深度残差网络和分位数回归结合起来的区间预测代码

这里是一个基于深度残差网络和分位数回归结合的区间预测代码。请注意，这只是一个示例代码，具体实现可能因数据集和模型结构而异。

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Dropout, Flatten, Conv1D, Add, Lambda
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam

# 构建深度残差网络
def resnet_block(x, filters, kernel_size, activation='relu', dropout_rate=0.2):
    # 残差路径
    res = Conv1D(filters=filters, kernel_size=kernel_size, padding='same')(x)
    res = Dropout(dropout_rate)(res)
    res = Conv1D(filters=filters, kernel_size=kernel_size, padding='same')(res)
    # 主路径
    x = Conv1D(filters=filters, kernel_size=kernel_size, padding='same')(x)
    x = Dropout(dropout_rate)(x)
    x = Conv1D(filters=filters, kernel_size=kernel_size, padding='same')(x)
    # 合并残差路径和主路径
    x = Add()([res, x])
    x = Activation(activation)(x)
    return x

def build_resnet(input_shape, output_shape, filters, kernel_size, activation='relu', dropout_rate=0.2):
    # 输入层
    inputs = Input(shape=input_shape)
    # 残差块
    x = resnet_block(inputs, filters, kernel_size, activation, dropout_rate)
    x = resnet_block(x, filters, kernel_size, activation, dropout_rate)
    x = resnet_block(x, filters, kernel_size, activation, dropout_rate)
    # 输出层
    outputs = Dense(output_shape, activation='linear')(x)
    # 模型
    model = Model(inputs=inputs, outputs=outputs)
    return model

# 构建分位数回归模型
def build_quantile_loss(q):
    def quantile_loss(y_true, y_pred):
        error = y_true - y_pred
        return tf.keras.backend.mean(tf.keras.backend.maximum(q * error, (q - 1) * error), axis=-1)
    return quantile_loss

def build_quantile_model(input_shape, output_shape, filters, kernel_size, activation='relu', dropout_rate=0.2, quantiles=[0.1, 0.5, 0.9]):
    # 初始化模型列表
    models = []
    # 构建多个分位数回归模型
    for q in quantiles:
        model = build_resnet(input_shape, output_shape, filters, kernel_size, activation, dropout_rate)
        model.compile(loss=build_quantile_loss(q), optimizer=Adam())
        models.append(model)
    return models

# 训练模型
def train_model(X_train, y_train, models, batch_size=32, epochs=100):
    # 训练每个分位数回归模型
    for model in models:
        model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1, shuffle=True)

# 预测分位数
def predict_quantiles(X_test, models):
    # 预测每个分位数
    y_preds = []
    for model in models:
        y_preds.append(model.predict(X_test))
    # 整理预测结果
    y_preds = np.array(y_preds)
    y_preds = np.transpose(y_preds, (1, 2, 0))
    return y_preds

# 构建区间预测模型
def build_interval_model(input_shape, output_shape, filters, kernel_size, activation='relu', dropout_rate=0.2, quantiles=[0.1, 0.5, 0.9]):
    # 构建多个分位数回归模型
    models = build_quantile_model(input_shape, output_shape, filters, kernel_size, activation, dropout_rate, quantiles)
    # 输入层
    inputs = Input(shape=input_shape)
    # 预测分位数
    preds = predict_quantiles(inputs, models)
    # 计算区间
    lower = Lambda(lambda x: x[:, :, 0])(preds)
    upper = Lambda(lambda x: x[:, :, -1])(preds)
    # 输出层
    outputs = tf.stack([lower, upper], axis=-1)
    # 模型
    model = Model(inputs=inputs, outputs=outputs)
    return model

在这个代码中，`build_resnet`函数构建了一个深度残差网络模型，`build_quantile_model`函数构建了多个分位数回归模型，`train_model`函数用于训练模型，`predict_quantiles`函数用于预测分位数，`build_interval_model`函数将分位数回归模型和区间预测模型结合起来，构建了一个完整的区间预测模型。