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class="vp-sidebar-sub-headers"></ul><!--]--></li><li><!--[--><a class="vp-link nav-link vp-sidebar-link vp-sidebar-page" href="/UserGuide/V1.1.x/Operators-Functions/Variation-Trend.html"><!---->Variation Trend<!----></a><ul class="vp-sidebar-sub-headers"></ul><!--]--></li><li><!--[--><a class="vp-link nav-link vp-sidebar-link vp-sidebar-page" href="/UserGuide/V1.1.x/Operators-Functions/Sample.html"><!---->Sample<!----></a><ul class="vp-sidebar-sub-headers"></ul><!--]--></li><li><!--[--><a class="vp-link nav-link vp-sidebar-link vp-sidebar-page" href="/UserGuide/V1.1.x/Operators-Functions/Time-Series.html"><!---->Time-Series<!----></a><ul class="vp-sidebar-sub-headers"></ul><!--]--></li><li><!--[--><a class="vp-link nav-link vp-sidebar-link vp-sidebar-page" href="/UserGuide/V1.1.x/Operators-Functions/Lambda.html"><!---->Lambda<!----></a><ul class="vp-sidebar-sub-headers"></ul><!--]--></li><li><!--[--><a class="vp-link nav-link vp-sidebar-link vp-sidebar-page" href="/UserGuide/V1.1.x/Operators-Functions/Data-Profiling.html"><!---->Data Profiling<!----></a><ul class="vp-sidebar-sub-headers"></ul><!--]--></li><li><!--[--><a class="vp-link nav-link vp-sidebar-link vp-sidebar-page" href="/UserGuide/V1.1.x/Operators-Functions/Anomaly-Detection.html"><!---->Anomaly Detection<!----></a><ul class="vp-sidebar-sub-headers"></ul><!--]--></li><li><!--[--><a class="vp-link nav-link vp-sidebar-link vp-sidebar-page" href="/UserGuide/V1.1.x/Operators-Functions/Data-Matching.html"><!---->Data Matching<!----></a><ul class="vp-sidebar-sub-headers"></ul><!--]--></li><li><!--[--><a class="vp-link nav-link vp-sidebar-link vp-sidebar-page" href="/UserGuide/V1.1.x/Operators-Functions/Frequency-Domain.html"><!---->Frequency Domain<!----></a><ul class="vp-sidebar-sub-headers"></ul><!--]--></li><li><!--[--><a class="vp-link nav-link vp-sidebar-link vp-sidebar-page" href="/UserGuide/V1.1.x/Operators-Functions/Data-Quality.html"><!---->Data Quality<!----></a><ul class="vp-sidebar-sub-headers"></ul><!--]--></li><li><!--[--><a class="vp-link nav-link vp-sidebar-link vp-sidebar-page" href="/UserGuide/V1.1.x/Operators-Functions/Data-Repairing.html"><!---->Data Repairing<!----></a><ul class="vp-sidebar-sub-headers"></ul><!--]--></li><li><!--[--><a class="vp-link nav-link vp-sidebar-link vp-sidebar-page" href="/UserGuide/V1.1.x/Operators-Functions/Series-Discovery.html"><!---->Series Discovery<!----></a><ul class="vp-sidebar-sub-headers"></ul><!--]--></li><li><!--[--><a class="vp-link nav-link active vp-sidebar-link vp-sidebar-page active" href="/UserGuide/V1.1.x/Operators-Functions/Machine-Learning.html"><!---->Machine Learning<!----></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="/UserGuide/V1.1.x/Operators-Functions/Machine-Learning.html#ar"><!---->AR<!----></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="/UserGuide/V1.1.x/Operators-Functions/Machine-Learning.html#usage"><!---->Usage<!----></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="/UserGuide/V1.1.x/Operators-Functions/Machine-Learning.html#examples"><!---->Examples<!----></a><ul class="vp-sidebar-sub-headers"></ul></li></ul></li></ul><!--]--></li></ul></section></li><li><section class="vp-sidebar-group"><button class="vp-sidebar-heading clickable" type="button"><!----><span class="vp-sidebar-title">Trigger</span><span class="vp-arrow end"></span></button><!----></section></li><li><section class="vp-sidebar-group"><button class="vp-sidebar-heading clickable" type="button"><!----><span class="vp-sidebar-title">Monitor and Alert</span><span class="vp-arrow end"></span></button><!----></section></li><li><section class="vp-sidebar-group"><button class="vp-sidebar-heading clickable" type="button"><!----><span 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id="toc"><!--[--><!----><!--]--><div class="toc-header">On This Page<button type="button" class="print-button" title="Print"><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="/#ar">AR</a></li><li><ul class="toc-list"><!--[--><li class="toc-item"><a class="vp-link toc-link level3" href="/#usage">Usage</a></li><!----><!--]--><!--[--><li class="toc-item"><a class="vp-link toc-link level3" href="/#examples">Examples</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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--><h1 id="machine-learning" tabindex="-1"><a class="header-anchor" href="#machine-learning" aria-hidden="true">#</a> Machine Learning</h1><h2 id="ar" tabindex="-1"><a class="header-anchor" href="#ar" aria-hidden="true">#</a> AR</h2><h3 id="usage" tabindex="-1"><a class="header-anchor" href="#usage" aria-hidden="true">#</a> Usage</h3><p>This function is used to learn the coefficients of the autoregressive models for a time series.</p><p><strong>Name:</strong> AR</p><p><strong>Input Series:</strong> Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.</p><p><strong>Parameters:</strong></p><ul><li><code>p</code>: The order of the autoregressive model. Its default value is 1.</li></ul><p><strong>Output Series:</strong> Output a single series. The type is DOUBLE. The first line corresponds to the first order coefficient, and so on.</p><p><strong>Note:</strong></p><ul><li>Parameter <code>p</code> should be a positive integer.</li><li>Most points in the series should be sampled at a constant time interval.</li><li>Linear interpolation is applied for the missing points in the series.</li></ul><h3 id="examples" tabindex="-1"><a class="header-anchor" href="#examples" aria-hidden="true">#</a> Examples</h3><h4 id="assigning-model-order" tabindex="-1"><a class="header-anchor" href="#assigning-model-order" aria-hidden="true">#</a> Assigning Model Order</h4><p>Input Series:</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+---------------+
|                         Time|root.test.d0.s0|
+-----------------------------+---------------+
|2020-01-01T00:00:01.000+08:00|           -4.0|
|2020-01-01T00:00:02.000+08:00|           -3.0|
|2020-01-01T00:00:03.000+08:00|           -2.0|
|2020-01-01T00:00:04.000+08:00|           -1.0|
|2020-01-01T00:00:05.000+08:00|            0.0|
|2020-01-01T00:00:06.000+08:00|            1.0|
|2020-01-01T00:00:07.000+08:00|            2.0|
|2020-01-01T00:00:08.000+08:00|            3.0|
|2020-01-01T00:00:09.000+08:00|            4.0|
+-----------------------------+---------------+
</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></div><p>SQL for query:</p><div class="language-sql line-numbers-mode" data-ext="sql"><pre class="language-sql"><code><span class="token keyword">select</span> ar<span class="token punctuation">(</span>s0<span class="token punctuation">,</span><span class="token string">&quot;p&quot;</span><span class="token operator">=</span><span class="token string">&quot;2&quot;</span><span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test<span class="token punctuation">.</span>d0
</code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>Output Series:</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>+-----------------------------+---------------------------+
|                         Time|ar(root.test.d0.s0,&quot;p&quot;=&quot;2&quot;)|
+-----------------------------+---------------------------+
|1970-01-01T08:00:00.001+08:00|                     0.9429|
|1970-01-01T08:00:00.002+08:00|                    -0.2571|
+-----------------------------+---------------------------+
</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></div></div><!----><footer class="page-meta"><div class="meta-item edit-link"><a href="https://github.com/apache/iotdb-docs/edit/main/src/UserGuide/V1.1.x/Operators-Functions/Machine-Learning.md" rel="noopener noreferrer" target="_blank" aria-label="Found Error? Edit this page on GitHub" 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><!--]-->Found Error? Edit this page on GitHub<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">Last update: </span><!----></div><div class="contributors"><span class="label">Contributors: </span><!--[--><!--[--><span class="contributor" title="email: critas@outlook.com">CritasWang</span><!--]--><!--]--></div></div></footer><nav class="vp-page-nav"><a class="vp-link nav-link prev" href="/UserGuide/V1.1.x/Operators-Functions/Series-Discovery.html"><div class="hint"><span class="arrow start"></span>Prev</div><div class="link"><!---->Series Discovery</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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