| import{_ as e,O as n,P as i,ah as s,aW as a}from"./framework-62ad666a.js";const t={},r=a(`<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">"p"</span><span class="token operator">=</span><span class="token string">"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>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,"p"="2")| |
| +-----------------------------+---------------------------+ |
| |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>`,19);function o(d,l){return n(),i("div",null,[s(` |
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