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href="/zh/UserGuide/V1.1.x/Operators-Functions/Data-Repairing.html"><!---->数据修复<!----></a><ul class="vp-sidebar-sub-headers"><li class="vp-sidebar-sub-header"><a class="vp-link nav-link vp-sidebar-link vp-heading" href="/zh/UserGuide/V1.1.x/Operators-Functions/Data-Repairing.html#数据修复"><!---->数据修复<!----></a><ul class="vp-sidebar-sub-headers"><li class="vp-sidebar-sub-header"><a class="vp-link nav-link vp-sidebar-link vp-heading" href="/zh/UserGuide/V1.1.x/Operators-Functions/Data-Repairing.html#timestamprepair"><!---->TimestampRepair<!----></a><ul class="vp-sidebar-sub-headers"></ul></li><li class="vp-sidebar-sub-header"><a class="vp-link nav-link vp-sidebar-link vp-heading" href="/zh/UserGuide/V1.1.x/Operators-Functions/Data-Repairing.html#valuefill"><!---->ValueFill<!----></a><ul class="vp-sidebar-sub-headers"></ul></li><li class="vp-sidebar-sub-header"><a class="vp-link nav-link vp-sidebar-link vp-heading" href="/zh/UserGuide/V1.1.x/Operators-Functions/Data-Repairing.html#valuerepair"><!---->ValueRepair<!----></a><ul class="vp-sidebar-sub-headers"></ul></li><li class="vp-sidebar-sub-header"><a class="vp-link nav-link vp-sidebar-link vp-heading" href="/zh/UserGuide/V1.1.x/Operators-Functions/Data-Repairing.html#masterrepair"><!---->MasterRepair<!----></a><ul class="vp-sidebar-sub-headers"></ul></li><li class="vp-sidebar-sub-header"><a class="vp-link nav-link vp-sidebar-link vp-heading" href="/zh/UserGuide/V1.1.x/Operators-Functions/Data-Repairing.html#seasonalrepair"><!---->SeasonalRepair<!----></a><ul class="vp-sidebar-sub-headers"></ul></li></ul></li></ul><!--]--></li><li><!--[--><a class="vp-link nav-link vp-sidebar-link vp-sidebar-page" href="/zh/UserGuide/V1.1.x/Operators-Functions/Series-Discovery.html"><!---->序列发现<!----></a><ul class="vp-sidebar-sub-headers"></ul><!--]--></li><li><!--[--><a class="vp-link nav-link vp-sidebar-link vp-sidebar-page" 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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 12 分钟</span><meta property="timeRequired" content="PT12M"></span><!----><!----></div><hr></div><div class="toc-place-holder"><aside id="toc"><!--[--><!----><!--]--><div class="toc-header">此页内容<button type="button" class="print-button" title="打印"><svg xmlns="http://www.w3.org/2000/svg" class="icon print-icon" viewBox="0 0 1024 1024" fill="currentColor" aria-label="print icon"><path d="M819.2 364.8h-44.8V128c0-17.067-14.933-32-32-32H281.6c-17.067 0-32 14.933-32 32v236.8h-44.8C145.067 364.8 96 413.867 96 473.6v192c0 59.733 49.067 108.8 108.8 108.8h44.8V896c0 17.067 14.933 32 32 32h460.8c17.067 0 32-14.933 32-32V774.4h44.8c59.733 0 108.8-49.067 108.8-108.8v-192c0-59.733-49.067-108.8-108.8-108.8zM313.6 160h396.8v204.8H313.6V160zm396.8 704H313.6V620.8h396.8V864zM864 665.6c0 25.6-19.2 44.8-44.8 44.8h-44.8V588.8c0-17.067-14.933-32-32-32H281.6c-17.067 0-32 14.933-32 32v121.6h-44.8c-25.6 0-44.8-19.2-44.8-44.8v-192c0-25.6 19.2-44.8 44.8-44.8h614.4c25.6 0 44.8 19.2 44.8 44.8v192z"></path></svg></button></div><div class="toc-wrapper"><ul class="toc-list"><!--[--><li class="toc-item"><a class="vp-link toc-link level2" href="/#数据修复">数据修复</a></li><li><ul class="toc-list"><!--[--><li class="toc-item"><a class="vp-link toc-link level3" href="/#timestamprepair">TimestampRepair</a></li><!----><!--]--><!--[--><li class="toc-item"><a class="vp-link toc-link level3" href="/#valuefill">ValueFill</a></li><!----><!--]--><!--[--><li class="toc-item"><a class="vp-link toc-link level3" href="/#valuerepair">ValueRepair</a></li><!----><!--]--><!--[--><li class="toc-item"><a class="vp-link toc-link level3" href="/#masterrepair">MasterRepair</a></li><!----><!--]--><!--[--><li class="toc-item"><a class="vp-link toc-link level3" href="/#seasonalrepair">SeasonalRepair</a></li><!----><!--]--></ul></li><!--]--></ul><div class="toc-marker" style="top:-1.7rem;"></div></div><!--[--><!----><!--]--></aside></div><!----><div class="theme-hope-content"><!-- |
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| --><h2 id="数据修复" tabindex="-1"><a class="header-anchor" href="#数据修复" aria-hidden="true">#</a> 数据修复</h2><h3 id="timestamprepair" tabindex="-1"><a class="header-anchor" href="#timestamprepair" aria-hidden="true">#</a> TimestampRepair</h3><h4 id="函数简介" tabindex="-1"><a class="header-anchor" href="#函数简介" aria-hidden="true">#</a> 函数简介</h4><p>本函数用于时间戳修复。根据给定的标准时间间隔，采用最小化修复代价的方法，通过对数据时间戳的微调，将原本时间戳间隔不稳定的数据修复为严格等间隔的数据。在未给定标准时间间隔的情况下，本函数将使用时间间隔的中位数 (median)、众数 (mode) 或聚类中心 (cluster) 来推算标准时间间隔。</p><p><strong>函数名：</strong> TIMESTAMPREPAIR</p><p><strong>输入序列：</strong> 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE</p><p><strong>参数：</strong></p><ul><li><code>interval</code>: 标准时间间隔（单位是毫秒），是一个正整数。在缺省情况下，将根据指定的方法推算。</li><li><code>method</code>：推算标准时间间隔的方法，取值为 'median', 'mode' 或 'cluster'，仅在<code>interval</code>缺省时有效。在缺省情况下，将使用中位数方法进行推算。</li></ul><p><strong>输出序列：</strong> 输出单个序列，类型与输入序列相同。该序列是修复后的输入序列。</p><h4 id="使用示例" tabindex="-1"><a class="header-anchor" href="#使用示例" aria-hidden="true">#</a> 使用示例</h4><h5 id="指定标准时间间隔" tabindex="-1"><a class="header-anchor" href="#指定标准时间间隔" aria-hidden="true">#</a> 指定标准时间间隔</h5><p>在给定<code>interval</code>参数的情况下，本函数将按照指定的标准时间间隔进行修复。</p><p>输入序列：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+---------------+ |
| | Time|root.test.d2.s1| |
| +-----------------------------+---------------+ |
| |2021-07-01T12:00:00.000+08:00| 1.0| |
| |2021-07-01T12:00:10.000+08:00| 2.0| |
| |2021-07-01T12:00:19.000+08:00| 3.0| |
| |2021-07-01T12:00:30.000+08:00| 4.0| |
| |2021-07-01T12:00:40.000+08:00| 5.0| |
| |2021-07-01T12:00:50.000+08:00| 6.0| |
| |2021-07-01T12:01:01.000+08:00| 7.0| |
| |2021-07-01T12:01:11.000+08:00| 8.0| |
| |2021-07-01T12:01:21.000+08:00| 9.0| |
| |2021-07-01T12:01:31.000+08:00| 10.0| |
| +-----------------------------+---------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><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>用于查询的SQL语句：</p><div class="language-sql line-numbers-mode" data-ext="sql"><pre class="language-sql"><code><span class="token keyword">select</span> timestamprepair<span class="token punctuation">(</span>s1<span class="token punctuation">,</span><span class="token string">'interval'</span><span class="token operator">=</span><span class="token string">'10000'</span><span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test<span class="token punctuation">.</span>d2 |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>输出序列：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+----------------------------------------------------+ |
| | Time|timestamprepair(root.test.d2.s1, "interval"="10000")| |
| +-----------------------------+----------------------------------------------------+ |
| |2021-07-01T12:00:00.000+08:00| 1.0| |
| |2021-07-01T12:00:10.000+08:00| 2.0| |
| |2021-07-01T12:00:20.000+08:00| 3.0| |
| |2021-07-01T12:00:30.000+08:00| 4.0| |
| |2021-07-01T12:00:40.000+08:00| 5.0| |
| |2021-07-01T12:00:50.000+08:00| 6.0| |
| |2021-07-01T12:01:00.000+08:00| 7.0| |
| |2021-07-01T12:01:10.000+08:00| 8.0| |
| |2021-07-01T12:01:20.000+08:00| 9.0| |
| |2021-07-01T12:01:30.000+08:00| 10.0| |
| +-----------------------------+----------------------------------------------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><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><h5 id="自动推算标准时间间隔" tabindex="-1"><a class="header-anchor" href="#自动推算标准时间间隔" aria-hidden="true">#</a> 自动推算标准时间间隔</h5><p>如果<code>interval</code>参数没有给定，本函数将按照推算的标准时间间隔进行修复。</p><p>输入序列同上，用于查询的 SQL 语句如下：</p><div class="language-sql line-numbers-mode" data-ext="sql"><pre class="language-sql"><code><span class="token keyword">select</span> timestamprepair<span class="token punctuation">(</span>s1<span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test<span class="token punctuation">.</span>d2 |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>输出序列：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+--------------------------------+ |
| | Time|timestamprepair(root.test.d2.s1)| |
| +-----------------------------+--------------------------------+ |
| |2021-07-01T12:00:00.000+08:00| 1.0| |
| |2021-07-01T12:00:10.000+08:00| 2.0| |
| |2021-07-01T12:00:20.000+08:00| 3.0| |
| |2021-07-01T12:00:30.000+08:00| 4.0| |
| |2021-07-01T12:00:40.000+08:00| 5.0| |
| |2021-07-01T12:00:50.000+08:00| 6.0| |
| |2021-07-01T12:01:00.000+08:00| 7.0| |
| |2021-07-01T12:01:10.000+08:00| 8.0| |
| |2021-07-01T12:01:20.000+08:00| 9.0| |
| |2021-07-01T12:01:30.000+08:00| 10.0| |
| +-----------------------------+--------------------------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><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="valuefill" tabindex="-1"><a class="header-anchor" href="#valuefill" aria-hidden="true">#</a> ValueFill</h3><h4 id="函数简介-1" tabindex="-1"><a class="header-anchor" href="#函数简介-1" aria-hidden="true">#</a> 函数简介</h4><p><strong>函数名：</strong> ValueFill</p><p><strong>输入序列：</strong> 单列时序数据，类型为INT32 / INT64 / FLOAT / DOUBLE</p><p><strong>参数：</strong></p><ul><li><code>method</code>: {"mean", "previous", "linear", "likelihood", "AR", "MA", "SCREEN"}, 默认为 "linear"。其中，“mean” 指使用均值填补的方法； “previous" 指使用前值填补方法；“linear" 指使用线性插值填补方法；“likelihood” 为基于速度的正态分布的极大似然估计方法；“AR” 指自回归的填补方法；“MA” 指滑动平均的填补方法；"SCREEN" 指约束填补方法；缺省情况下使用 “linear”。</li></ul><p><strong>输出序列：</strong> 填补后的单维序列。</p><p><strong>备注：</strong> AR 模型采用 AR(1)，时序列需满足自相关条件，否则将输出单个数据点 (0, 0.0).</p><h4 id="使用示例-1" tabindex="-1"><a class="header-anchor" href="#使用示例-1" aria-hidden="true">#</a> 使用示例</h4><h5 id="使用-linear-方法进行填补" tabindex="-1"><a class="header-anchor" href="#使用-linear-方法进行填补" aria-hidden="true">#</a> 使用 linear 方法进行填补</h5><p>当<code>method</code>缺省或取值为 'linear' 时，本函数将使用线性插值方法进行填补。</p><p>输入序列：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+---------------+ |
| | Time|root.test.d2.s1| |
| +-----------------------------+---------------+ |
| |2020-01-01T00:00:02.000+08:00| NaN| |
| |2020-01-01T00:00:03.000+08:00| 101.0| |
| |2020-01-01T00:00:04.000+08:00| 102.0| |
| |2020-01-01T00:00:06.000+08:00| 104.0| |
| |2020-01-01T00:00:08.000+08:00| 126.0| |
| |2020-01-01T00:00:10.000+08:00| 108.0| |
| |2020-01-01T00:00:14.000+08:00| NaN| |
| |2020-01-01T00:00:15.000+08:00| 113.0| |
| |2020-01-01T00:00:16.000+08:00| 114.0| |
| |2020-01-01T00:00:18.000+08:00| 116.0| |
| |2020-01-01T00:00:20.000+08:00| NaN| |
| |2020-01-01T00:00:22.000+08:00| NaN| |
| |2020-01-01T00:00:26.000+08:00| 124.0| |
| |2020-01-01T00:00:28.000+08:00| 126.0| |
| |2020-01-01T00:00:30.000+08:00| 128.0| |
| +-----------------------------+---------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><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><p>用于查询的 SQL 语句：</p><div class="language-sql line-numbers-mode" data-ext="sql"><pre class="language-sql"><code><span class="token keyword">select</span> valuefill<span class="token punctuation">(</span>s1<span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test<span class="token punctuation">.</span>d2 |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>输出序列：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+-----------------------+ |
| | Time|valuefill(root.test.d2)| |
| +-----------------------------+-----------------------+ |
| |2020-01-01T00:00:02.000+08:00| NaN| |
| |2020-01-01T00:00:03.000+08:00| 101.0| |
| |2020-01-01T00:00:04.000+08:00| 102.0| |
| |2020-01-01T00:00:06.000+08:00| 104.0| |
| |2020-01-01T00:00:08.000+08:00| 126.0| |
| |2020-01-01T00:00:10.000+08:00| 108.0| |
| |2020-01-01T00:00:14.000+08:00| 108.0| |
| |2020-01-01T00:00:15.000+08:00| 113.0| |
| |2020-01-01T00:00:16.000+08:00| 114.0| |
| |2020-01-01T00:00:18.000+08:00| 116.0| |
| |2020-01-01T00:00:20.000+08:00| 118.7| |
| |2020-01-01T00:00:22.000+08:00| 121.3| |
| |2020-01-01T00:00:26.000+08:00| 124.0| |
| |2020-01-01T00:00:28.000+08:00| 126.0| |
| |2020-01-01T00:00:30.000+08:00| 128.0| |
| +-----------------------------+-----------------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><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><h5 id="使用-previous-方法进行填补" tabindex="-1"><a class="header-anchor" href="#使用-previous-方法进行填补" aria-hidden="true">#</a> 使用 previous 方法进行填补</h5><p>当<code>method</code>取值为 'previous' 时，本函数将使前值填补方法进行数值填补。</p><p>输入序列同上，用于查询的 SQL 语句如下：</p><div class="language-sql line-numbers-mode" data-ext="sql"><pre class="language-sql"><code><span class="token keyword">select</span> valuefill<span class="token punctuation">(</span>s1<span class="token punctuation">,</span><span class="token string">"method"</span><span class="token operator">=</span><span class="token string">"previous"</span><span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test<span class="token punctuation">.</span>d2 |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>输出序列：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+-------------------------------------------+ |
| | Time|valuefill(root.test.d2,"method"="previous")| |
| +-----------------------------+-------------------------------------------+ |
| |2020-01-01T00:00:02.000+08:00| NaN| |
| |2020-01-01T00:00:03.000+08:00| 101.0| |
| |2020-01-01T00:00:04.000+08:00| 102.0| |
| |2020-01-01T00:00:06.000+08:00| 104.0| |
| |2020-01-01T00:00:08.000+08:00| 126.0| |
| |2020-01-01T00:00:10.000+08:00| 108.0| |
| |2020-01-01T00:00:14.000+08:00| 110.5| |
| |2020-01-01T00:00:15.000+08:00| 113.0| |
| |2020-01-01T00:00:16.000+08:00| 114.0| |
| |2020-01-01T00:00:18.000+08:00| 116.0| |
| |2020-01-01T00:00:20.000+08:00| 116.0| |
| |2020-01-01T00:00:22.000+08:00| 116.0| |
| |2020-01-01T00:00:26.000+08:00| 124.0| |
| |2020-01-01T00:00:28.000+08:00| 126.0| |
| |2020-01-01T00:00:30.000+08:00| 128.0| |
| +-----------------------------+-------------------------------------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><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="valuerepair" tabindex="-1"><a class="header-anchor" href="#valuerepair" aria-hidden="true">#</a> ValueRepair</h3><h4 id="函数简介-2" tabindex="-1"><a class="header-anchor" href="#函数简介-2" aria-hidden="true">#</a> 函数简介</h4><p>本函数用于对时间序列的数值进行修复。目前，本函数支持两种修复方法：<strong>Screen</strong> 是一种基于速度阈值的方法，在最小改动的前提下使得所有的速度符合阈值要求；<strong>LsGreedy</strong> 是一种基于速度变化似然的方法，将速度变化建模为高斯分布，并采用贪心算法极大化似然函数。</p><p><strong>函数名：</strong> VALUEREPAIR</p><p><strong>输入序列：</strong> 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。</p><p><strong>参数：</strong></p><ul><li><code>method</code>：修复时采用的方法，取值为 'Screen' 或 'LsGreedy'. 在缺省情况下，使用 Screen 方法进行修复。</li><li><code>minSpeed</code>：该参数仅在使用 Screen 方法时有效。当速度小于该值时会被视作数值异常点加以修复。在缺省情况下为中位数减去三倍绝对中位差。</li><li><code>maxSpeed</code>：该参数仅在使用 Screen 方法时有效。当速度大于该值时会被视作数值异常点加以修复。在缺省情况下为中位数加上三倍绝对中位差。</li><li><code>center</code>：该参数仅在使用 LsGreedy 方法时有效。对速度变化分布建立的高斯模型的中心。在缺省情况下为 0。</li><li><code>sigma</code> ：该参数仅在使用 LsGreedy 方法时有效。对速度变化分布建立的高斯模型的标准差。在缺省情况下为绝对中位差。</li></ul><p><strong>输出序列：</strong> 输出单个序列，类型与输入序列相同。该序列是修复后的输入序列。</p><p><strong>提示：</strong> 输入序列中的<code>NaN</code>在修复之前会先进行线性插值填补。</p><h4 id="使用示例-2" tabindex="-1"><a class="header-anchor" href="#使用示例-2" aria-hidden="true">#</a> 使用示例</h4><h5 id="使用-screen-方法进行修复" tabindex="-1"><a class="header-anchor" href="#使用-screen-方法进行修复" aria-hidden="true">#</a> 使用 Screen 方法进行修复</h5><p>当<code>method</code>缺省或取值为 'Screen' 时，本函数将使用 Screen 方法进行数值修复。</p><p>输入序列：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+---------------+ |
| | Time|root.test.d2.s1| |
| +-----------------------------+---------------+ |
| |2020-01-01T00:00:02.000+08:00| 100.0| |
| |2020-01-01T00:00:03.000+08:00| 101.0| |
| |2020-01-01T00:00:04.000+08:00| 102.0| |
| |2020-01-01T00:00:06.000+08:00| 104.0| |
| |2020-01-01T00:00:08.000+08:00| 126.0| |
| |2020-01-01T00:00:10.000+08:00| 108.0| |
| |2020-01-01T00:00:14.000+08:00| 112.0| |
| |2020-01-01T00:00:15.000+08:00| 113.0| |
| |2020-01-01T00:00:16.000+08:00| 114.0| |
| |2020-01-01T00:00:18.000+08:00| 116.0| |
| |2020-01-01T00:00:20.000+08:00| 118.0| |
| |2020-01-01T00:00:22.000+08:00| 100.0| |
| |2020-01-01T00:00:26.000+08:00| 124.0| |
| |2020-01-01T00:00:28.000+08:00| 126.0| |
| |2020-01-01T00:00:30.000+08:00| NaN| |
| +-----------------------------+---------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><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><p>用于查询的 SQL 语句：</p><div class="language-sql line-numbers-mode" data-ext="sql"><pre class="language-sql"><code><span class="token keyword">select</span> valuerepair<span class="token punctuation">(</span>s1<span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test<span class="token punctuation">.</span>d2 |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>输出序列：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+----------------------------+ |
| | Time|valuerepair(root.test.d2.s1)| |
| +-----------------------------+----------------------------+ |
| |2020-01-01T00:00:02.000+08:00| 100.0| |
| |2020-01-01T00:00:03.000+08:00| 101.0| |
| |2020-01-01T00:00:04.000+08:00| 102.0| |
| |2020-01-01T00:00:06.000+08:00| 104.0| |
| |2020-01-01T00:00:08.000+08:00| 106.0| |
| |2020-01-01T00:00:10.000+08:00| 108.0| |
| |2020-01-01T00:00:14.000+08:00| 112.0| |
| |2020-01-01T00:00:15.000+08:00| 113.0| |
| |2020-01-01T00:00:16.000+08:00| 114.0| |
| |2020-01-01T00:00:18.000+08:00| 116.0| |
| |2020-01-01T00:00:20.000+08:00| 118.0| |
| |2020-01-01T00:00:22.000+08:00| 120.0| |
| |2020-01-01T00:00:26.000+08:00| 124.0| |
| |2020-01-01T00:00:28.000+08:00| 126.0| |
| |2020-01-01T00:00:30.000+08:00| 128.0| |
| +-----------------------------+----------------------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><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><h5 id="使用-lsgreedy-方法进行修复" tabindex="-1"><a class="header-anchor" href="#使用-lsgreedy-方法进行修复" aria-hidden="true">#</a> 使用 LsGreedy 方法进行修复</h5><p>当<code>method</code>取值为 'LsGreedy' 时，本函数将使用 LsGreedy 方法进行数值修复。</p><p>输入序列同上，用于查询的 SQL 语句如下：</p><div class="language-sql line-numbers-mode" data-ext="sql"><pre class="language-sql"><code><span class="token keyword">select</span> valuerepair<span class="token punctuation">(</span>s1<span class="token punctuation">,</span><span class="token string">'method'</span><span class="token operator">=</span><span class="token string">'LsGreedy'</span><span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test<span class="token punctuation">.</span>d2 |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>输出序列：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+-------------------------------------------------+ |
| | Time|valuerepair(root.test.d2.s1, "method"="LsGreedy")| |
| +-----------------------------+-------------------------------------------------+ |
| |2020-01-01T00:00:02.000+08:00| 100.0| |
| |2020-01-01T00:00:03.000+08:00| 101.0| |
| |2020-01-01T00:00:04.000+08:00| 102.0| |
| |2020-01-01T00:00:06.000+08:00| 104.0| |
| |2020-01-01T00:00:08.000+08:00| 106.0| |
| |2020-01-01T00:00:10.000+08:00| 108.0| |
| |2020-01-01T00:00:14.000+08:00| 112.0| |
| |2020-01-01T00:00:15.000+08:00| 113.0| |
| |2020-01-01T00:00:16.000+08:00| 114.0| |
| |2020-01-01T00:00:18.000+08:00| 116.0| |
| |2020-01-01T00:00:20.000+08:00| 118.0| |
| |2020-01-01T00:00:22.000+08:00| 120.0| |
| |2020-01-01T00:00:26.000+08:00| 124.0| |
| |2020-01-01T00:00:28.000+08:00| 126.0| |
| |2020-01-01T00:00:30.000+08:00| 128.0| |
| +-----------------------------+-------------------------------------------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><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="masterrepair" tabindex="-1"><a class="header-anchor" href="#masterrepair" aria-hidden="true">#</a> MasterRepair</h3><h4 id="函数简介-3" tabindex="-1"><a class="header-anchor" href="#函数简介-3" aria-hidden="true">#</a> 函数简介</h4><p>本函数实现基于主数据的时间序列数据修复。</p><p>**函数名：**MasterRepair</p><p><strong>输入序列：</strong> 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。</p><p><strong>参数：</strong></p><ul><li><code>omega</code>：算法窗口大小，非负整数（单位为毫秒）， 在缺省情况下，算法根据不同时间差下的两个元组距离自动估计该参数。</li><li><code>eta</code>：算法距离阈值，正数， 在缺省情况下，算法根据窗口中元组的距离分布自动估计该参数。</li><li><code>k</code>：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。</li><li><code>output_column</code>：输出列的序号，默认输出第一列的修复结果。</li></ul><p>**输出序列：**输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。</p><h4 id="使用示例-3" tabindex="-1"><a class="header-anchor" href="#使用示例-3" aria-hidden="true">#</a> 使用示例</h4><p>输入序列：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+------------+------------+------------+------------+------------+------------+ |
| | Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3| |
| +-----------------------------+------------+------------+------------+------------+------------+------------+ |
| |2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195| 1704| 1154.55| 0.195| |
| |2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193| 1702| 1152.30| 0.193| |
| |2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192| 1702| 1148.65| 0.192| |
| |2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194| 1701| 1145.20| 0.194| |
| |2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195| 1703| 1150.55| 0.195| |
| |2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193| 1704| 1151.55| 0.193| |
| |2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194| 1705| 1153.55| 0.194| |
| |2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190| 1706| 1152.30| 0.190| |
| +-----------------------------+------------+------------+------------+------------+------------+------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><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>用于查询的 SQL 语句：</p><div class="language-sql line-numbers-mode" data-ext="sql"><pre class="language-sql"><code><span class="token keyword">select</span> MasterRepair<span class="token punctuation">(</span>t1<span class="token punctuation">,</span>t2<span class="token punctuation">,</span>t3<span class="token punctuation">,</span>m1<span class="token punctuation">,</span>m2<span class="token punctuation">,</span>m3<span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>输出序列：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+-------------------------------------------------------------------------------------------+ |
| | Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)| |
| +-----------------------------+-------------------------------------------------------------------------------------------+ |
| |2021-07-01T12:00:01.000+08:00| 1704| |
| |2021-07-01T12:00:02.000+08:00| 1702| |
| |2021-07-01T12:00:03.000+08:00| 1702| |
| |2021-07-01T12:00:04.000+08:00| 1701| |
| |2021-07-01T12:00:07.000+08:00| 1703| |
| |2021-07-01T12:00:08.000+08:00| 1704| |
| |2021-07-01T12:01:09.000+08:00| 1705| |
| |2021-07-01T12:01:10.000+08:00| 1706| |
| +-----------------------------+-------------------------------------------------------------------------------------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><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="seasonalrepair" tabindex="-1"><a class="header-anchor" href="#seasonalrepair" aria-hidden="true">#</a> SeasonalRepair</h3><h4 id="函数简介-4" tabindex="-1"><a class="header-anchor" href="#函数简介-4" aria-hidden="true">#</a> 函数简介</h4><p>本函数用于对周期性时间序列的数值进行基于分解的修复。目前，本函数支持两种方法：<strong>Classical</strong>使用经典分解方法得到的残差项检测数值的异常波动，并使用滑动平均修复序列；<strong>Improved</strong>使用改进的分解方法得到的残差项检测数值的异常波动，并使用滑动中值修复序列。</p><p><strong>函数名：</strong> SEASONALREPAIR</p><p><strong>输入序列：</strong> 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。</p><p><strong>参数：</strong></p><ul><li><code>method</code>：修复时采用的分解方法，取值为'Classical'或'Improved'。在缺省情况下，使用经典分解方法进行修复。</li><li><code>period</code>：序列的周期。</li><li><code>k</code>：残差项的范围阈值，用来限制残差项偏离中心的程度。在缺省情况下为9。</li><li><code>max_iter</code>：算法的最大迭代次数。在缺省情况下为10。</li></ul><p><strong>输出序列：</strong> 输出单个序列，类型与输入序列相同。该序列是修复后的输入序列。</p><p><strong>提示：</strong> 输入序列中的<code>NaN</code>在修复之前会先进行线性插值填补。</p><h4 id="使用示例-4" tabindex="-1"><a class="header-anchor" href="#使用示例-4" aria-hidden="true">#</a> 使用示例</h4><h5 id="使用经典分解方法进行修复" tabindex="-1"><a class="header-anchor" href="#使用经典分解方法进行修复" aria-hidden="true">#</a> 使用经典分解方法进行修复</h5><p>当<code>method</code>缺省或取值为'Classical'时，本函数将使用经典分解方法进行数值修复。</p><p>输入序列：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+---------------+ |
| | Time|root.test.d2.s1| |
| +-----------------------------+---------------+ |
| |2020-01-01T00:00:02.000+08:00| 100.0| |
| |2020-01-01T00:00:04.000+08:00| 120.0| |
| |2020-01-01T00:00:06.000+08:00| 80.0| |
| |2020-01-01T00:00:08.000+08:00| 100.5| |
| |2020-01-01T00:00:10.000+08:00| 119.5| |
| |2020-01-01T00:00:12.000+08:00| 101.0| |
| |2020-01-01T00:00:14.000+08:00| 99.5| |
| |2020-01-01T00:00:16.000+08:00| 119.0| |
| |2020-01-01T00:00:18.000+08:00| 80.5| |
| |2020-01-01T00:00:20.000+08:00| 99.0| |
| |2020-01-01T00:00:22.000+08:00| 121.0| |
| |2020-01-01T00:00:24.000+08:00| 79.5| |
| +-----------------------------+---------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><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>用于查询的SQL语句：</p><div class="language-sql line-numbers-mode" data-ext="sql"><pre class="language-sql"><code><span class="token keyword">select</span> seasonalrepair<span class="token punctuation">(</span>s1<span class="token punctuation">,</span><span class="token string">'period'</span><span class="token operator">=</span><span class="token number">3</span><span class="token punctuation">,</span><span class="token string">'k'</span><span class="token operator">=</span><span class="token number">2</span><span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test<span class="token punctuation">.</span>d2 |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>输出序列：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+--------------------------------------------------+ |
| | Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)| |
| +-----------------------------+--------------------------------------------------+ |
| |2020-01-01T00:00:02.000+08:00| 100.0| |
| |2020-01-01T00:00:04.000+08:00| 120.0| |
| |2020-01-01T00:00:06.000+08:00| 80.0| |
| |2020-01-01T00:00:08.000+08:00| 100.5| |
| |2020-01-01T00:00:10.000+08:00| 119.5| |
| |2020-01-01T00:00:12.000+08:00| 87.0| |
| |2020-01-01T00:00:14.000+08:00| 99.5| |
| |2020-01-01T00:00:16.000+08:00| 119.0| |
| |2020-01-01T00:00:18.000+08:00| 80.5| |
| |2020-01-01T00:00:20.000+08:00| 99.0| |
| |2020-01-01T00:00:22.000+08:00| 121.0| |
| |2020-01-01T00:00:24.000+08:00| 79.5| |
| +-----------------------------+--------------------------------------------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><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><h5 id="使用改进的分解方法进行修复" tabindex="-1"><a class="header-anchor" href="#使用改进的分解方法进行修复" aria-hidden="true">#</a> 使用改进的分解方法进行修复</h5><p>当<code>method</code>取值为'Improved'时，本函数将使用改进的分解方法进行数值修复。</p><p>输入序列同上，用于查询的SQL语句如下：</p><div class="language-sql line-numbers-mode" data-ext="sql"><pre class="language-sql"><code><span class="token keyword">select</span> seasonalrepair<span class="token punctuation">(</span>s1<span class="token punctuation">,</span><span class="token string">'method'</span><span class="token operator">=</span><span class="token string">'improved'</span><span class="token punctuation">,</span><span class="token string">'period'</span><span class="token operator">=</span><span class="token number">3</span><span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test<span class="token punctuation">.</span>d2 |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>输出序列：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+-------------------------------------------------------------+ |
| | Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)| |
| +-----------------------------+-------------------------------------------------------------+ |
| |2020-01-01T00:00:02.000+08:00| 100.0| |
| |2020-01-01T00:00:04.000+08:00| 120.0| |
| |2020-01-01T00:00:06.000+08:00| 80.0| |
| |2020-01-01T00:00:08.000+08:00| 100.5| |
| |2020-01-01T00:00:10.000+08:00| 119.5| |
| |2020-01-01T00:00:12.000+08:00| 81.5| |
| |2020-01-01T00:00:14.000+08:00| 99.5| |
| |2020-01-01T00:00:16.000+08:00| 119.0| |
| |2020-01-01T00:00:18.000+08:00| 80.5| |
| |2020-01-01T00:00:20.000+08:00| 99.0| |
| |2020-01-01T00:00:22.000+08:00| 121.0| |
| |2020-01-01T00:00:24.000+08:00| 79.5| |
| +-----------------------------+-------------------------------------------------------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><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></div><!----><footer class="page-meta"><div class="meta-item edit-link"><a href="https://github.com/apache/iotdb-docs/edit/main/src/zh/UserGuide/V1.1.x/Operators-Functions/Data-Repairing.md" rel="noopener noreferrer" target="_blank" aria-label="发现错误？在 GitHub 上编辑此页" class="nav-link label"><!--[--><svg xmlns="http://www.w3.org/2000/svg" class="icon edit-icon" viewBox="0 0 1024 1024" fill="currentColor" aria-label="edit icon"><path d="M430.818 653.65a60.46 60.46 0 0 1-50.96-93.281l71.69-114.012 7.773-10.365L816.038 80.138A60.46 60.46 0 0 1 859.225 62a60.46 60.46 0 0 1 43.186 18.138l43.186 43.186a60.46 60.46 0 0 1 0 86.373L588.879 565.55l-8.637 8.637-117.466 68.234a60.46 60.46 0 0 1-31.958 11.229z"></path><path d="M728.802 962H252.891A190.883 190.883 0 0 1 62.008 771.98V296.934a190.883 190.883 0 0 1 190.883-192.61h267.754a60.46 60.46 0 0 1 0 120.92H252.891a69.962 69.962 0 0 0-69.098 69.099V771.98a69.962 69.962 0 0 0 69.098 69.098h475.911A69.962 69.962 0 0 0 797.9 771.98V503.363a60.46 60.46 0 1 1 120.922 0V771.98A190.883 190.883 0 0 1 728.802 962z"></path></svg><!--]-->发现错误？在 GitHub 上编辑此页<span><svg class="external-link-icon" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path><polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg><span class="external-link-icon-sr-only">open in new window</span></span><!----></a></div><div class="meta-item git-info"><div class="update-time"><span class="label">上次编辑于: </span><!----></div><div class="contributors"><span class="label">贡献者: </span><!--[--><!--[--><span class="contributor" title="email: 105700158+wanghui42@users.noreply.github.com">wanghui42</span><!--]--><!--]--></div></div></footer><nav class="vp-page-nav"><a class="vp-link nav-link prev" href="/zh/UserGuide/V1.1.x/Operators-Functions/Data-Quality.html"><div class="hint"><span class="arrow start"></span>上一页</div><div class="link"><!---->数据质量</div></a><a class="vp-link nav-link next" href="/zh/UserGuide/V1.1.x/Operators-Functions/Series-Discovery.html"><div class="hint">下一页<span class="arrow end"></span></div><div class="link">序列发现<!----></div></a></nav><!----><!----><!--]--></main><!--]--><footer style="padding-bottom:2rem;"><span id="doc-version" style="display:none;">rel/1.1</span><p style="text-align:center;color:#909399;font-size:12px;margin:0 30px;">Copyright © 2023 The Apache Software Foundation.<br> Apache and the Apache feather logo are trademarks of The Apache Software Foundation</p><p style="text-align:center;margin-top:10px;color:#909399;font-size:12px;margin:0 30px;"><strong>Have a question?</strong> Connect with us on QQ, WeChat, or Slack. <a href="https://github.com/apache/iotdb/issues/1995">Join the community</a> now.</p><p style="text-align:center;margin-top:10px;color:#909399;font-size:12px;margin:0 30px;"> We use <a href="https://analytics.google.com">Google Analytics</a> to collect anonymous, aggregated usage information. </p></footer></div><!--]--><!--]--><!----><!--]--></div> |
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