多维时序 | Matlab实现BiTCN双向时间卷积神经网络多变量时间序列预测

预测效果

基本介绍

1.Matlab实现BiTCN双向时间卷积神经网络多变量时间序列预测（完整源码和数据)
2.运行环境Matlab2023及以上，excel数据集，多列输入，单列输出，方便替换数据，考虑历史特征的影响；
3.多指标评价，评价指标包括：R2、MAE、MAPE、MSE等，代码质量极高。

程序设计

• 完整程序和数据获取方式资源处下载Matlab实现BiTCN双向时间卷积神经网络多变量时间序列预测。

clc;clear;close all;format compact
tic
clc
clear all

 % 创建TCN正向支路
    layers = [
        convolution1dLayer(filterSize, numFilters, DilationFactor = dilationFactor, Padding = "causal", Name="conv1_" + i)  % 一维卷积层 
        layerNormalizationLayer                                                                                             % 层归一化
        spatialDropoutLayer(dropoutFactor)                                                                                  % 空间丢弃层
        convolution1dLayer(filterSize, numFilters, DilationFactor = dilationFactor, Padding = "causal")                     % 一维卷积层  
        layerNormalizationLayer                                                                                             % 层归一化
        reluLayer                                                                                                           % 激活层
        spatialDropoutLayer(dropoutFactor)                                                                                  % 空间丢弃层
        additionLayer(4, Name = "add_" + i)
    ];
 
    % 添加残差块到网络
    lgraph = addLayers(lgraph, layers);
 
    % 连接卷积层到残差块
    lgraph = connectLayers(lgraph, outputName, "conv1_" + i);
 
    % 创建 TCN反向支路flip网络结构
    Fliplayers = [
        FlipLayer("flip_" + i)                                                                                               % 反向翻转
        convolution1dLayer(1, numFilters, Name = "convSkip_"+i);                                                             % 反向残差连接
        convolution1dLayer(filterSize, numFilters, DilationFactor = dilationFactor, Padding = "causal", Name="conv2_" + i)   % 一维卷积层
        layerNormalizationLayer                                                                                              % 层归一化
        spatialDropoutLayer(dropoutFactor)                                                                                   % 空间丢弃层
        convolution1dLayer(filterSize, numFilters, DilationFactor = dilationFactor, Padding = "causal")                      % 一维卷积层
        layerNormalizationLayer                                                                                              % 层归一化
        reluLayer                                                                                                            % 激活层
        spatialDropoutLayer(dropoutFactor, Name="drop" + i)                                                                  % 空间丢弃层
    ];
 
    % 添加 flip 网络结构到网络
    lgraph = addLayers(lgraph, Fliplayers);
 
    % 连接 flip 卷积层到残差块
    lgraph = connectLayers(lgraph, outputName, "flip_" + i);
    lgraph = connectLayers(lgraph, "drop" + i, "add_" + i + "/in3");
    lgraph = connectLayers(lgraph, "convSkip_"+i, "add_" + i + "/in4");
    % 残差连接 -- 首层
    if i == 1
        % 建立残差卷积层
        % Include convolution in first skip connection.
        layer = convolution1dLayer(1,numFilters,Name="convSkip");

        lgraph = addLayers(lgraph,layer);
        lgraph = connectLayers(lgraph,outputName,"convSkip");
        lgraph = connectLayers(lgraph,"convSkip","add_" + i + "/in2");
    else
        lgraph = connectLayers(lgraph,outputName,"add_" + i + "/in2");
    end

参考资料

[1] https://blog.csdn.net/kjm13182345320/category_11799242.html?spm=1001.2014.3001.5482
[2] https://blog.csdn.net/kjm13182345320/article/details/124571691