"Matlab 2023版以上多变量时间序列预测完整源码及数据集：基于NGO-TCN-BiGRU-Attention模型优化，含R2、MSE等多指标评价及参数优化",Matlab完整源码和数据1.基
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"Matlab 2023版以上多变量时间序列预测完整源码及数据集：基于NGO-TCN-BiGRU-Attention模型优化，含R2、MSE等多指标评价及参数优化",Matlab完整源码和数据1.基于NGO-TCN-BiGRU-Attention北方苍蝇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。,关键词：NGO-TCN-BiGRU-Attention；多变量时间序列预测；Matlab完整源码；数据集；主运行文件（main.m）；命令窗口输出多指标评价；学习率优化；神经元个数优化；注意力机制键值优化；正则化参数优化。,Matlab多变量时间序列预测完整源码：NGO-TCN-BiGRU-Attention模型优化版 <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/90341507/2/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/90341507/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">根据您的需求<span class="ff2">，</span>下面是一篇基于<span class="_ _0"> </span><span class="ff3">NGO-TCN-BiGRU-Attention<span class="_ _1"> </span></span>北方苍蝇算法优化时间卷积双向门控</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">循环单元融合注意力机制多变量时间序列预测的<span class="_ _0"> </span><span class="ff3">Matlab<span class="_ _1"> </span></span>完整源码和数据示例文章<span class="ff4">。</span></div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">一<span class="ff4">、</span>引言</div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">时间序列预测是机器学习和深度学习领域的重要应用之一<span class="ff4">。</span>本文将使用<span class="_ _0"> </span><span class="ff3">Matlab 2023<span class="_ _1"> </span></span>版以上版本<span class="ff2">，</span></div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">基于<span class="_ _0"> </span><span class="ff3">NGO-TCN-BiGRU-Attention<span class="_ _1"> </span></span>模型<span class="ff2">，</span>对多变量时间序列进行预测<span class="ff4">。</span>该模型融合了时间卷积网络</div><div class="t m0 x1 h2 y6 ff2 fs0 fc0 sc0 ls0 ws0">（<span class="ff3">TCN</span>）<span class="ff4">、<span class="ff1">双向门控循环单元</span></span>（<span class="ff3">BiGRU</span>）<span class="ff1">和注意力机制</span>，<span class="ff1">以优化预测精度<span class="ff4">。</span></span></div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">二<span class="ff4">、</span>数据准备</div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">首先<span class="ff2">，</span>您需要准备一个包含多个特征和单个输出变量的数据集<span class="ff4">。</span>这些数据应考虑历史特征的影响<span class="ff4">。</span>将</div><div class="t m0 x1 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">数据集保存在一个文件夹中<span class="ff2">，</span>并确保数据格式适用于<span class="_ _0"> </span><span class="ff3">Matlab<span class="ff4">。</span></span></div><div class="t m0 x1 h2 ya ff1 fs0 fc0 sc0 ls0 ws0">三<span class="ff4">、<span class="ff3">Matlab<span class="_ _1"> </span></span></span>源码</div><div class="t m0 x1 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">接下来<span class="ff2">，</span>我们将编写<span class="_ _0"> </span><span class="ff3">Matlab<span class="_ _1"> </span></span>代码来实现<span class="_ _0"> </span><span class="ff3">NGO-TCN-BiGRU-Attention<span class="_ _1"> </span></span>模型<span class="ff4">。</span>主要代码将保存在名</div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">为<span class="ff3">“main.m”</span>的文件中<span class="ff4">。</span>所有相关文件<span class="ff2">（</span>如数据加载<span class="ff4">、</span>模型训练<span class="ff4">、</span>预测和评估等<span class="ff2">）</span>应放在同一个文件</div><div class="t m0 x1 h2 yd ff1 fs0 fc0 sc0 ls0 ws0">夹中<span class="ff4">。</span></div><div class="t m0 x1 h2 ye ff3 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _2"> </span><span class="ff1">数据加载与预处理</span></div><div class="t m0 x1 h2 yf ff1 fs0 fc0 sc0 ls0 ws0">在<span class="ff3">“main.m”</span>中<span class="ff2">，</span>首先加载数据并进行预处理<span class="ff4">。</span>这包括数据清洗<span class="ff4">、</span>特征工程和归一化等步骤<span class="ff4">。</span></div><div class="t m0 x1 h2 y10 ff3 fs0 fc0 sc0 ls0 ws0">2.<span class="_ _2"> </span><span class="ff1">构建<span class="_ _0"> </span></span>NGO-TCN-BiGRU-Attention<span class="_ _1"> </span><span class="ff1">模型</span></div><div class="t m0 x1 h2 y11 ff1 fs0 fc0 sc0 ls0 ws0">接下来<span class="ff2">，</span>构建<span class="_ _0"> </span><span class="ff3">NGO-TCN-BiGRU-Attention<span class="_ _1"> </span></span>模型<span class="ff4">。</span>这包括定义网络结构<span class="ff4">、</span>优化器<span class="ff4">、</span>损失函数等<span class="ff4">。</span>注</div><div class="t m0 x1 h2 y12 ff1 fs0 fc0 sc0 ls0 ws0">意优化学习率<span class="ff4">、</span>神经元个数<span class="ff4">、</span>注意力机制的键值和正则化参数等超参数<span class="ff4">。</span></div><div class="t m0 x1 h2 y13 ff3 fs0 fc0 sc0 ls0 ws0">3.<span class="_ _2"> </span><span class="ff1">训练模型</span></div><div class="t m0 x1 h2 y14 ff1 fs0 fc0 sc0 ls0 ws0">使用训练数据对模型进行训练<span class="ff4">。</span>在训练过程中<span class="ff2">，</span>记录损失函数值和验证指标<span class="ff2">（</span>如<span class="_ _0"> </span><span class="ff3">R2<span class="ff4">、</span>MSE<span class="_ _1"> </span></span>等<span class="ff2">）<span class="ff4">。</span></span></div><div class="t m0 x1 h2 y15 ff3 fs0 fc0 sc0 ls0 ws0">4.<span class="_ _2"> </span><span class="ff1">预测与评估</span></div><div class="t m0 x1 h2 y16 ff1 fs0 fc0 sc0 ls0 ws0">使用测试数据对模型进行预测<span class="ff2">，</span>并计算<span class="_ _0"> </span><span class="ff3">R2<span class="ff4">、</span>MSE<span class="ff4">、</span>MAE<span class="ff4">、</span>MAPE<span class="_ _1"> </span></span>和<span class="_ _0"> </span><span class="ff3">RMSE<span class="_ _1"> </span></span>等多个指标以评估模型性能</div><div class="t m0 x1 h2 y17 ff4 fs0 fc0 sc0 ls0 ws0">。<span class="ff1">在命令窗口中输出这些指标</span>。</div><div class="t m0 x1 h2 y18 ff1 fs0 fc0 sc0 ls0 ws0">四<span class="ff4">、</span>结果与讨论</div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div>