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

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

目录

• • 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
    • 程序设计
    • 参考资料

      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)

      

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      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

        
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