Matlab源码实现：基于OOA-TCN-BiGRU-Attention混合模型的多变量时间序列预测系统完整解决方案,Matlab完整源码和数据1.基于OOA-TCN-BiGRU-Attention
资源内容介绍
Matlab源码实现：基于OOA-TCN-BiGRU-Attention混合模型的多变量时间序列预测系统完整解决方案,Matlab完整源码和数据1.基于OOA-TCN-BiGRU-Attention鱼鹰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：Matlab完整源码; OOA-TCN-BiGRU-Attention算法; 鱼鹰算法优化; 时间卷积双向门控循环单元; 注意力机制; 多变量时间序列预测; Matlab2023版以上; 输入多个特征; 输出单个变量; 历史特征影响; 数据集; main.m主运行; 文件夹; 命令窗口输出R2等评价指标; 优化学习率; 神经元个数; 注意力机制键值; 正则化参数。注意：以上内容分号进行分隔， <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/90341505/2/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/90341505/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">由于这篇文章主要讨论了复杂的机器学习算法模型实现及在<span class="_ _0"> </span><span class="ff2">Matlab<span class="_ _1"> </span></span>中的代码编写<span class="ff3">，</span>我将按照要求逐</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">步构建一个基于<span class="_ _0"> </span><span class="ff2">OOA-TCN-BiGRU-Attention<span class="_ _1"> </span></span>的鱼鹰算法优化时间卷积双向门控循环单元融合注意</div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">力机制的多变量时间序列预测的<span class="_ _0"> </span><span class="ff2">Matlab<span class="_ _1"> </span></span>完整源码和数据集<span class="ff4">。</span></div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">首先<span class="ff3">，</span>请确保您已经安装了<span class="_ _0"> </span><span class="ff2">Matlab 2023<span class="_ _1"> </span></span>版或更高版本<span class="ff3">，</span>因为这将需要使用最新的深度学习工具箱</div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">和相关的函数<span class="ff4">。</span></div><div class="t m0 x1 h2 y6 ff1 fs0 fc0 sc0 ls0 ws0">一<span class="ff4">、</span>准备数据集<span class="ff3">（<span class="ff2">data</span>）</span></div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">首先<span class="ff3">，</span>您需要准备一个多变量时间序列数据集<span class="ff3">（<span class="ff2">data</span>）<span class="ff4">。</span></span>该数据集应包含多个特征和一个目标变量<span class="ff4">。</span></div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">特征应包括影响目标变量的各种因素的历史数据<span class="ff4">。</span>请确保数据已经预处理过<span class="ff3">，</span>例如缺失值填充<span class="ff4">、</span>标准</div><div class="t m0 x1 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">化或归一化等<span class="ff4">。</span></div><div class="t m0 x1 h2 ya ff1 fs0 fc0 sc0 ls0 ws0">二<span class="ff4">、</span>构建主运行文件<span class="ff3">（<span class="ff2">main.m</span>）</span></div><div class="t m0 x1 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">在<span class="_ _0"> </span><span class="ff2">Matlab<span class="_ _1"> </span></span>中创建一个名为<span class="_ _0"> </span><span class="ff2">main.m<span class="_ _1"> </span></span>的主运行文件<span class="ff3">，</span>该文件将负责调用其他脚本和函数来完成整个预</div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">测过程<span class="ff4">。</span></div><div class="t m0 x1 h2 yd ff1 fs0 fc0 sc0 ls0 ws0">三<span class="ff4">、</span>模型构建与训练</div><div class="t m0 x1 h2 ye ff1 fs0 fc0 sc0 ls0 ws0">在<span class="_ _0"> </span><span class="ff2">main.m<span class="_ _1"> </span></span>中<span class="ff3">，</span>您将构建并训练基于<span class="_ _0"> </span><span class="ff2">OOA-TCN-BiGRU-Attention<span class="_ _1"> </span></span>的鱼鹰算法优化模型<span class="ff4">。</span>以下是一</div><div class="t m0 x1 h2 yf ff1 fs0 fc0 sc0 ls0 ws0">个简化的代码示例<span class="ff3">，</span>展示了如何使用<span class="_ _0"> </span><span class="ff2">Matlab<span class="_ _1"> </span></span>的深度学习工具箱构建模型<span class="ff4">、</span>设置层<span class="ff4">、</span>优化器等<span class="ff4">。</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">导入所需的库和工具箱</span></div><div class="t m0 x1 h2 y12 ff2 fs0 fc0 sc0 ls0 ws0">addpath('<span class="ff1">您的文件夹路径</span>'); % <span class="ff1">确保包含了所有需要的</span>.m<span class="_ _1"> </span><span class="ff1">文件和</span>.mat<span class="_ _1"> </span><span class="ff1">文件</span></div><div class="t m0 x1 h2 y13 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">加载数据</span></div><div class="t m0 x1 h2 y14 ff2 fs0 fc0 sc0 ls0 ws0">[features, labels] = loadData('<span class="ff1">您的数据集路径</span>');</div><div class="t m0 x1 h2 y15 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">定义模型结构</span></div><div class="t m0 x1 h3 y16 ff2 fs0 fc0 sc0 ls0 ws0">layers = [ ...</div><div class="t m0 x2 h2 y17 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">输入层</span>...</div><div class="t m0 x2 h3 y18 ff2 fs0 fc0 sc0 ls0 ws0">...</div><div class="t m0 x2 h2 y19 ff2 fs0 fc0 sc0 ls0 ws0">sequenceInputLayer(numFeatures) tcnLayer ... % <span class="ff1">时间卷积层<span class="ff3">（</span></span>TCN<span class="ff3">）</span></div><div class="t m0 x2 h3 y1a ff2 fs0 fc0 sc0 ls0 ws0">...</div><div class="t m0 x2 h2 y1b ff2 fs0 fc0 sc0 ls0 ws0">bidirectionalGRULayer(numHiddenUnits) ... % <span class="ff1">双向门控循环单元层<span class="ff3">（</span></span>BiGRU<span class="ff3">）</span></div><div class="t m0 x2 h3 y1c ff2 fs0 fc0 sc0 ls0 ws0">...</div><div class="t m0 x2 h2 y1d ff2 fs0 fc0 sc0 ls0 ws0">attentionLayer ... % <span class="ff1">注意力机制层</span></div><div class="t m0 x2 h3 y1e ff2 fs0 fc0 sc0 ls0 ws0">...</div><div class="t m0 x2 h2 y1f ff2 fs0 fc0 sc0 ls0 ws0">fullyConnectedLayer(1) ... % <span class="ff1">全连接层用于输出单个变量预测</span></div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div>