look_back):
        X.append(scaled_data[i:i+look_back, 0])
        Y.append(scaled_data[i+look_back, 0])
    X = np.array(X)
    Y = np.array(Y)
    # 将数据集分为训练集和测试集
    train_size = int(len(X) * 0.7)
    X_train, X_test = X[:train_size], X[train_size:]
    Y_train, Y_test = Y[:train_size], Y[train_size:]
    return X_train, Y_train, X_test, Y_test, scaler

def create_cnn_model(look_back):
    model = Sequential()
    model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(look_back, 1)))
    model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
    model.add(MaxPooling1D(pool_size=2))
    model.add(Conv1D(filters=128, kernel_size=3, activation='relu'))
    model.add(Conv1D(filters=128, kernel_size=3, activation='relu'))
    model.add(MaxPooling1D(pool_size=2))
    model.add(Conv1D(filters=256, kernel_size=3, activation='relu'))
    model.add(Conv1D(filters=256, kernel_size=3, activation='relu'))
    model.add(MaxPooling1D(pool_size=2))
    model.add(Flatten())
    model.add(Dense(256, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(64, activation='relu'))
    return model

def bls_feature(X_train, Y_train, X_test, Y_test, hidden_neurons, sparsity):
    # 构造BLS模型
    bls = BLS(hidden_neurons, sparsity)
    # 训练BLS模型
    bls.fit(X_train, Y_train, X_test, Y_test)
    # 使用BLS模型进行特征提取
    X_train_features = bls.predict(X_train)
    X_test_features = bls.predict(X_test)
    return X_train_features, X_test_features

def train_and_predict(X_train_features, Y_train, X_test_features, Y_test):
    # 构造线性回归模型
    model = Sequential()
    model.add(Dense(1, input_dim=X_train_features.shape[1], activation='linear'))
    model.compile(loss='mean_squared_error', optimizer='adam')
    # 训练线性回归模型
    model.fit(X_train_features, Y_train, epochs=100, batch_size=64, verbose=0)
    # 使用线性回归模型进行预测
    Y_train_pred = model.predict(X_train_features)
    Y_test_pred = model.predict(X_test_features)
    # 计算预测误差
    train_rmse = np.sqrt(mean_squared_error(Y_train, Y_train_pred))
    test_rmse = np.sqrt(mean_squared_error(Y_test, Y_test_pred))
    print('Train RMSE: %.3f' % train_rmse)
    print('Test RMSE: %.3f' % test_rmse)

# 加载数据
X_train, Y_train, X_test, Y_test, scaler = load_data('data.csv', look_back=60)

# 特征提取
hidden_neurons = 100
sparsity = 0.8
X_train_features, X_test_features = bls_feature(X_train, Y_train, X_test, Y_test, hidden_neurons, sparsity)

# 建模和预测
model = create_cnn_model(look_back=60)
model.summary()
model.compile(loss='mse', optimizer='adam')
model.fit(X_train.reshape(-1, 60, 1), Y_train, epochs=100, batch_size=64, verbose=0)
Y_train_pred = model.predict(X_train.reshape(-1, 60, 1))
Y_test_pred = model.predict(X_test.reshape(-1, 60, 1))
train_rmse = np.sqrt(mean_squared_error(Y_train, Y_train_pred))
test_rmse = np.sqrt(mean_squared_error(Y_test, Y_test_pred))
print('Train RMSE: %.3f' % train_rmse)
print('Test RMSE: %.3f' % test_rmse)

train_and_predict(X_train_features, Y_train, X_test_features, Y_test)

需要注意的是，上述代码中使用了一个名为“BLS”的自定义模块，用于实现基于学习的稳健回归算法。如果您还没有这个模块，请先下载并安装。

生成CNN-BLS的python代码

好的，以下是生成CNN-BLS的Python代码的示例：

import numpy as np
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer

digits = load_digits()
X = digits.data
y = digits.target
X -= X.min()
X /= X.max()

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

labels_train = LabelBinarizer().fit_transform(y_train)
labels_test = LabelBinarizer().fit_transform(y_test)

from cnn_bls import ConvNN_BLS

cnn_bls = ConvNN_BLS(input_shape=(8, 8, 1),
                     n_classes=10,
                     conv_layers=[(6, 5)],
                     hidden_layers=[10],
                     bls_hidden_layers=[],
                     epochs=10,
                     batch_size=10,
                     reg=0.01,
                     verbose=True)

cnn_bls.fit(X_train, labels_train)
print("Accuracy:", cnn_bls.score(X_test, labels_test))