基于PSO-TCN-BiGRU-Attention融合算法的Matlab多变量时间序列预测完整源码与数据集，包含优化学习率、神经元数及注意力机制参数的R2、MSE等多指标评价体系,Matlab完整源码
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基于PSO-TCN-BiGRU-Attention融合算法的Matlab多变量时间序列预测完整源码与数据集，包含优化学习率、神经元数及注意力机制参数的R2、MSE等多指标评价体系,Matlab完整源码和数据1.基于PSO-TCN-BiGRU-Attention粒子群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：PSO-TCN-BiGRU-Attention；多变量时间序列预测；Matlab2023版以上；输入特征；输出单个变量；历史特征影响；数据集；main.m；命令窗口输出评价指标；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。,基于PSO-TCN-BiGRU-Attention的Matlab多变量时间序列预测完整源码 <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/90341504/2/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/90341504/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">基于<span class="_ _0"> </span><span class="ff2">PSO-TCN-BiGRU-Attention<span class="_ _1"> </span></span>的时间序列预测<span class="_ _0"> </span><span class="ff2">Matlab<span class="_ _1"> </span></span>源码</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">本篇文章将提供一个基于<span class="_ _0"> </span><span class="ff2">Matlab<span class="_ _1"> </span></span>的完整源码<span class="ff4">，</span>该源码利用<span class="_ _0"> </span><span class="ff2">PSO<span class="ff4">（</span></span>粒子群算法<span class="ff4">）</span>优化<span class="_ _0"> </span><span class="ff2">TCN<span class="ff4">（</span></span>时间卷积</div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">网络<span class="ff4">）</span>与<span class="_ _0"> </span><span class="ff2">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<span class="ff4">（<span class="ff2">Attention Mechanism</span>）</span>的多变量时间</div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">序列预测模型<span class="ff3">。</span>该模型考虑了历史特征的影响<span class="ff4">，</span>并输出单个变量预测结果<span class="ff4">，</span>同时提供多种评价指标如</div><div class="t m0 x1 h2 y6 ff2 fs0 fc0 sc0 ls0 ws0">R2<span class="ff3">、</span>MSE<span class="ff3">、</span>MAE<span class="ff3">、</span>MAPE<span class="_ _1"> </span><span class="ff1">和<span class="_ _0"> </span></span>RMSE<span class="ff3">。</span></div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">二<span class="ff3">、</span>模型架构</div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">本模型结合了时间卷积网络<span class="ff4">（<span class="ff2">TCN</span>）</span>和双向门控循环单元<span class="ff4">（<span class="ff2">BiGRU</span>）</span>的优点<span class="ff4">，</span>并通过注意力机制提升</div><div class="t m0 x1 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">模型性能<span class="ff3">。</span>在训练过程中<span class="ff4">，</span>我们将使用粒子群算法<span class="ff4">（<span class="ff2">PSO</span>）</span>对学习率<span class="ff3">、</span>神经元个数<span class="ff3">、</span>注意力机制的键</div><div class="t m0 x1 h2 ya ff1 fs0 fc0 sc0 ls0 ws0">值以及正则化参数进行优化<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 ff2 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _2"> </span><span class="ff1">数据准备</span></div><div class="t m0 x1 h2 yd ff1 fs0 fc0 sc0 ls0 ws0">首先<span class="ff4">，</span>我们需要准备数据集<span class="ff2">`data`<span class="ff3">。</span></span>数据集应包含多个特征和单个输出变量<span class="ff4">，</span>并考虑历史特征的影</div><div class="t m0 x1 h2 ye ff1 fs0 fc0 sc0 ls0 ws0">响<span class="ff3">。</span>将数据集分为训练集和测试集<span class="ff3">。</span></div><div class="t m0 x1 h2 yf ff2 fs0 fc0 sc0 ls0 ws0">2.<span class="_ _2"> </span><span class="ff1">主运行文件<span class="_ _0"> </span></span>main.m</div><div class="t m0 x1 h2 y10 ff1 fs0 fc0 sc0 ls0 ws0">将所有相关文件放在一个文件夹中<span class="ff4">，</span>并创建主运行文件<span class="ff2">`main.m`<span class="ff3">。</span></span></div><div class="t m0 x1 h3 y11 ff2 fs0 fc0 sc0 ls0 ws0">```matlab</div><div class="t m0 x1 h2 y12 ff2 fs0 fc0 sc0 ls0 ws0">% main.m - <span class="ff1">主运行文件</span></div><div class="t m0 x1 h2 y13 ff2 fs0 fc0 sc0 ls0 ws0">clear; clc; % <span class="ff1">清除工作空间和命令窗口内容</span></div><div class="t m0 x1 h2 y14 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">加载数据集</span></div><div class="t m0 x1 h2 y15 ff2 fs0 fc0 sc0 ls0 ws0">load('data.mat'); % <span class="ff1">假设<span class="_ _0"> </span></span>data.mat<span class="_ _1"> </span><span class="ff1">包含你的数据集</span></div><div class="t m0 x1 h2 y16 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">划分训练集和测试集</span></div><div class="t m0 x1 h2 y17 ff2 fs0 fc0 sc0 ls0 ws0">trainData = ...; % <span class="ff1">训练集数据</span></div><div class="t m0 x1 h2 y18 ff2 fs0 fc0 sc0 ls0 ws0">testData = ...; % <span class="ff1">测试集数据</span></div><div class="t m0 x1 h2 y19 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">模型参数初始化</span></div><div class="t m0 x1 h2 y1a ff2 fs0 fc0 sc0 ls0 ws0">psoParams = ...; % PSO<span class="_ _1"> </span><span class="ff1">算法参数设置</span></div><div class="t m0 x1 h2 y1b ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">初始化网络参数</span></div><div class="t m0 x1 h2 y1c ff2 fs0 fc0 sc0 ls0 ws0">% ... <span class="ff1">这里添加你的网络初始化代码</span> ...</div><div class="t m0 x1 h2 y1d ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">训练网络并优化参数</span></div><div class="t m0 x1 h3 y1e ff2 fs0 fc0 sc0 ls0 ws0">[optimizedParams, history] = psoOptimizeNetwork(psoParams, trainData);</div><div class="t m0 x1 h2 y1f ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">使用优化后的参数进行预测</span></div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div>