基于WOA-TCN-BiGRU-Attention算法的Matlab多变量时间序列预测完整源码与数据集：包含学习率、神经元数及注意力机制参数优化，输出R2、MSE、MAE、MAPE与RMSE多维度评价
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基于WOA-TCN-BiGRU-Attention算法的Matlab多变量时间序列预测完整源码与数据集：包含学习率、神经元数及注意力机制参数优化，输出R2、MSE、MAE、MAPE与RMSE多维度评价指标,Matlab完整源码和数据1.基于WOA-TCN-BiGRU-Attention鲸鱼算法优化时间卷积双向门控循环单元融合注意力机制多变量时间序列预测，要求Matlab2023版以上；2.输入多个特征，输出单个变量，考虑历史特征的影响，多变量时间序列预测；3.data为数据集，main.m为主运行即可,所有文件放在一个文件夹；4.命令窗口输出R2、MSE、MAE、MAPE和RMSE多指标评价；5.优化学习率，神经元个数，注意力机制的键值, 正则化参数。,核心关键词：WOA-TCN-BiGRU-Attention；Matlab完整源码；数据集；多变量时间序列预测；R2；MSE；MAE；MAPE；RMSE；学习率优化；神经元个数调整；注意力机制键值调整；正则化参数调整。,Matlab多变量时间序列预测完整源码：WOA-TCN-BiGRU-Attention模型 <link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/base.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90341512/2/raw.css" rel="stylesheet"/><div id="sidebar" style="display: none"><div id="outline"></div></div><div class="pf w0 h0" data-page-no="1" id="pf1"><div class="pc pc1 w0 h0"><img alt="" class="bi x0 y0 w1 h1" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90341512/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">基于<span class="_ _0"> </span><span class="ff2">WOA-TCN-BiGRU-Attention<span class="_ _1"> </span></span>的<span class="_ _0"> </span><span class="ff2">Matlab<span class="_ _1"> </span></span>多变量时间序列预测完整源码</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">一<span class="ff3">、</span>前言</div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">本文将使用<span class="_ _0"> </span><span class="ff2">Matlab<span class="_ _1"> </span></span>编写一个基于<span class="_ _0"> </span><span class="ff2">WOA<span class="ff4">（</span></span>鲸鱼优化算法<span class="ff4">）<span class="ff2">-TCN</span>（</span>时间卷积网络<span class="ff4">）<span class="ff2">-BiGRU</span>（</span>双向门控</div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">循环单元<span class="ff4">）<span class="ff2">-Attention<span class="_ _1"> </span></span></span>机制的多变量时间序列预测模型<span class="ff3">。</span>模型将通过优化学习率<span class="ff3">、</span>神经元个数<span class="ff3">、</span>注</div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">意力机制的键值以及正则化参数<span class="ff4">，</span>对多变量时间序列进行预测<span class="ff4">，</span>并在命令窗口输出<span class="_ _0"> </span><span class="ff2">R2<span class="ff3">、</span>MSE<span class="ff3">、</span>MAE<span class="ff3">、</span></span></div><div class="t m0 x1 h2 y6 ff2 fs0 fc0 sc0 ls0 ws0">MAPE<span class="_ _1"> </span><span class="ff1">和<span class="_ _0"> </span></span>RMSE<span class="_ _1"> </span><span class="ff1">等多项评价指标<span class="ff3">。</span></span></div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">二<span class="ff3">、</span>算法描述</div><div class="t m0 x1 h2 y8 ff2 fs0 fc0 sc0 ls0 ws0">WOA<span class="_ _1"> </span><span class="ff1">是一种新型的优化算法<span class="ff4">，</span>通过模拟鲸鱼的群体行为来进行全局搜索和局部开发<span class="ff4">，</span>用于寻找全局最</span></div><div class="t m0 x1 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">优解<span class="ff3">。<span class="ff2">TCN<span class="_ _1"> </span></span></span>则是一种新型的时间序列处理网络<span class="ff4">，</span>通过卷积操作来捕捉时间序列的局部依赖性<span class="ff3">。<span class="ff2">BiGRU</span></span></div><div class="t m0 x1 h2 ya ff1 fs0 fc0 sc0 ls0 ws0">则是一种基于<span class="_ _0"> </span><span class="ff2">RNN<span class="ff4">（</span></span>循环神经网络<span class="ff4">）</span>的变体<span class="ff4">，</span>可以捕捉时间序列的长期依赖性<span class="ff3">。<span class="ff2">Attention<span class="_ _1"> </span></span></span>机制则</div><div class="t m0 x1 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">可以加强模型对重要信息的关注程度<span class="ff3">。</span></div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">三<span class="ff3">、</span>代码实现</div><div class="t m0 x1 h2 yd ff1 fs0 fc0 sc0 ls0 ws0">下面是一个简单的<span class="_ _0"> </span><span class="ff2">Matlab 2023<span class="_ _1"> </span></span>版以上的实现流程<span class="ff3">。</span>注意<span class="ff4">，</span>这是一个简化的实现<span class="ff4">，</span>可能需要根据实</div><div class="t m0 x1 h2 ye ff1 fs0 fc0 sc0 ls0 ws0">际的数据集和需求进行相应的调整<span class="ff3">。</span></div><div class="t m0 x1 h2 yf ff2 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _2"> </span><span class="ff1">导入数据集并预处理</span></div><div class="t m0 x1 h3 y10 ff2 fs0 fc0 sc0 ls0 ws0">```matlab</div><div class="t m0 x1 h2 y11 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">假设<span class="_ _0"> </span></span>data<span class="_ _1"> </span><span class="ff1">为你的数据集文件<span class="ff4">，</span>包含多个特征和单个输出变量</span></div><div class="t m0 x1 h2 y12 ff2 fs0 fc0 sc0 ls0 ws0">data = load('data.csv'); % <span class="ff1">根据实际情况修改文件路径和格式</span></div><div class="t m0 x1 h2 y13 ff2 fs0 fc0 sc0 ls0 ws0">X = data(:, 1:end-1); % <span class="ff1">输入特征</span></div><div class="t m0 x1 h2 y14 ff2 fs0 fc0 sc0 ls0 ws0">y = data(:, end); % <span class="ff1">输出变量</span></div><div class="t m0 x1 h3 y15 ff2 fs0 fc0 sc0 ls0 ws0">```</div><div class="t m0 x1 h2 y16 ff2 fs0 fc0 sc0 ls0 ws0">2.<span class="_ _2"> </span><span class="ff1">定义<span class="_ _0"> </span></span>WOA-TCN-BiGRU-Attention<span class="_ _1"> </span><span class="ff1">模型</span></div><div class="t m0 x1 h3 y17 ff2 fs0 fc0 sc0 ls0 ws0">```matlab</div><div class="t m0 x1 h2 y18 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">这里省略了详细的网络结构定义<span class="ff4">，</span>你需要根据实际需求进行定义</span></div><div class="t m0 x1 h2 y19 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">例如<span class="ff4">，</span>定义层数<span class="ff3">、</span>神经元个数<span class="ff3">、</span>学习率等参数</span></div><div class="t m0 x1 h3 y1a ff2 fs0 fc0 sc0 ls0 ws0">```</div><div class="t m0 x1 h2 y1b ff2 fs0 fc0 sc0 ls0 ws0">3.<span class="_ _2"> </span><span class="ff1">训练模型并优化参数</span></div><div class="t m0 x1 h3 y1c ff2 fs0 fc0 sc0 ls0 ws0">```matlab</div><div class="t m0 x1 h2 y1d ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">使用<span class="_ _0"> </span></span>WOA<span class="_ _1"> </span><span class="ff1">算法对学习率<span class="ff3">、</span>神经元个数<span class="ff3">、</span>注意力机制的键值进行优化</span></div><div class="t m0 x1 h2 y1e ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">这里假设已经有一个函数或脚本执行<span class="_ _0"> </span></span>WOA<span class="_ _1"> </span><span class="ff1">算法并返回最优参数</span></div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div>