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end"></span></button><!----></section></li><li><section class="vp-sidebar-group"><button class="vp-sidebar-header clickable" type="button"><!----><span class="vp-sidebar-title">FAQ</span><span class="vp-arrow end"></span></button><!----></section></li></ul><!----></aside><!--[--><main id="main-content" class="vp-page"><!--[--><!----><!----><nav class="vp-breadcrumb disable"></nav><div class="vp-page-title"><h1><!---->AINode</h1><div class="page-info"><!----><!----><span class="page-date-info" aria-label="写作日期📅" data-balloon-pos="up"><svg xmlns="http://www.w3.org/2000/svg" class="icon calendar-icon" viewBox="0 0 1024 1024" fill="currentColor" aria-label="calendar icon" name="calendar"><path d="M716.4 110.137c0-18.753-14.72-33.473-33.472-33.473-18.753 0-33.473 14.72-33.473 33.473v33.473h66.993v-33.473zm-334.87 0c0-18.753-14.72-33.473-33.473-33.473s-33.52 14.72-33.52 33.473v33.473h66.993v-33.473zm468.81 33.52H716.4v100.465c0 18.753-14.72 33.473-33.472 33.473a33.145 33.145 0 01-33.473-33.473V143.657H381.53v100.465c0 18.753-14.72 33.473-33.473 33.473a33.145 33.145 0 01-33.473-33.473V143.657H180.6A134.314 134.314 0 0046.66 277.595v535.756A134.314 134.314 0 00180.6 947.289h669.74a134.36 134.36 0 00133.94-133.938V277.595a134.314 134.314 0 00-133.94-133.938zm33.473 267.877H147.126a33.145 33.145 0 01-33.473-33.473c0-18.752 14.72-33.473 33.473-33.473h736.687c18.752 0 33.472 14.72 33.472 33.473a33.145 33.145 0 01-33.472 33.473z"></path></svg><span data-allow-mismatch="text">2025/7/4</span><meta property="datePublished" content="2025-07-04T15:38:44.000Z"></span><!----><span class="page-reading-time-info" aria-label="阅读时间⌛" data-balloon-pos="up"><svg xmlns="http://www.w3.org/2000/svg" class="icon timer-icon" viewBox="0 0 1024 1024" fill="currentColor" aria-label="timer icon" name="timer"><path d="M799.387 122.15c4.402-2.978 7.38-7.897 7.38-13.463v-1.165c0-8.933-7.38-16.312-16.312-16.312H256.33c-8.933 0-16.311 7.38-16.311 16.312v1.165c0 5.825 2.977 10.874 7.637 13.592 4.143 194.44 97.22 354.963 220.201 392.763-122.204 37.542-214.893 196.511-220.2 389.397-4.661 5.049-7.638 11.651-7.638 19.03v5.825h566.49v-5.825c0-7.379-2.849-13.981-7.509-18.9-5.049-193.016-97.867-351.985-220.2-389.527 123.24-37.67 216.446-198.453 220.588-392.892zM531.16 450.445v352.632c117.674 1.553 211.787 40.778 211.787 88.676H304.097c0-48.286 95.149-87.382 213.728-88.676V450.445c-93.077-3.107-167.901-81.297-167.901-177.093 0-8.803 6.99-15.793 15.793-15.793 8.803 0 15.794 6.99 15.794 15.793 0 80.261 63.69 145.635 142.01 145.635s142.011-65.374 142.011-145.635c0-8.803 6.99-15.793 15.794-15.793s15.793 6.99 15.793 15.793c0 95.019-73.789 172.82-165.96 177.093z"></path></svg><span>大约 15 分钟</span><meta property="timeRequired" content="PT15M"></span><!----><!----></div><hr></div><!----><div class="" vp-content><!----><div id="markdown-content"><h1 id="ainode" tabindex="-1"><a class="header-anchor" href="#ainode"><span>AINode</span></a></h1><p>AINode 是支持时序相关模型注册、管理、调用的 IoTDB 原生节点，内置业界领先的自研时序大模型，如清华自研时序模型 Timer 系列，可通过标准 SQL 语句进行调用，实现时序数据的毫秒级实时推理，可支持时序趋势预测、缺失值填补、异常值检测等应用场景。</p><blockquote><p>V2.0.5及以后版本支持</p></blockquote><p>系统架构如下图所示：</p><figure><img src="/img/AINode-0.png" alt="" tabindex="0" loading="lazy"><figcaption></figcaption></figure><p>三种节点的职责如下：</p><ul><li><strong>ConfigNode</strong>：负责分布式节点管理和负载均衡。</li><li><strong>DataNode</strong>：负责接收并解析用户的 SQL请求；负责存储时间序列数据；负责数据的预处理计算。</li><li><strong>AINode</strong>：负责时序模型的管理和使用。</li></ul><h2 id="_1-优势特点" tabindex="-1"><a class="header-anchor" href="#_1-优势特点"><span>1. 优势特点</span></a></h2><p>与单独构建机器学习服务相比，具有以下优势：</p><ul><li><p><strong>简单易用</strong>：无需使用 Python 或 Java 编程，使用 SQL 语句即可完成机器学习模型管理与推理的完整流程。如创建模型可使用CREATE MODEL语句、使用模型进行推理可使用 SELECT * FROM FORECAST (...) 语句等，使用更加简单便捷。</p></li><li><p><strong>避免数据迁移</strong>：使用 IoTDB 原生机器学习可以将存储在 IoTDB 中的数据直接应用于机器学习模型的推理，无需将数据移动到单独的机器学习服务平台，从而加速数据处理、提高安全性并降低成本。</p></li></ul><figure><img src="/img/h1.png" alt="" tabindex="0" loading="lazy"><figcaption></figcaption></figure><ul><li><strong>内置先进算法</strong>：支持业内领先机器学习分析算法，覆盖典型时序分析任务，为时序数据库赋能原生数据分析能力。如： <ul><li><strong>时间序列预测（Time Series Forecasting）</strong>：从过去时间序列中学习变化模式；从而根据给定过去时间的观测值，输出未来序列最可能的预测。</li><li><strong>时序异常检测（Anomaly Detection for Time Series）</strong>：在给定的时间序列数据中检测和识别异常值，帮助发现时间序列中的异常行为。</li><li><strong>时间序列标注（Time Series Annotation）</strong>：为每个数据点或特定时间段添加额外的信息或标记，例如事件发生、异常点、趋势变化等，以便更好地理解和分析数据。</li></ul></li></ul><h2 id="_2-基本概念" tabindex="-1"><a class="header-anchor" href="#_2-基本概念"><span>2. 基本概念</span></a></h2><ul><li><strong>模型（Model）</strong>：机器学习模型，以时序数据作为输入，输出分析任务的结果或决策。模型是 AINode 的基本管理单元，支持模型的增（注册）、删、查、用（推理）。</li><li><strong>创建（Create）</strong>: 将外部设计或训练好的模型文件或算法加载到 AINode 中，由 IoTDB 统一管理与使用。</li><li><strong>推理（Inference）</strong>：使用创建的模型在指定时序数据上完成该模型适用的时序分析任务。</li><li><strong>内置能力（Built-in）</strong>：AINode 自带常见时序分析场景（例如预测与异常检测）的机器学习算法或自研模型。</li></ul><figure><img src="/img/AINode-new.png" alt="" tabindex="0" loading="lazy"><figcaption></figcaption></figure><h2 id="_3-安装部署" tabindex="-1"><a class="header-anchor" href="#_3-安装部署"><span>3. 安装部署</span></a></h2><p>AINode 的部署可参考文档 <a class="route-link" href="/zh/UserGuide/latest-Table/Deployment-and-Maintenance/AINode_Deployment_apache.html">AINode 部署</a>。</p><h2 id="_4-使用指导" tabindex="-1"><a class="header-anchor" href="#_4-使用指导"><span>4. 使用指导</span></a></h2><p>AINode 对时序模型提供了模型创建及删除功能，内置模型无需创建，可直接使用。</p><h3 id="_4-1-注册模型" tabindex="-1"><a class="header-anchor" href="#_4-1-注册模型"><span>4.1 注册模型</span></a></h3><p>通过指定模型输入输出的向量维度，可以注册训练好的深度学习模型，从而用于模型推理。</p><p>符合以下内容的模型可以注册到AINode中：</p><ol><li>AINode 目前支持基于 PyTorch 2.4.0 版本训练的模型，需避免使用 2.4.0 版本以上的特性。</li><li>AINode 支持使用 PyTorch JIT 存储的模型（<code>model.pt</code>），模型文件需要包含模型的结构和权重。</li><li>模型输入序列可以包含一列或多列，若有多列，需要和模型能力、模型配置文件对应。</li><li>模型的配置参数必须在<code>config.yaml</code>配置文件中明确定义。使用模型时，必须严格按照<code>config.yaml</code>配置文件中定义的输入输出维度。如果输入输出列数不匹配配置文件，将会导致错误。</li></ol><p>下方为模型注册的SQL语法定义。</p><div class="language-sql line-numbers-mode" data-highlighter="shiki" data-ext="sql" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-sql"><span class="line"><span style="color:#C678DD;">create</span><span style="color:#ABB2BF;"> model </span><span style="color:#56B6C2;">&lt;</span><span style="color:#ABB2BF;">model_id</span><span style="color:#56B6C2;">&gt;</span><span style="color:#C678DD;"> using</span><span style="color:#ABB2BF;"> uri </span><span style="color:#56B6C2;">&lt;</span><span style="color:#ABB2BF;">uri</span><span style="color:#56B6C2;">&gt;</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div></div></div><p>SQL中参数的具体含义如下：</p><ul><li><p>model_id：模型的全局唯一标识，不可重复。模型名称具备以下约束：</p><ul><li>允许出现标识符 [ 0-9 a-z A-Z _ ]（字母，数字（非开头），下划线（非开头））</li><li>长度限制为2-64字符</li><li>大小写敏感</li></ul></li><li><p>uri：模型注册文件的资源路径，路径下应包含<strong>模型结构及权重文件 <a href="http://model.pt" target="_blank" rel="noopener noreferrer">model.pt</a> 文件和模型配置文件 config.yaml</strong></p><ul><li><p>模型结构及权重文件：模型训练完成后得到的权重文件，目前支持 pytorch 训练得到的 .pt 文件</p></li><li><p>模型配置文件：模型注册时需要提供的与模型结构有关的参数，其中必须包含模型的输入输出维度用于模型推理：</p></li><li><table><thead><tr><th><strong>参数名</strong></th><th><strong>参数描述</strong></th><th><strong>示例</strong></th></tr></thead><tbody><tr><td>input_shape</td><td>模型输入的行列，用于模型推理</td><td>[96,2]</td></tr><tr><td>output_shape</td><td>模型输出的行列，用于模型推理</td><td>[48,2]</td></tr></tbody></table></li></ul><p>除了模型推理外，还可以指定模型输入输出的数据类型：</p><ul><li><table><thead><tr><th><strong>参数名</strong></th><th><strong>参数描述</strong></th><th><strong>示例</strong></th></tr></thead><tbody><tr><td>input_type</td><td>模型输入的数据类型</td><td>[&#39;float32&#39;,&#39;float32&#39;]</td></tr><tr><td>output_type</td><td>模型输出的数据类型</td><td>[&#39;float32&#39;,&#39;float32&#39;]</td></tr></tbody></table></li></ul><p>除此之外，可以额外指定备注信息用于在模型管理时进行展示</p><ul><li><table><thead><tr><th><strong>参数名</strong></th><th><strong>参数描述</strong></th><th><strong>示例</strong></th></tr></thead><tbody><tr><td>attributes</td><td>可选，用户自行设定的模型备注信息，用于模型展示</td><td>&#39;model_type&#39;: &#39;dlinear&#39;,&#39;kernel_size&#39;: &#39;25&#39;</td></tr></tbody></table></li></ul></li></ul><p>除了本地模型文件的注册，还可以通过URI来指定远程资源路径来进行注册，使用开源的模型仓库（例如HuggingFace）。</p><h4 id="示例" tabindex="-1"><a class="header-anchor" href="#示例"><span>示例</span></a></h4><p>在 <a href="https://github.com/apache/iotdb/tree/master/integration-test/src/test/resources/ainode-example" target="_blank" rel="noopener noreferrer">example 文件夹</a>下，包含model.pt和config.yaml文件，model.pt为训练得到，config.yaml的内容如下：</p><div class="language-yaml line-numbers-mode" data-highlighter="shiki" data-ext="yaml" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-yaml"><span class="line"><span style="color:#E06C75;">configs</span><span style="color:#ABB2BF;">: </span></span>
<span class="line"><span style="color:#7F848E;font-style:italic;"> # 必选项</span></span>
<span class="line"><span style="color:#E06C75;"> input_shape</span><span style="color:#ABB2BF;">: [</span><span style="color:#D19A66;">96</span><span style="color:#ABB2BF;">, </span><span style="color:#D19A66;">2</span><span style="color:#ABB2BF;">] </span><span style="color:#7F848E;font-style:italic;"># 表示模型接收的数据为96行x2列</span></span>
<span class="line"><span style="color:#E06C75;"> output_shape</span><span style="color:#ABB2BF;">: [</span><span style="color:#D19A66;">48</span><span style="color:#ABB2BF;">, </span><span style="color:#D19A66;">2</span><span style="color:#ABB2BF;">] </span><span style="color:#7F848E;font-style:italic;"># 表示模型输出的数据为48行x2列</span></span>
<span class="line"><span style="color:#ABB2BF;"> </span></span>
<span class="line"><span style="color:#7F848E;font-style:italic;"> # 可选项 默认为全部float32，列数为shape对应的列数</span></span>
<span class="line"><span style="color:#E06C75;"> input_type</span><span style="color:#ABB2BF;">: [</span><span style="color:#98C379;">&quot;int64&quot;</span><span style="color:#ABB2BF;">,</span><span style="color:#98C379;">&quot;int64&quot;</span><span style="color:#ABB2BF;">] </span><span style="color:#7F848E;font-style:italic;">#输入对应的数据类型，需要与输入列数匹配</span></span>
<span class="line"><span style="color:#E06C75;"> output_type</span><span style="color:#ABB2BF;">: [</span><span style="color:#98C379;">&quot;text&quot;</span><span style="color:#ABB2BF;">,</span><span style="color:#98C379;">&quot;int64&quot;</span><span style="color:#ABB2BF;">] </span><span style="color:#7F848E;font-style:italic;">#输出对应的数据类型，需要与输出列数匹配</span></span>
<span class="line"></span>
<span class="line"><span style="color:#E06C75;">attributes</span><span style="color:#ABB2BF;">: </span><span style="color:#7F848E;font-style:italic;"># 可选项 为用户自定义的备注信息</span></span>
<span class="line"><span style="color:#98C379;"> &#39;model_type&#39;</span><span style="color:#ABB2BF;">: </span><span style="color:#98C379;">&#39;dlinear&#39;</span></span>
<span class="line"><span style="color:#98C379;"> &#39;kernel_size&#39;</span><span style="color:#ABB2BF;">: </span><span style="color:#98C379;">&#39;25&#39;</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><p>指定该文件夹作为加载路径就可以注册该模型</p><div class="language-sql line-numbers-mode" data-highlighter="shiki" data-ext="sql" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-sql"><span class="line"><span style="color:#ABB2BF;">IoTDB</span><span style="color:#56B6C2;">&gt;</span><span style="color:#C678DD;"> create</span><span style="color:#ABB2BF;"> model dlinear_example </span><span style="color:#C678DD;">using</span><span style="color:#ABB2BF;"> uri </span><span style="color:#98C379;">&quot;file://./example&quot;</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div></div></div><p>SQL执行后会异步进行注册的流程，可以通过模型展示查看模型的注册状态（见模型展示章节），注册成功的耗时主要受到模型文件大小的影响。</p><p>模型注册完成后，就可以通过使用正常查询的方式调用具体函数，进行模型推理。</p><h3 id="_4-2-查看模型" tabindex="-1"><a class="header-anchor" href="#_4-2-查看模型"><span>4.2 查看模型</span></a></h3><p>注册成功的模型可以通过show models指令查询模型的具体信息。其SQL定义如下：</p><div class="language-sql line-numbers-mode" data-highlighter="shiki" data-ext="sql" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-sql"><span class="line"><span style="color:#ABB2BF;">show models</span></span>
<span class="line"></span>
<span class="line"><span style="color:#ABB2BF;">show models </span><span style="color:#56B6C2;">&lt;</span><span style="color:#ABB2BF;">model_id</span><span style="color:#56B6C2;">&gt;</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><p>除了直接展示所有模型的信息外，可以指定model id来查看某一具体模型的信息。模型展示的结果中包含如下信息：</p><table><thead><tr><th><strong>ModelId</strong></th><th><strong>ModelType</strong></th><th><strong>Category</strong></th><th><strong>State</strong></th></tr></thead><tbody><tr><td>模型ID</td><td>模型类型</td><td>模型种类</td><td>模型状态</td></tr></tbody></table><ul><li>模型状态机流转示意图如下</li></ul><figure><img src="/img/AINode-State-apache.png" alt="" tabindex="0" loading="lazy"><figcaption></figcaption></figure><p><strong>说明：</strong></p><ol><li>启动 AINode，show models 只能看到 BUILT-IN 模型</li><li>用户可导入自己的模型，来源为 USER-DEFINED，可尝试从配置文件解析 ModelType，解析不到则为空</li><li>时序大模型权重不随 AINode 打包，AINode 启动时自动下载，下载过程中为 LOADING</li><li>下载成功转变为 ACTIVE，失败则变成 INACTIVE</li></ol><p><strong>示例</strong></p><div class="language-sql line-numbers-mode" data-highlighter="shiki" data-ext="sql" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-sql"><span class="line"><span style="color:#ABB2BF;">IoTDB</span><span style="color:#56B6C2;">&gt;</span><span style="color:#ABB2BF;"> show models</span></span>
<span class="line"><span style="color:#ABB2BF;">+</span><span style="color:#7F848E;font-style:italic;">---------------------+--------------------+--------------+---------+</span></span>
<span class="line"><span style="color:#ABB2BF;">| ModelId| ModelType| Category| </span><span style="color:#C678DD;">State</span><span style="color:#ABB2BF;">|</span></span>
<span class="line"><span style="color:#ABB2BF;">+</span><span style="color:#7F848E;font-style:italic;">---------------------+--------------------+--------------+---------+</span></span>
<span class="line"><span style="color:#ABB2BF;">| arima| Arima| BUILT-</span><span style="color:#C678DD;">IN</span><span style="color:#ABB2BF;">| ACTIVE|</span></span>
<span class="line"><span style="color:#ABB2BF;">| holtwinters| HoltWinters| BUILT-</span><span style="color:#C678DD;">IN</span><span style="color:#ABB2BF;">| ACTIVE|</span></span>
<span class="line"><span style="color:#ABB2BF;">|exponential_smoothing|ExponentialSmoothing| BUILT-</span><span style="color:#C678DD;">IN</span><span style="color:#ABB2BF;">| ACTIVE|</span></span>
<span class="line"><span style="color:#ABB2BF;">| naive_forecaster| NaiveForecaster| BUILT-</span><span style="color:#C678DD;">IN</span><span style="color:#ABB2BF;">| ACTIVE|</span></span>
<span class="line"><span style="color:#ABB2BF;">| stl_forecaster| StlForecaster| BUILT-</span><span style="color:#C678DD;">IN</span><span style="color:#ABB2BF;">| ACTIVE|</span></span>
<span class="line"><span style="color:#ABB2BF;">| gaussian_hmm| GaussianHmm| BUILT-</span><span style="color:#C678DD;">IN</span><span style="color:#ABB2BF;">| ACTIVE|</span></span>
<span class="line"><span style="color:#ABB2BF;">| gmm_hmm| GmmHmm| BUILT-</span><span style="color:#C678DD;">IN</span><span style="color:#ABB2BF;">| ACTIVE|</span></span>
<span class="line"><span style="color:#ABB2BF;">| stray| Stray| BUILT-</span><span style="color:#C678DD;">IN</span><span style="color:#ABB2BF;">| ACTIVE|</span></span>
<span class="line"><span style="color:#ABB2BF;">| custom| | USER-DEFINED| ACTIVE|</span></span>
<span class="line"><span style="color:#ABB2BF;">| timer_xl| Timer-XL| BUILT-</span><span style="color:#C678DD;">IN</span><span style="color:#ABB2BF;">| LOADING|</span></span>
<span class="line"><span style="color:#ABB2BF;">| sundial| Timer-Sundial| BUILT-</span><span style="color:#C678DD;">IN</span><span style="color:#ABB2BF;">| ACTIVE|</span></span>
<span class="line"><span style="color:#ABB2BF;">+</span><span style="color:#7F848E;font-style:italic;">---------------------+--------------------+--------------+---------+</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><h3 id="_4-3-删除模型" tabindex="-1"><a class="header-anchor" href="#_4-3-删除模型"><span>4.3 删除模型</span></a></h3><p>对于注册成功的模型，用户可以通过SQL进行删除，该操作会删除所有 AINode 下的相关模型文件，其SQL如下：</p><div class="language-sql line-numbers-mode" data-highlighter="shiki" data-ext="sql" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-sql"><span class="line"><span style="color:#C678DD;">drop</span><span style="color:#ABB2BF;"> model </span><span style="color:#56B6C2;">&lt;</span><span style="color:#ABB2BF;">model_id</span><span style="color:#56B6C2;">&gt;</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div></div></div><p>需要指定已经成功注册的模型 model_id 来删除对应的模型。由于模型删除涉及模型数据清理，操作不会立即完成，此时模型的状态为 DROPPING，该状态的模型不能用于模型推理。请注意，该功能不支持删除内置模型。</p><h3 id="_4-4-使用内置模型推理" tabindex="-1"><a class="header-anchor" href="#_4-4-使用内置模型推理"><span>4.4 使用内置模型推理</span></a></h3><p>SQL语法如下：</p><div class="language-sql line-numbers-mode" data-highlighter="shiki" data-ext="sql" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-sql"><span class="line"><span style="color:#C678DD;">SELECT</span><span style="color:#ABB2BF;"> * </span><span style="color:#C678DD;">FROM</span><span style="color:#ABB2BF;"> forecast(</span></span>
<span class="line"><span style="color:#ABB2BF;"> input, </span></span>
<span class="line"><span style="color:#ABB2BF;"> model_id,</span></span>
<span class="line"><span style="color:#ABB2BF;"> [output_length, </span></span>
<span class="line"><span style="color:#ABB2BF;"> output_start_time,</span></span>
<span class="line"><span style="color:#ABB2BF;"> output_interval,</span></span>
<span class="line"><span style="color:#ABB2BF;"> timecol, </span></span>
<span class="line"><span style="color:#ABB2BF;"> preserve_input,</span></span>
<span class="line"><span style="color:#ABB2BF;"> model_options]?</span></span>
<span class="line"><span style="color:#ABB2BF;">)</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><p>内置模型推理无需注册流程，通过 forecast 函数，指定 model_id 就可以使用模型的推理功能</p><ul><li><p>请注意，使用内置时序大模型进行推理的前提条件是本地存有对应模型权重，目录为 /IOTDB_AINODE_HOME/data/ainode/models/weights/model_id/。若本地没有模型权重，则会自动从 HuggingFace 拉取，请保证本地能直接访问 HuggingFace。</p></li><li><p>参数介绍如下：</p></li></ul><table><thead><tr><th style="text-align:left;">参数名</th><th style="text-align:left;">参数类型</th><th style="text-align:left;">参数属性</th><th style="text-align:left;">描述</th><th style="text-align:left;">是否必填</th><th style="text-align:left;">备注</th></tr></thead><tbody><tr><td style="text-align:left;">input</td><td style="text-align:left;">表参数</td><td style="text-align:left;">SET SEMANTIC</td><td style="text-align:left;">待预测的输入数据</td><td style="text-align:left;">是</td><td style="text-align:left;"></td></tr><tr><td style="text-align:left;">model_id</td><td style="text-align:left;">标量参数</td><td style="text-align:left;">字符串类型</td><td style="text-align:left;">需要选择的model名</td><td style="text-align:left;">是</td><td style="text-align:left;">只能为非空，且内置的模型，否则报错：空字符串：MODEL_ID should never be null or empty不存在的模型：model [%s] has not been created模型不可用：model [%s] is not available</td></tr><tr><td style="text-align:left;">output_length</td><td style="text-align:left;">标量参数</td><td style="text-align:left;">INT32类型默认值：96</td><td style="text-align:left;">输出窗口大小</td><td style="text-align:left;">否</td><td style="text-align:left;">必须大于 0，否则报错：OUTPUT_LENGTH should be greater than 0</td></tr><tr><td style="text-align:left;">output_start_time</td><td style="text-align:left;">标量参数</td><td style="text-align:left;">时间戳类型默认值：输入数据的最后一个时间戳加 output_interval</td><td style="text-align:left;">输出的预测点的起始时间戳</td><td style="text-align:left;">否</td><td style="text-align:left;">可以为负数，表示1970年1月1号之前的时间戳</td></tr><tr><td style="text-align:left;">output_interval</td><td style="text-align:left;">标量参数</td><td style="text-align:left;">时间间隔类型默认值：0（输入数据的采样间隔）</td><td style="text-align:left;">输出的预测点之间的时间间隔支持的单位是 ns、us、ms、s、m、h、d、w</td><td style="text-align:left;">否</td><td style="text-align:left;">大于 0 时，采用用户指定的输出间隔；小于等于 0 时，根据输入数据自动推测</td></tr><tr><td style="text-align:left;">timecol</td><td style="text-align:left;">标量参数</td><td style="text-align:left;">字符串类型默认值：time</td><td style="text-align:left;">时间列名</td><td style="text-align:left;">否</td><td style="text-align:left;">存在于 input 中的，数据类型为 TIMESTAMP 的列，否则报错：若数据类型不为 TIMESTAMP： The type of the column [%s] is not as expected.若列不存在：Required column [%s] not found in the source table argument.</td></tr><tr><td style="text-align:left;">preserve_input</td><td style="text-align:left;">标量参数</td><td style="text-align:left;">布尔类型默认值：false</td><td style="text-align:left;">是否在输出结果集中保留输入的所有原始行</td><td style="text-align:left;">否</td><td style="text-align:left;"></td></tr><tr><td style="text-align:left;">model_options</td><td style="text-align:left;">标量参数</td><td style="text-align:left;">字符串类型默认值：空字符串</td><td style="text-align:left;">模型相关的key-value对，比如是否需要对输入进行归一化等。不同的key-value对以&#39;;&#39;间隔</td><td style="text-align:left;">否</td><td style="text-align:left;">指定某个模型不支持参数，并不会报错，只会被忽略；AINode 中内置的模型支持的常见参数详见文末附录说明。</td></tr></tbody></table><p><strong>说明：</strong></p><ol><li>forecast 函数默认对输入表中所有列进行预测（不包含time列和partition by 的列）。</li><li>forecast 函数对于输入数据无顺序性要求，默认对输入数据按照时间戳（由 TIMECOL 参数指定时间戳的列名）做升序排序后，再调用模型进行预测。</li><li>不同模型对于输入数据的行数要求不同，输入数据少于最低行数要求时会报错。 <ul><li>在当前的 AINdoe 内置模型中，Timer-XL 模型至少需要输入 96 行数据，Timer-Sundial 模型至少需要输入 16 行数据。</li></ul></li><li>forecast 函数的返回结果列包含 input 表的所有输入列，列的数据类型与原表列的数据类型一致。若 preserve_input= true，则还包含 is_input 列（表征当前行是否为输入行） <ul><li>目前只支持对 INT32、INT64、FLOAT、DOUBLE 进行预测，否则报错：The type of the column [%s] is [%s], only INT32, INT64, FLOAT, DOUBLE is allowed</li></ul></li><li>output_start_time 和 output_interval 只会影响输出结果集的时间戳列生成，均为可选参数。 <ul><li>output_start_time 默认为输入数据的最后一个时间戳加 output_interval</li><li>output_interval = (输入数据的最后一个时间戳 - 输入数据的第一个时间戳) / n - 1， 默认为输入数据的采样间隔</li><li>第 N 个输出行的时间为 output_start_time + (N - 1) * output_interval</li></ul></li></ol><p><strong>示例：需要提前创建数据库及表</strong></p><div class="language-sql line-numbers-mode" data-highlighter="shiki" data-ext="sql" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-sql"><span class="line"><span style="color:#C678DD;">create</span><span style="color:#C678DD;"> database</span><span style="color:#61AFEF;"> etth</span></span>
<span class="line"><span style="color:#C678DD;">create</span><span style="color:#C678DD;"> table</span><span style="color:#61AFEF;"> eg</span><span style="color:#ABB2BF;"> (hufl </span><span style="color:#C678DD;">FLOAT</span><span style="color:#ABB2BF;"> FIELD, hull </span><span style="color:#C678DD;">FLOAT</span><span style="color:#ABB2BF;"> FIELD, mufl </span><span style="color:#C678DD;">FLOAT</span><span style="color:#ABB2BF;"> FIELD, mull </span><span style="color:#C678DD;">FLOAT</span><span style="color:#ABB2BF;"> FIELD, lufl </span><span style="color:#C678DD;">FLOAT</span><span style="color:#ABB2BF;"> FIELD, lull </span><span style="color:#C678DD;">FLOAT</span><span style="color:#ABB2BF;"> FIELD, ot </span><span style="color:#C678DD;">FLOAT</span><span style="color:#ABB2BF;"> FIELD)</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div><div class="line-number"></div></div></div><p>我们所使用的的测试集的数据为<a href="/img/ETTh1-tab.csv">ETTh1-tab</a>。</p><p><strong>查看当前支持的模型</strong></p><div class="language-bash line-numbers-mode" data-highlighter="shiki" data-ext="bash" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-bash"><span class="line"><span style="color:#61AFEF;">IoTDB:etth</span><span style="color:#ABB2BF;">&gt; </span><span style="color:#98C379;">show</span><span style="color:#98C379;"> models</span></span>
<span class="line"><span style="color:#61AFEF;">+---------------------+--------------------+--------+------+</span></span>
<span class="line"><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">ModelId</span><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">ModelType</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">Category</span><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">State</span><span style="color:#ABB2BF;">|</span></span>
<span class="line"><span style="color:#61AFEF;">+---------------------+--------------------+--------+------+</span></span>
<span class="line"><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">arima</span><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">Arima</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">BUILT-IN</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">ACTIVE</span><span style="color:#ABB2BF;">|</span></span>
<span class="line"><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">holtwinters</span><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">HoltWinters</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">BUILT-IN</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">ACTIVE</span><span style="color:#ABB2BF;">|</span></span>
<span class="line"><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">exponential_smoothing</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">ExponentialSmoothing</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">BUILT-IN</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">ACTIVE</span><span style="color:#ABB2BF;">|</span></span>
<span class="line"><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">naive_forecaster</span><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">NaiveForecaster</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">BUILT-IN</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">ACTIVE</span><span style="color:#ABB2BF;">|</span></span>
<span class="line"><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">stl_forecaster</span><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">StlForecaster</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">BUILT-IN</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">ACTIVE</span><span style="color:#ABB2BF;">|</span></span>
<span class="line"><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">gaussian_hmm</span><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">GaussianHmm</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">BUILT-IN</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">ACTIVE</span><span style="color:#ABB2BF;">|</span></span>
<span class="line"><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">gmm_hmm</span><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">GmmHmm</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">BUILT-IN</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">ACTIVE</span><span style="color:#ABB2BF;">|</span></span>
<span class="line"><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">stray</span><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">Stray</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">BUILT-IN</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">ACTIVE</span><span style="color:#ABB2BF;">|</span></span>
<span class="line"><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">sundial</span><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">Timer-Sundial</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">BUILT-IN</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">ACTIVE</span><span style="color:#ABB2BF;">|</span></span>
<span class="line"><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">timer_xl</span><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">Timer-XL</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">BUILT-IN</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">ACTIVE</span><span style="color:#ABB2BF;">|</span></span>
<span class="line"><span style="color:#61AFEF;">+---------------------+--------------------+--------+------+</span></span>
<span class="line"><span style="color:#61AFEF;">Total</span><span style="color:#98C379;"> line</span><span style="color:#98C379;"> number</span><span style="color:#98C379;"> =</span><span style="color:#D19A66;"> 10</span></span>
<span class="line"><span style="color:#61AFEF;">It</span><span style="color:#98C379;"> costs</span><span style="color:#98C379;"> 0.004s</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><p><strong>表模型推理（以 sundial 为例）</strong></p><div class="language-bash line-numbers-mode" data-highlighter="shiki" data-ext="bash" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-bash"><span class="line"><span style="color:#61AFEF;">IoTDB:etth</span><span style="color:#ABB2BF;">&gt; </span><span style="color:#98C379;">select</span><span style="color:#98C379;"> Time,</span><span style="color:#98C379;"> HUFL,HULL,MUFL,MULL,LUFL,LULL,OT</span><span style="color:#98C379;"> from</span><span style="color:#98C379;"> eg</span><span style="color:#98C379;"> LIMIT</span><span style="color:#D19A66;"> 96</span></span>
<span class="line"><span style="color:#61AFEF;">+-----------------------------+------+-----+-----+-----+-----+-----+------+</span></span>
<span class="line"><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">Time</span><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">HUFL</span><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">HULL</span><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">MUFL</span><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">MULL</span><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">LUFL</span><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">LULL</span><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">OT</span><span style="color:#ABB2BF;">|</span></span>
<span class="line"><span style="color:#61AFEF;">+-----------------------------+------+-----+-----+-----+-----+-----+------+</span></span>
<span class="line"><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">2016-07-01T00:00:00.000+08:00</span><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">5.827</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">2.009</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">1.599</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">0.462</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">4.203</span><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">1.34</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">30.531</span><span style="color:#ABB2BF;">|</span></span>
<span class="line"><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">2016-07-01T01:00:00.000+08:00</span><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">5.693</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">2.076</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">1.492</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">0.426</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">4.142</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">1.371</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">27.787</span><span style="color:#ABB2BF;">|</span></span>
<span class="line"><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">2016-07-01T02:00:00.000+08:00</span><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">5.157</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">1.741</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">1.279</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">0.355</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">3.777</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">1.218</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">27.787</span><span style="color:#ABB2BF;">|</span></span>
<span class="line"><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">2016-07-01T03:00:00.000+08:00</span><span style="color:#ABB2BF;">| </span><span style="color:#61AFEF;">5.09</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">1.942</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">1.279</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">0.391</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">3.807</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">1.279</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">25.044</span><span style="color:#ABB2BF;">|</span></span>
<span class="line"><span style="color:#56B6C2;">......</span></span>
<span class="line"><span style="color:#61AFEF;">Total</span><span style="color:#98C379;"> line</span><span style="color:#98C379;"> number</span><span style="color:#98C379;"> =</span><span style="color:#D19A66;"> 96</span></span>
<span class="line"><span style="color:#61AFEF;">It</span><span style="color:#98C379;"> costs</span><span style="color:#98C379;"> 0.119s</span></span>
<span class="line"></span>
<span class="line"><span style="color:#61AFEF;">IoTDB:etth</span><span style="color:#ABB2BF;">&gt; </span><span style="color:#98C379;">select</span><span style="color:#E5C07B;"> *</span><span style="color:#98C379;"> from</span><span style="color:#98C379;"> forecast</span><span style="color:#ABB2BF;">( </span></span>
<span class="line"><span style="color:#61AFEF;"> model_id</span><span style="color:#ABB2BF;"> =&gt; </span><span style="color:#98C379;">&#39;sundial&#39;,</span></span>
<span class="line"><span style="color:#61AFEF;"> input</span><span style="color:#ABB2BF;"> =&gt; (select </span><span style="color:#98C379;">Time,</span><span style="color:#98C379;"> ot</span><span style="color:#98C379;"> from</span><span style="color:#98C379;"> etth.eg</span><span style="color:#98C379;"> where</span><span style="color:#98C379;"> time</span><span style="color:#ABB2BF;"> &gt;</span><span style="color:#98C379;">=</span><span style="color:#98C379;"> 2016-08-07T18:00:00.000+08:00</span><span style="color:#98C379;"> limit</span><span style="color:#D19A66;"> 1440</span><span style="color:#ABB2BF;">) </span><span style="color:#98C379;">order</span><span style="color:#98C379;"> BY</span><span style="color:#98C379;"> time,</span></span>
<span class="line"><span style="color:#61AFEF;"> output_length</span><span style="color:#ABB2BF;"> =&gt; </span><span style="color:#D19A66;">96</span></span>
<span class="line"><span style="color:#ABB2BF;">)</span></span>
<span class="line"><span style="color:#61AFEF;">+-----------------------------+---------+</span></span>
<span class="line"><span style="color:#ABB2BF;">| time| </span><span style="color:#61AFEF;">ot</span><span style="color:#ABB2BF;">|</span></span>
<span class="line"><span style="color:#61AFEF;">+-----------------------------+---------+</span></span>
<span class="line"><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">2016-10-06T18:00:00.000+08:00</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">20.781654</span><span style="color:#ABB2BF;">|</span></span>
<span class="line"><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">2016-10-06T19:00:00.000+08:00</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">20.252121</span><span style="color:#ABB2BF;">|</span></span>
<span class="line"><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">2016-10-06T20:00:00.000+08:00</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">19.960138</span><span style="color:#ABB2BF;">|</span></span>
<span class="line"><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">2016-10-06T21:00:00.000+08:00</span><span style="color:#ABB2BF;">|</span><span style="color:#61AFEF;">19.662334</span><span style="color:#ABB2BF;">|</span></span>
<span class="line"><span style="color:#56B6C2;">......</span></span>
<span class="line"><span style="color:#61AFEF;">Total</span><span style="color:#98C379;"> line</span><span style="color:#98C379;"> number</span><span style="color:#98C379;"> =</span><span style="color:#D19A66;"> 96</span></span>
<span class="line"><span style="color:#61AFEF;">It</span><span style="color:#98C379;"> costs</span><span style="color:#98C379;"> 1.615s</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><h3 id="_4-5-时序大模型导入步骤" tabindex="-1"><a class="header-anchor" href="#_4-5-时序大模型导入步骤"><span>4.5 时序大模型导入步骤</span></a></h3><p>AINode 目前支持多种时序大模型，部署使用请参考<a class="route-link" href="/zh/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model.html">时序大模型</a></p><h2 id="_5-权限管理" tabindex="-1"><a class="header-anchor" href="#_5-权限管理"><span>5 权限管理</span></a></h2><p>使用AINode相关的功能时，可以使用IoTDB本身的鉴权去做一个权限管理，用户只有在具备 USE_MODEL 权限时，才可以使用模型管理的相关功能。当使用推理功能时，用户需要有访问输入模型的SQL对应的源序列的权限。</p><table><thead><tr><th style="text-align:left;"><strong>权限名称</strong></th><th style="text-align:left;"><strong>权限范围</strong></th><th style="text-align:left;"><strong>管理员用户（默认ROOT）</strong></th><th style="text-align:left;"><strong>普通用户</strong></th><th style="text-align:left;"><strong>路径相关</strong></th></tr></thead><tbody><tr><td style="text-align:left;">USE_MODEL</td><td style="text-align:left;">create model / show models / drop model</td><td style="text-align:left;">√</td><td style="text-align:left;">√</td><td style="text-align:left;">x</td></tr><tr><td style="text-align:left;">READ_DATA</td><td style="text-align:left;">call inference</td><td style="text-align:left;">√</td><td style="text-align:left;">√</td><td style="text-align:left;">√</td></tr></tbody></table><h2 id="_6-附录" tabindex="-1"><a class="header-anchor" href="#_6-附录"><span>6 附录</span></a></h2><p><strong>Arima</strong></p><table><thead><tr><th style="text-align:left;">支持的参数</th><th style="text-align:left;">含义</th><th style="text-align:left;">默认值</th></tr></thead><tbody><tr><td style="text-align:left;">order</td><td style="text-align:left;">ARIMA模型的阶数 <code>(p, d, q)</code>：p是自回归阶数，d是差分阶数，q是滑动平均阶数。</td><td style="text-align:left;">(1,0,0)</td></tr><tr><td style="text-align:left;">seansonal_order</td><td style="text-align:left;">季节性ARIMA的阶数 <code>(P, D, Q, s)</code>：分别为季节性自回归、差分、滑动平均阶数，s是季节周期（如12代表月度数据）。</td><td style="text-align:left;">(0,0,0,0)</td></tr><tr><td style="text-align:left;">method</td><td style="text-align:left;">优化器选择，可选：&#39;newton&#39;、&#39;nm&#39;、&#39;bfgs&#39;、&#39;lbfgs&#39;、&#39;powell&#39;、&#39;cg&#39;、&#39;ncg&#39;、&#39;basinhopping&#39;。</td><td style="text-align:left;">&#39;lbfgs&#39;</td></tr><tr><td style="text-align:left;">maxiter</td><td style="text-align:left;">最大迭代次数或函数评估次数。</td><td style="text-align:left;">50</td></tr><tr><td style="text-align:left;">out_of_sample_size</td><td style="text-align:left;">用于验证的时间序列尾部样本数，模型不在这些样本上拟合。</td><td style="text-align:left;">0</td></tr><tr><td style="text-align:left;">scoring</td><td style="text-align:left;">验证时使用的评分函数，字符串需为 sklearn 中可导入的评分指标，或用户自定义函数。</td><td style="text-align:left;">&#39;mse&#39;</td></tr><tr><td style="text-align:left;">trend</td><td style="text-align:left;">趋势项配置，若 with_intercept=True 且此项为 None，则默认使用 &#39;c&#39;（包含常数项）。</td><td style="text-align:left;">None</td></tr><tr><td style="text-align:left;">with_intercept</td><td style="text-align:left;">是否包含截距项。</td><td style="text-align:left;">True</td></tr><tr><td style="text-align:left;">time_varying_regression</td><td style="text-align:left;">是否允许回归系数随时间变化。</td><td style="text-align:left;">False</td></tr><tr><td style="text-align:left;">enforce_stationarity</td><td style="text-align:left;">是否强制AR部分平稳性。</td><td style="text-align:left;">True</td></tr><tr><td style="text-align:left;">enforce_invertibility</td><td style="text-align:left;">是否强制MA部分可逆性。</td><td style="text-align:left;">True</td></tr><tr><td style="text-align:left;">simple_differnecing</td><td style="text-align:left;">是否使用差分后的数据估计（牺牲前几行数据换取更简状态空间）。</td><td style="text-align:left;">False</td></tr><tr><td style="text-align:left;">measurement_error</td><td style="text-align:left;">是否认为观测值中含有误差。</td><td style="text-align:left;">False</td></tr><tr><td style="text-align:left;">mle_regression</td><td style="text-align:left;">是否使用极大似然估计回归系数，若 <code>time_varying_regression=True</code> 则必须为 False。</td><td style="text-align:left;">True</td></tr><tr><td style="text-align:left;">hamilton_representation</td><td style="text-align:left;">是否使用 Hamilton 表达方式（默认用 Harvey）。</td><td style="text-align:left;">False</td></tr><tr><td style="text-align:left;">concentrate_scale</td><td style="text-align:left;">是否从似然函数中排除误差方差参数，减少待估参数个数（但无法获得误差项方差的标准误）。</td><td style="text-align:left;">False</td></tr></tbody></table><p><strong>NaiveForecaster</strong></p><table><thead><tr><th>支持的参数</th><th>含义</th><th>默认值</th></tr></thead><tbody><tr><td>strategy</td><td>预测策略： • <code>&quot;last&quot;</code>：预测训练集最后一个值；若设置了季节周期（<code>sp</code>&gt;1），则每个季节分别预测其最后一个周期值。对 NaN 值鲁棒。 • <code>&quot;mean&quot;</code>：预测最后窗口中的平均值；若 <code>sp</code>&gt;1，按每个季节分别计算均值。对 NaN 值鲁棒。 • <code>&quot;drift&quot;</code>：用最后窗口的首尾点拟合一条直线并外推预测。对 NaN 值不鲁棒。</td><td>&quot;last&quot;</td></tr><tr><td>sp</td><td>季节性周期。若为 <code>None</code>，等效于 <code>1</code>，表示无季节性；如果设为 12，表示每 12 个单位（如月）为一个周期。</td><td>1</td></tr></tbody></table><ul><li>STLForecaster</li></ul><table><thead><tr><th style="text-align:left;">支持的参数</th><th style="text-align:left;">含义</th><th style="text-align:left;">默认值</th></tr></thead><tbody><tr><td style="text-align:left;">sp</td><td style="text-align:left;">季节周期长度（周期性单位数）。传入 statsmodels 的 STL 中。</td><td style="text-align:left;">2</td></tr><tr><td style="text-align:left;">seasonal</td><td style="text-align:left;">季节项平滑窗口长度，必须为 ≥3 的奇数，通常建议 ≥7。</td><td style="text-align:left;">7</td></tr><tr><td style="text-align:left;">seasonal_deg</td><td style="text-align:left;">季节项 LOESS 的多项式阶数（0 表示常数，1 表示线性）。</td><td style="text-align:left;">1</td></tr><tr><td style="text-align:left;">trend_deg</td><td style="text-align:left;">趋势项 LOESS 的多项式阶数（0 或 1）。</td><td style="text-align:left;">1</td></tr><tr><td style="text-align:left;">low_pass_deg</td><td style="text-align:left;">低通项 LOESS 的多项式阶数（0 或 1）。</td><td style="text-align:left;">1</td></tr><tr><td style="text-align:left;">seasonal_jump</td><td style="text-align:left;">LOESS 拟合的插值步长（季节项），每 n 点拟合一次，中间插值。值越大，估计速度越快。</td><td style="text-align:left;">1</td></tr><tr><td style="text-align:left;">trend_jump</td><td style="text-align:left;">趋势项插值步长，越大速度越快但精度可能下降。</td><td style="text-align:left;">1</td></tr><tr><td style="text-align:left;">low_pass_jump</td><td style="text-align:left;">低通项插值步长，设置同上。</td><td style="text-align:left;">1</td></tr></tbody></table><p><strong>ExponentialSmoothing (HoltWinters)</strong></p><table><thead><tr><th style="text-align:left;">支持的参数</th><th style="text-align:left;">含义</th><th style="text-align:left;">默认值</th></tr></thead><tbody><tr><td style="text-align:left;">damped_trend</td><td style="text-align:left;">是否使用阻尼趋势（趋势会逐渐平缓，而非无限增长）。</td><td style="text-align:left;">True</td></tr><tr><td style="text-align:left;">initialization_method</td><td style="text-align:left;">初始化方法： • <code>&quot;estimated&quot;</code>：通过拟合估计初始状态 • <code>&quot;heuristic&quot;</code>：使用启发式方法估计初始水平/趋势/季节 • <code>&quot;known&quot;</code>：用户显式提供所有初始值 • <code>&quot;legacy-heuristic&quot;</code>：旧版本兼容方式</td><td style="text-align:left;">&quot;estimated&quot;</td></tr><tr><td style="text-align:left;">optmized</td><td style="text-align:left;">是否通过最大化对数似然来优化参数。</td><td style="text-align:left;">True</td></tr><tr><td style="text-align:left;">remove_bias</td><td style="text-align:left;">是否移除偏差，使预测值和拟合值的残差平均值为0。</td><td style="text-align:left;">False</td></tr><tr><td style="text-align:left;">use_brute</td><td style="text-align:left;">是否使用穷举法（网格搜索）来寻找初始参数。否则使用启发式初始值。</td><td style="text-align:left;"></td></tr></tbody></table></div><!----><!----><!----></div><footer class="vp-page-meta"><div class="vp-meta-item edit-link"><a class="auto-link external-link vp-meta-label" href="https://github.com/apache/iotdb-docs/edit/main/src/zh/UserGuide/latest-Table/AI-capability/AINode_apache.md" aria-label="发现错误？在 GitHub 上编辑此页" rel="noopener noreferrer" 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