| import{_ as e,o as n,c as s,e as a}from"./app-Bx8hKGcu.js";const i={},l=a(`<h2 id="数据修复" tabindex="-1"><a class="header-anchor" href="#数据修复"><span>数据修复</span></a></h2><h3 id="timestamprepair" tabindex="-1"><a class="header-anchor" href="#timestamprepair"><span>TimestampRepair</span></a></h3><h4 id="函数简介" tabindex="-1"><a class="header-anchor" href="#函数简介"><span>函数简介</span></a></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"><span>使用示例</span></a></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"><span>指定标准时间间隔</span></a></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" data-title="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" data-title="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" data-title="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"><span>自动推算标准时间间隔</span></a></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" data-title="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" data-title="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"><span>ValueFill</span></a></h3><h4 id="函数简介-1" tabindex="-1"><a class="header-anchor" href="#函数简介-1"><span>函数简介</span></a></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"><span>使用示例</span></a></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"><span>使用 linear 方法进行填补</span></a></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" data-title="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" data-title="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" data-title="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"><span>使用 previous 方法进行填补</span></a></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" data-title="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" data-title="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"><span>ValueRepair</span></a></h3><h4 id="函数简介-2" tabindex="-1"><a class="header-anchor" href="#函数简介-2"><span>函数简介</span></a></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"><span>使用示例</span></a></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"><span>使用 Screen 方法进行修复</span></a></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" data-title="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" data-title="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" data-title="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"><span>使用 LsGreedy 方法进行修复</span></a></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" data-title="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" data-title="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"><span>MasterRepair</span></a></h3><h4 id="函数简介-3" tabindex="-1"><a class="header-anchor" href="#函数简介-3"><span>函数简介</span></a></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"><span>使用示例</span></a></h4><p>输入序列：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="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" data-title="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" data-title="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"><span>SeasonalRepair</span></a></h3><h4 id="函数简介-4" tabindex="-1"><a class="header-anchor" href="#函数简介-4"><span>函数简介</span></a></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"><span>使用示例</span></a></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"><span>使用经典分解方法进行修复</span></a></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" data-title="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" data-title="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" data-title="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"><span>使用改进的分解方法进行修复</span></a></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" data-title="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" data-title="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>`,110),d=[l];function t(r,c){return n(),s("div",null,d)}const o=e(i,[["render",t],["__file","Data-Repairing.html.vue"]]),u=JSON.parse('{"path":"/zh/UserGuide/V1.1.x/Operators-Functions/Data-Repairing.html","title":"","lang":"zh-CN","frontmatter":{"description":"数据修复 TimestampRepair 函数简介 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。 函...","head":[["link",{"rel":"alternate","hreflang":"en-us","href":"https://iotdb.apache.org/UserGuide/V1.1.x/Operators-Functions/Data-Repairing.html"}],["meta",{"property":"og:url","content":"https://iotdb.apache.org/zh/UserGuide/V1.1.x/Operators-Functions/Data-Repairing.html"}],["meta",{"property":"og:site_name","content":"IoTDB Website"}],["meta",{"property":"og:description","content":"数据修复 TimestampRepair 函数简介 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。 函..."}],["meta",{"property":"og:type","content":"article"}],["meta",{"property":"og:locale","content":"zh-CN"}],["meta",{"property":"og:locale:alternate","content":"en-US"}],["meta",{"property":"og:updated_time","content":"2023-07-10T03:11:17.000Z"}],["meta",{"property":"article:modified_time","content":"2023-07-10T03:11:17.000Z"}],["script",{"type":"application/ld+json"},"{\\"@context\\":\\"https://schema.org\\",\\"@type\\":\\"Article\\",\\"headline\\":\\"\\",\\"image\\":[\\"\\"],\\"dateModified\\":\\"2023-07-10T03:11:17.000Z\\",\\"author\\":[]}"]]},"headers":[{"level":2,"title":"数据修复","slug":"数据修复","link":"#数据修复","children":[{"level":3,"title":"TimestampRepair","slug":"timestamprepair","link":"#timestamprepair","children":[]},{"level":3,"title":"ValueFill","slug":"valuefill","link":"#valuefill","children":[]},{"level":3,"title":"ValueRepair","slug":"valuerepair","link":"#valuerepair","children":[]},{"level":3,"title":"MasterRepair","slug":"masterrepair","link":"#masterrepair","children":[]},{"level":3,"title":"SeasonalRepair","slug":"seasonalrepair","link":"#seasonalrepair","children":[]}]}],"git":{"createdTime":1688958677000,"updatedTime":1688958677000,"contributors":[{"name":"CritasWang","email":"critas@outlook.com","commits":1}]},"readingTime":{"minutes":11.71,"words":3513},"filePathRelative":"zh/UserGuide/V1.1.x/Operators-Functions/Data-Repairing.md","localizedDate":"2023年7月10日","autoDesc":true}');export{o as comp,u as data}; |
