| import{_ as e,o as n,c as s,e as a}from"./app-Bp5kEZWW.js";const t={},i=a(`<h1 id="machine-learning" tabindex="-1"><a class="header-anchor" href="#machine-learning"><span>Machine Learning</span></a></h1><h2 id="ar" tabindex="-1"><a class="header-anchor" href="#ar"><span>AR</span></a></h2><h3 id="usage" tabindex="-1"><a class="header-anchor" href="#usage"><span>Usage</span></a></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"><span>Examples</span></a></h3><h4 id="assigning-model-order" tabindex="-1"><a class="header-anchor" href="#assigning-model-order"><span>Assigning Model Order</span></a></h4><p>Input Series:</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+---------------+ |
| +-----------------------------+---------------+ |
| |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" data-title="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" data-title="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),r=[i];function o(l,d){return n(),s("div",null,r)}const p=e(t,[["render",o],["__file","Machine-Learning.html.vue"]]),u=JSON.parse('{"path":"/UserGuide/V1.1.x/Operators-Functions/Machine-Learning.html","title":"Machine Learning","lang":"en-US","frontmatter":{"description":"Machine Learning AR Usage This function is used to learn the coefficients of the autoregressive models for a time series. Name: AR Input Series: Only support a single input nume...","head":[["link",{"rel":"alternate","hreflang":"zh-cn","href":"https://iotdb.apache.org/zh/UserGuide/V1.1.x/Operators-Functions/Machine-Learning.html"}],["meta",{"property":"og:url","content":"https://iotdb.apache.org/UserGuide/V1.1.x/Operators-Functions/Machine-Learning.html"}],["meta",{"property":"og:site_name","content":"IoTDB Website"}],["meta",{"property":"og:title","content":"Machine Learning"}],["meta",{"property":"og:description","content":"Machine Learning AR Usage This function is used to learn the coefficients of the autoregressive models for a time series. Name: AR Input Series: Only support a single input nume..."}],["meta",{"property":"og:type","content":"article"}],["meta",{"property":"og:locale","content":"en-US"}],["meta",{"property":"og:locale:alternate","content":"zh-CN"}],["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\\":\\"Machine Learning\\",\\"image\\":[\\"\\"],\\"dateModified\\":\\"2023-07-10T03:11:17.000Z\\",\\"author\\":[]}"]]},"headers":[{"level":2,"title":"AR","slug":"ar","link":"#ar","children":[{"level":3,"title":"Usage","slug":"usage","link":"#usage","children":[]},{"level":3,"title":"Examples","slug":"examples","link":"#examples","children":[]}]}],"git":{"createdTime":1688958677000,"updatedTime":1688958677000,"contributors":[{"name":"CritasWang","email":"critas@outlook.com","commits":1}]},"readingTime":{"minutes":1.12,"words":336},"filePathRelative":"UserGuide/V1.1.x/Operators-Functions/Machine-Learning.md","localizedDate":"July 10, 2023","autoDesc":true}');export{p as comp,u as data}; |
