时间序列预测迁移学习：基于全连接层基础模型的 Python Keras 实现

以下是基于 Python 和 Keras 实现的时间序列预测的迁移学习模型，基础模型为全连接层：

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Input
from keras.optimizers import Adam
from keras.utils import to_categorical
from sklearn.preprocessing import MinMaxScaler

# 生成时间序列数据
def generate_data(n_samples, n_features, n_in, n_out):
    X = np.random.rand(n_samples, n_features)
    y = np.zeros((n_samples, n_out))
    for i in range(n_samples):
        for j in range(n_out):
            y[i,j] = np.sum(X[i,j:(j+n_in)]) # 将输入特征的后n_in个元素相加，作为输出特征
    return X, y

# 归一化数据
def normalize_data(X_train, X_test, y_train, y_test):
    scaler_X = MinMaxScaler(feature_range=(0, 1))
    scaler_X.fit(X_train)
    X_train = scaler_X.transform(X_train)
    X_test = scaler_X.transform(X_test)

    scaler_y = MinMaxScaler(feature_range=(0, 1))
    scaler_y.fit(y_train)
    y_train = scaler_y.transform(y_train)
    y_test = scaler_y.transform(y_test)

    return X_train, X_test, y_train, y_test, scaler_X, scaler_y

# 构建全连接层模型
def build_base_model(input_shape, output_shape):
    model = Sequential()
    model.add(Dense(64, input_shape=input_shape, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(32, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(output_shape, activation='linear'))
    return model

# 迁移学习模型
def build_transfer_learning_model(base_model, input_shape, output_shape):
    model = Sequential()
    model.add(base_model)
    model.add(Dense(32, input_shape=input_shape, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(output_shape, activation='linear'))
    return model

# 训练模型
def train_model(model, X_train, y_train, X_test, y_test):
    model.compile(loss='mse', optimizer=Adam(lr=0.001), metrics=['mae'])
    model.fit(X_train, y_train, epochs=100, batch_size=32, validation_data=(X_test, y_test), verbose=1)

# 预测模型
def predict_model(model, X_test, scaler_y):
    y_pred = model.predict(X_test)
    y_pred = scaler_y.inverse_transform(y_pred)
    return y_pred

# 测试模型
def test_model(y_test, y_pred):
    mae = np.mean(np.abs(y_test - y_pred))
    print('MAE: %.2f' % mae)

if __name__ == '__main__':
    # 生成数据集
    n_samples = 1000
    n_features = 10
    n_in = 5
    n_out = 3
    X, y = generate_data(n_samples, n_features, n_in, n_out)

    # 划分训练集和测试集
    train_size = int(n_samples * 0.8)
    X_train, X_test = X[:train_size], X[train_size:]
    y_train, y_test = y[:train_size], y[train_size:]

    # 归一化数据
    X_train, X_test, y_train, y_test, scaler_X, scaler_y = normalize_data(X_train, X_test, y_train, y_test)

    # 构建基础模型和迁移学习模型
    input_shape = (n_features,)
    output_shape = n_out
    base_model = build_base_model(input_shape, output_shape)
    transfer_learning_model = build_transfer_learning_model(base_model, input_shape, output_shape)

    # 训练基础模型和迁移学习模型
    train_model(base_model, X_train, y_train, X_test, y_test)
    train_model(transfer_learning_model, X_train, y_train, X_test, y_test)

    # 预测基础模型和迁移学习模型
    y_pred_base = predict_model(base_model, X_test, scaler_y)
    y_pred_transfer = predict_model(transfer_learning_model, X_test, scaler_y)

    # 测试基础模型和迁移学习模型
    test_model(y_test, y_pred_base)
    test_model(y_test, y_pred_transfer)

该代码首先生成一个包含10个特征的时间序列数据集，其中每个样本包含5个输入特征和3个输出特征。然后将数据集划分为训练集和测试集，并对数据进行归一化。接下来，构建了一个基础模型和一个迁移学习模型，其中基础模型为一个三层的全连接层模型，迁移学习模型在基础模型的基础上添加了一个额外的全连接层。最后，训练模型、预测模型并测试模型的性能。