Matlab 2023版以上多变量时间序列预测模型:CPO-TCN-BiGRU-Attention算法优化与多指标评价完整源码集,Matlab完整源码和数据1.基于CPO-TCN-BiGRU-Att
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Matlab 2023版以上多变量时间序列预测模型:CPO-TCN-BiGRU-Attention算法优化与多指标评价完整源码集,Matlab完整源码和数据1.基于CPO-TCN-BiGRU-Attention冠豪猪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:CPO-TCN-BiGRU-Attention; 冠豪猪算法; 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; Matlab 2023版以上; 输入特征输出单一变量; 历史特征影响; 命令窗口输出多指标评价; 优化学习率; 神经元个数; 注意力机制键值; 正则化参数; Data集; main.m主运行文件。以上关键词用分号分隔为:CPO-TCN-BiGRU-Att <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/90341509/2/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/90341509/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">冠豪猪算法与多变量时间序列预测的<span class="_ _0"> </span><span class="ff2">Matlab<span class="_ _1"> </span></span>源码及数据分析</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">一<span class="ff3">、</span>引言</div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">多变量时间序列预测是众多领域内常见的预测问题<span class="ff4">,</span>包括经济预测<span class="ff3">、</span>天气预测等<span class="ff3">。</span>随着深度学习的发</div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">展<span class="ff4">,</span>多种神经网络结构如<span class="_ _0"> </span><span class="ff2">CPO-TCN-BiGRU<span class="ff4">(</span></span>双向门控循环单元<span class="ff4">)</span>融合注意力机制在时间序列预测中</div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">取得了良好的效果<span class="ff3">。</span>本文将介绍一个基于<span class="_ _0"> </span><span class="ff2">Matlab<span class="_ _1"> </span></span>的完整源码和数据集<span class="ff4">,</span>用于实现上述算法的优化和</div><div class="t m0 x1 h2 y6 ff1 fs0 fc0 sc0 ls0 ws0">预测<span class="ff3">。</span></div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">二<span class="ff3">、</span>算法概述</div><div class="t m0 x1 h2 y8 ff2 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _2"> </span>CPO-TCN-BiGRU-Attention<span class="ff4">:<span class="ff1">该算法结合了卷积神经网络</span>(</span>TCN<span class="ff4">)<span class="ff3">、<span class="ff1">双向门控循环单元</span></span>(</span></div><div class="t m0 x2 h2 y9 ff2 fs0 fc0 sc0 ls0 ws0">BiGRU<span class="ff4">)<span class="ff1">和注意力机制<span class="ff3">。</span>其中</span>,</span>TCN<span class="_ _1"> </span><span class="ff1">用于捕捉时间序列的局部依赖性<span class="ff4">,</span></span>BiGRU<span class="_ _1"> </span><span class="ff1">用于捕捉长期依</span></div><div class="t m0 x2 h2 ya ff1 fs0 fc0 sc0 ls0 ws0">赖性<span class="ff4">,</span>而注意力机制则用于强调对预测结果影响较大的特征<span class="ff3">。</span></div><div class="t m0 x1 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">三<span class="ff3">、<span class="ff2">Matlab<span class="_ _1"> </span></span></span>源码实现</div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">下面是一个基于<span class="_ _0"> </span><span class="ff2">Matlab 2023<span class="_ _1"> </span></span>版以上的完整源码框架<span class="ff4">,</span>以供参考<span class="ff4">:</span></div><div class="t m0 x1 h2 yd ff4 fs0 fc0 sc0 ls0 ws0">(<span class="ff1">一</span>)<span class="ff2">main.m <span class="ff1">文件</span></span></div><div class="t m0 x1 h2 ye ff1 fs0 fc0 sc0 ls0 ws0">此文件作为主运行文件<span class="ff4">,</span>负责调用其他函数并整合整个流程<span class="ff3">。</span></div><div class="t m0 x1 h3 yf ff2 fs0 fc0 sc0 ls0 ws0">```matlab</div><div class="t m0 x1 h2 y10 ff2 fs0 fc0 sc0 ls0 ws0">% main.m <span class="ff1">文件内容</span></div><div class="t m0 x1 h3 y11 ff2 fs0 fc0 sc0 ls0 ws0">clear; clc;</div><div class="t m0 x1 h2 y12 ff2 fs0 fc0 sc0 ls0 ws0">data = load('data.mat'); % <span class="ff1">加载数据集</span></div><div class="t m0 x1 h2 y13 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">初始化参数<span class="ff4">,</span>如学习率<span class="ff3">、</span>神经元个数等</span></div><div class="t m0 x1 h3 y14 ff2 fs0 fc0 sc0 ls0 ws0">params = initializeParams();</div><div class="t m0 x1 h2 y15 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">训练模型并保存结果</span></div><div class="t m0 x1 h3 y16 ff2 fs0 fc0 sc0 ls0 ws0">[model, history] = trainModel(data, params);</div><div class="t m0 x1 h2 y17 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">测试模型并输出评价指标</span></div><div class="t m0 x1 h3 y18 ff2 fs0 fc0 sc0 ls0 ws0">evaluateModel(model, data, history);</div><div class="t m0 x1 h3 y19 ff2 fs0 fc0 sc0 ls0 ws0">```</div><div class="t m0 x1 h2 y1a ff4 fs0 fc0 sc0 ls0 ws0">(<span class="ff1">二</span>)<span class="ff2">initializeParams.m <span class="ff1">文件</span></span>(<span class="ff1">用于初始化参数</span>)</div><div class="t m0 x1 h3 y1b ff2 fs0 fc0 sc0 ls0 ws0">```matlab</div><div class="t m0 x1 h3 y1c ff2 fs0 fc0 sc0 ls0 ws0">function params = initializeParams()</div><div class="t m0 x2 h2 y1d ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">初始化学习率<span class="ff3">、</span>神经元个数等参数</span></div><div class="t m0 x2 h2 y1e ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">例如<span class="ff4">:</span></span>params.learningRate = 0.001; params.neuronCount = 128; ...</div><div class="t m0 x1 h3 y1f ff2 fs0 fc0 sc0 ls0 ws0">end</div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div>