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class="page-info"><!----><!----><span class="page-date-info" aria-label="Writing Date"><svg xmlns="http://www.w3.org/2000/svg" class="icon calendar-icon" viewBox="0 0 1024 1024" fill="currentColor" aria-label="calendar icon"><path d="M716.4 110.137c0-18.753-14.72-33.473-33.472-33.473-18.753 0-33.473 14.72-33.473 33.473v33.473h66.993v-33.473zm-334.87 0c0-18.753-14.72-33.473-33.473-33.473s-33.52 14.72-33.52 33.473v33.473h66.993v-33.473zm468.81 33.52H716.4v100.465c0 18.753-14.72 33.473-33.472 33.473a33.145 33.145 0 01-33.473-33.473V143.657H381.53v100.465c0 18.753-14.72 33.473-33.473 33.473a33.145 33.145 0 01-33.473-33.473V143.657H180.6A134.314 134.314 0 0046.66 277.595v535.756A134.314 134.314 0 00180.6 947.289h669.74a134.36 134.36 0 00133.94-133.938V277.595a134.314 134.314 0 00-133.94-133.938zm33.473 267.877H147.126a33.145 33.145 0 01-33.473-33.473c0-18.752 14.72-33.473 33.473-33.473h736.687c18.752 0 33.472 14.72 33.472 33.473a33.145 33.145 0 01-33.472 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123.24-37.67 216.446-198.453 220.588-392.892zM531.16 450.445v352.632c117.674 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>About 10 min</span><meta property="timeRequired" content="PT10M"></span><!----><!----></div><hr></div><div class="vp-toc-placeholder"><aside id="toc"><!--[--><!----><!--]--><div class="vp-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 class="arrow end"></div></div><div class="vp-toc-wrapper"><ul class="vp-toc-list"><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level2" href="#iqr">IQR</a></li><li><ul class="vp-toc-list"><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level3" href="#usage">Usage</a></li><!----><!--]--><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level3" href="#examples">Examples</a></li><!----><!--]--></ul></li><!--]--><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level2" href="#ksigma">KSigma</a></li><li><ul class="vp-toc-list"><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level3" href="#usage-1">Usage</a></li><!----><!--]--><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level3" href="#examples-1">Examples</a></li><!----><!--]--></ul></li><!--]--><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level2" href="#lof">LOF</a></li><li><ul class="vp-toc-list"><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level3" href="#usage-2">Usage</a></li><!----><!--]--><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level3" href="#examples-2">Examples</a></li><!----><!--]--></ul></li><!--]--><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level2" href="#missdetect">MissDetect</a></li><li><ul class="vp-toc-list"><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level3" href="#usage-3">Usage</a></li><!----><!--]--><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level3" href="#examples-3">Examples</a></li><!----><!--]--></ul></li><!--]--><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level2" href="#range">Range</a></li><li><ul class="vp-toc-list"><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level3" href="#usage-4">Usage</a></li><!----><!--]--><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level3" href="#examples-4">Examples</a></li><!----><!--]--></ul></li><!--]--><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level2" href="#twosidedfilter">TwoSidedFilter</a></li><li><ul class="vp-toc-list"><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level3" href="#usage-5">Usage</a></li><!----><!--]--><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level3" href="#examples-5">Examples</a></li><!----><!--]--></ul></li><!--]--><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level2" href="#outlier">Outlier</a></li><li><ul class="vp-toc-list"><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level3" href="#usage-6">Usage</a></li><!----><!--]--><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level3" href="#examples-6">Examples</a></li><!----><!--]--></ul></li><!--]--></ul><div class="vp-toc-marker" style="top:-1.7rem;"></div></div><!--[--><!----><!--]--></aside></div><!--[--><!----><!--]--><div class="theme-hope-content"><h1 id="anomaly-detection" tabindex="-1"><a class="header-anchor" href="#anomaly-detection"><span>Anomaly Detection</span></a></h1><h2 id="iqr" tabindex="-1"><a class="header-anchor" href="#iqr"><span>IQR</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 detect anomalies based on IQR. Points distributing beyond 1.5 times IQR are selected.</p><p><strong>Name:</strong> IQR</p><p><strong>Input Series:</strong> Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.</p><ul><li><code>method</code>: When set to "batch", anomaly test is conducted after importing all data points; when set to "stream", it is required to provide upper and lower quantiles. The default method is "batch".</li><li><code>q1</code>: The lower quantile when method is set to "stream".</li><li><code>q3</code>: The upper quantile when method is set to "stream".</li></ul><p><strong>Output Series:</strong> Output a single series. The type is DOUBLE.</p><p><strong>Note:</strong> $IQR=Q_3-Q_1$</p><h3 id="examples" tabindex="-1"><a class="header-anchor" href="#examples"><span>Examples</span></a></h3><h4 id="batch-computing" tabindex="-1"><a class="header-anchor" href="#batch-computing"><span>Batch computing</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>+-----------------------------+------------+ |
| | Time|root.test.s1| |
| +-----------------------------+------------+ |
| |1970-01-01T08:00:00.100+08:00| 0.0| |
| |1970-01-01T08:00:00.200+08:00| 0.0| |
| |1970-01-01T08:00:00.300+08:00| 1.0| |
| |1970-01-01T08:00:00.400+08:00| -1.0| |
| |1970-01-01T08:00:00.500+08:00| 0.0| |
| |1970-01-01T08:00:00.600+08:00| 0.0| |
| |1970-01-01T08:00:00.700+08:00| -2.0| |
| |1970-01-01T08:00:00.800+08:00| 2.0| |
| |1970-01-01T08:00:00.900+08:00| 0.0| |
| |1970-01-01T08:00:01.000+08:00| 0.0| |
| |1970-01-01T08:00:01.100+08:00| 1.0| |
| |1970-01-01T08:00:01.200+08:00| -1.0| |
| |1970-01-01T08:00:01.300+08:00| -1.0| |
| |1970-01-01T08:00:01.400+08:00| 1.0| |
| |1970-01-01T08:00:01.500+08:00| 0.0| |
| |1970-01-01T08:00:01.600+08:00| 0.0| |
| |1970-01-01T08:00:01.700+08:00| 10.0| |
| |1970-01-01T08:00:01.800+08:00| 2.0| |
| |1970-01-01T08:00:01.900+08:00| -2.0| |
| |1970-01-01T08:00:02.000+08:00| 0.0| |
| +-----------------------------+------------+ |
| </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 class="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> iqr<span class="token punctuation">(</span>s1<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>Output series:</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+-----------------+ |
| | Time|iqr(root.test.s1)| |
| +-----------------------------+-----------------+ |
| |1970-01-01T08:00:01.700+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></div><h2 id="ksigma" tabindex="-1"><a class="header-anchor" href="#ksigma"><span>KSigma</span></a></h2><h3 id="usage-1" tabindex="-1"><a class="header-anchor" href="#usage-1"><span>Usage</span></a></h3><p>This function is used to detect anomalies based on the Dynamic K-Sigma Algorithm.<br> Within a sliding window, the input value with a deviation of more than k times the standard deviation from the average will be output as anomaly.</p><p><strong>Name:</strong> KSIGMA</p><p><strong>Input Series:</strong> Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.</p><ul><li><code>k</code>: How many times to multiply on standard deviation to define anomaly, the default value is 3.</li><li><code>window</code>: The window size of Dynamic K-Sigma Algorithm, the default value is 10000.</li></ul><p><strong>Output Series:</strong> Output a single series. The type is same as input series.</p><p><strong>Note:</strong> Only when is larger than 0, the anomaly detection will be performed. Otherwise, nothing will be output.</p><h3 id="examples-1" tabindex="-1"><a class="header-anchor" href="#examples-1"><span>Examples</span></a></h3><h4 id="assigning-k" tabindex="-1"><a class="header-anchor" href="#assigning-k"><span>Assigning k</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>+-----------------------------+---------------+ |
| | Time|root.test.d1.s1| |
| +-----------------------------+---------------+ |
| |2020-01-01T00:00:02.000+08:00| 0.0| |
| |2020-01-01T00:00:03.000+08:00| 50.0| |
| |2020-01-01T00:00:04.000+08:00| 100.0| |
| |2020-01-01T00:00:06.000+08:00| 150.0| |
| |2020-01-01T00:00:08.000+08:00| 200.0| |
| |2020-01-01T00:00:10.000+08:00| 200.0| |
| |2020-01-01T00:00:14.000+08:00| 200.0| |
| |2020-01-01T00:00:15.000+08:00| 200.0| |
| |2020-01-01T00:00:16.000+08:00| 200.0| |
| |2020-01-01T00:00:18.000+08:00| 200.0| |
| |2020-01-01T00:00:20.000+08:00| 150.0| |
| |2020-01-01T00:00:22.000+08:00| 100.0| |
| |2020-01-01T00:00:26.000+08:00| 50.0| |
| |2020-01-01T00:00:28.000+08:00| 0.0| |
| |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>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> ksigma<span class="token punctuation">(</span>s1<span class="token punctuation">,</span><span class="token string">"k"</span><span class="token operator">=</span><span class="token string">"1.0"</span><span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test<span class="token punctuation">.</span>d1 <span class="token keyword">where</span> <span class="token keyword">time</span> <span class="token operator"><=</span> <span class="token number">2020</span><span class="token operator">-</span><span class="token number">01</span><span class="token operator">-</span><span class="token number">01</span> <span class="token number">00</span>:<span class="token number">00</span>:<span class="token number">30</span> |
| </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 |ksigma(root.test.d1.s1,"k"="3.0")| |
| +-----------------------------+---------------------------------+ |
| |2020-01-01T00:00:02.000+08:00| 0.0| |
| |2020-01-01T00:00:03.000+08:00| 50.0| |
| |2020-01-01T00:00:26.000+08:00| 50.0| |
| |2020-01-01T00:00:28.000+08:00| 0.0| |
| +-----------------------------+---------------------------------+ |
| </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></div><h2 id="lof" tabindex="-1"><a class="header-anchor" href="#lof"><span>LOF</span></a></h2><h3 id="usage-2" tabindex="-1"><a class="header-anchor" href="#usage-2"><span>Usage</span></a></h3><p>This function is used to detect density anomaly of time series. According to k-th distance calculation parameter and local outlier factor (lof) threshold, the function judges if a set of input values is an density anomaly, and a bool mark of anomaly values will be output.</p><p><strong>Name:</strong> LOF</p><p><strong>Input Series:</strong> Multiple input series. The type is INT32 / INT64 / FLOAT / DOUBLE.</p><ul><li><code>method</code>:assign a detection method. The default value is "default", when input data has multiple dimensions. The alternative is "series", when a input series will be transformed to high dimension.</li><li><code>k</code>:use the k-th distance to calculate lof. Default value is 3.</li><li><code>window</code>: size of window to split origin data points. Default value is 10000.</li><li><code>windowsize</code>:dimension that will be transformed into when method is "series". The default value is 5.</li></ul><p><strong>Output Series:</strong> Output a single series. The type is DOUBLE.</p><p><strong>Note:</strong> Incomplete rows will be ignored. They are neither calculated nor marked as anomaly.</p><h3 id="examples-2" tabindex="-1"><a class="header-anchor" href="#examples-2"><span>Examples</span></a></h3><h4 id="using-default-parameters" tabindex="-1"><a class="header-anchor" href="#using-default-parameters"><span>Using default parameters</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>+-----------------------------+---------------+---------------+ |
| | Time|root.test.d1.s1|root.test.d1.s2| |
| +-----------------------------+---------------+---------------+ |
| |1970-01-01T08:00:00.100+08:00| 0.0| 0.0| |
| |1970-01-01T08:00:00.200+08:00| 0.0| 1.0| |
| |1970-01-01T08:00:00.300+08:00| 1.0| 1.0| |
| |1970-01-01T08:00:00.400+08:00| 1.0| 0.0| |
| |1970-01-01T08:00:00.500+08:00| 0.0| -1.0| |
| |1970-01-01T08:00:00.600+08:00| -1.0| -1.0| |
| |1970-01-01T08:00:00.700+08:00| -1.0| 0.0| |
| |1970-01-01T08:00:00.800+08:00| 2.0| 2.0| |
| |1970-01-01T08:00:00.900+08:00| 0.0| null| |
| +-----------------------------+---------------+---------------+ |
| </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> lof<span class="token punctuation">(</span>s1<span class="token punctuation">,</span>s2<span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test<span class="token punctuation">.</span>d1 <span class="token keyword">where</span> <span class="token keyword">time</span><span class="token operator"><</span><span class="token number">1000</span> |
| </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|lof(root.test.d1.s1, root.test.d1.s2)| |
| +-----------------------------+-------------------------------------+ |
| |1970-01-01T08:00:00.100+08:00| 3.8274824267668244| |
| |1970-01-01T08:00:00.200+08:00| 3.0117631741126156| |
| |1970-01-01T08:00:00.300+08:00| 2.838155437762879| |
| |1970-01-01T08:00:00.400+08:00| 3.0117631741126156| |
| |1970-01-01T08:00:00.500+08:00| 2.73518261244453| |
| |1970-01-01T08:00:00.600+08:00| 2.371440975708148| |
| |1970-01-01T08:00:00.700+08:00| 2.73518261244453| |
| |1970-01-01T08:00:00.800+08:00| 1.7561416374270742| |
| +-----------------------------+-------------------------------------+ |
| </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><h4 id="diagnosing-1d-timeseries" tabindex="-1"><a class="header-anchor" href="#diagnosing-1d-timeseries"><span>Diagnosing 1d timeseries</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>+-----------------------------+---------------+ |
| | Time|root.test.d1.s1| |
| +-----------------------------+---------------+ |
| |1970-01-01T08:00:00.100+08:00| 1.0| |
| |1970-01-01T08:00:00.200+08:00| 2.0| |
| |1970-01-01T08:00:00.300+08:00| 3.0| |
| |1970-01-01T08:00:00.400+08:00| 4.0| |
| |1970-01-01T08:00:00.500+08:00| 5.0| |
| |1970-01-01T08:00:00.600+08:00| 6.0| |
| |1970-01-01T08:00:00.700+08:00| 7.0| |
| |1970-01-01T08:00:00.800+08:00| 8.0| |
| |1970-01-01T08:00:00.900+08:00| 9.0| |
| |1970-01-01T08:00:01.000+08:00| 10.0| |
| |1970-01-01T08:00:01.100+08:00| 11.0| |
| |1970-01-01T08:00:01.200+08:00| 12.0| |
| |1970-01-01T08:00:01.300+08:00| 13.0| |
| |1970-01-01T08:00:01.400+08:00| 14.0| |
| |1970-01-01T08:00:01.500+08:00| 15.0| |
| |1970-01-01T08:00:01.600+08:00| 16.0| |
| |1970-01-01T08:00:01.700+08:00| 17.0| |
| |1970-01-01T08:00:01.800+08:00| 18.0| |
| |1970-01-01T08:00:01.900+08:00| 19.0| |
| |1970-01-01T08:00:02.000+08:00| 20.0| |
| +-----------------------------+---------------+ |
| </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 class="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> lof<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">"series"</span><span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test<span class="token punctuation">.</span>d1 <span class="token keyword">where</span> <span class="token keyword">time</span><span class="token operator"><</span><span class="token number">1000</span> |
| </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|lof(root.test.d1.s1)| |
| +-----------------------------+--------------------+ |
| |1970-01-01T08:00:00.100+08:00| 3.77777777777778| |
| |1970-01-01T08:00:00.200+08:00| 4.32727272727273| |
| |1970-01-01T08:00:00.300+08:00| 4.85714285714286| |
| |1970-01-01T08:00:00.400+08:00| 5.40909090909091| |
| |1970-01-01T08:00:00.500+08:00| 5.94999999999999| |
| |1970-01-01T08:00:00.600+08:00| 6.43243243243243| |
| |1970-01-01T08:00:00.700+08:00| 6.79999999999999| |
| |1970-01-01T08:00:00.800+08:00| 7.0| |
| |1970-01-01T08:00:00.900+08:00| 7.0| |
| |1970-01-01T08:00:01.000+08:00| 6.79999999999999| |
| |1970-01-01T08:00:01.100+08:00| 6.43243243243243| |
| |1970-01-01T08:00:01.200+08:00| 5.94999999999999| |
| |1970-01-01T08:00:01.300+08:00| 5.40909090909091| |
| |1970-01-01T08:00:01.400+08:00| 4.85714285714286| |
| |1970-01-01T08:00:01.500+08:00| 4.32727272727273| |
| |1970-01-01T08:00:01.600+08:00| 3.77777777777778| |
| +-----------------------------+--------------------+ |
| </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 class="line-number"></div></div></div><h2 id="missdetect" tabindex="-1"><a class="header-anchor" href="#missdetect"><span>MissDetect</span></a></h2><h3 id="usage-3" tabindex="-1"><a class="header-anchor" href="#usage-3"><span>Usage</span></a></h3><p>This function is used to detect missing anomalies.<br> In some datasets, missing values are filled by linear interpolation.<br> Thus, there are several long perfect linear segments.<br> By discovering these perfect linear segments,<br> missing anomalies are detected.</p><p><strong>Name:</strong> MISSDETECT</p><p><strong>Input Series:</strong> Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.</p><p><strong>Parameter:</strong></p><p><code>error</code>: The minimum length of the detected missing anomalies, which is an integer greater than or equal to 10. By default, it is 10.</p><p><strong>Output Series:</strong> Output a single series. The type is BOOLEAN. Each data point which is miss anomaly will be labeled as true.</p><h3 id="examples-3" tabindex="-1"><a class="header-anchor" href="#examples-3"><span>Examples</span></a></h3><p>Input series:</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+---------------+ |
| | Time|root.test.d2.s2| |
| +-----------------------------+---------------+ |
| |2021-07-01T12:00:00.000+08:00| 0.0| |
| |2021-07-01T12:00:01.000+08:00| 1.0| |
| |2021-07-01T12:00:02.000+08:00| 0.0| |
| |2021-07-01T12:00:03.000+08:00| 1.0| |
| |2021-07-01T12:00:04.000+08:00| 0.0| |
| |2021-07-01T12:00:05.000+08:00| 0.0| |
| |2021-07-01T12:00:06.000+08:00| 0.0| |
| |2021-07-01T12:00:07.000+08:00| 0.0| |
| |2021-07-01T12:00:08.000+08:00| 0.0| |
| |2021-07-01T12:00:09.000+08:00| 0.0| |
| |2021-07-01T12:00:10.000+08:00| 0.0| |
| |2021-07-01T12:00:11.000+08:00| 0.0| |
| |2021-07-01T12:00:12.000+08:00| 0.0| |
| |2021-07-01T12:00:13.000+08:00| 0.0| |
| |2021-07-01T12:00:14.000+08:00| 0.0| |
| |2021-07-01T12:00:15.000+08:00| 0.0| |
| |2021-07-01T12:00:16.000+08:00| 1.0| |
| |2021-07-01T12:00:17.000+08:00| 0.0| |
| |2021-07-01T12:00:18.000+08:00| 1.0| |
| |2021-07-01T12:00:19.000+08:00| 0.0| |
| |2021-07-01T12:00:20.000+08:00| 1.0| |
| +-----------------------------+---------------+ |
| </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 class="line-number"></div><div class="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> missdetect<span class="token punctuation">(</span>s2<span class="token punctuation">,</span><span class="token string">'minlen'</span><span class="token operator">=</span><span class="token string">'10'</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>Output series:</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+------------------------------------------+ |
| | Time|missdetect(root.test.d2.s2, "minlen"="10")| |
| +-----------------------------+------------------------------------------+ |
| |2021-07-01T12:00:00.000+08:00| false| |
| |2021-07-01T12:00:01.000+08:00| false| |
| |2021-07-01T12:00:02.000+08:00| false| |
| |2021-07-01T12:00:03.000+08:00| false| |
| |2021-07-01T12:00:04.000+08:00| true| |
| |2021-07-01T12:00:05.000+08:00| true| |
| |2021-07-01T12:00:06.000+08:00| true| |
| |2021-07-01T12:00:07.000+08:00| true| |
| |2021-07-01T12:00:08.000+08:00| true| |
| |2021-07-01T12:00:09.000+08:00| true| |
| |2021-07-01T12:00:10.000+08:00| true| |
| |2021-07-01T12:00:11.000+08:00| true| |
| |2021-07-01T12:00:12.000+08:00| true| |
| |2021-07-01T12:00:13.000+08:00| true| |
| |2021-07-01T12:00:14.000+08:00| true| |
| |2021-07-01T12:00:15.000+08:00| true| |
| |2021-07-01T12:00:16.000+08:00| false| |
| |2021-07-01T12:00:17.000+08:00| false| |
| |2021-07-01T12:00:18.000+08:00| false| |
| |2021-07-01T12:00:19.000+08:00| false| |
| |2021-07-01T12:00:20.000+08:00| false| |
| +-----------------------------+------------------------------------------+ |
| </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 class="line-number"></div><div class="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><h2 id="range" tabindex="-1"><a class="header-anchor" href="#range"><span>Range</span></a></h2><h3 id="usage-4" tabindex="-1"><a class="header-anchor" href="#usage-4"><span>Usage</span></a></h3><p>This function is used to detect range anomaly of time series. According to upper bound and lower bound parameters, the function judges if a input value is beyond range, aka range anomaly, and a new time series of anomaly will be output.</p><p><strong>Name:</strong> RANGE</p><p><strong>Input Series:</strong> Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.</p><ul><li><code>lower_bound</code>:lower bound of range anomaly detection.</li><li><code>upper_bound</code>:upper bound of range anomaly detection.</li></ul><p><strong>Output Series:</strong> Output a single series. The type is the same as the input.</p><p><strong>Note:</strong> Only when <code>upper_bound</code> is larger than <code>lower_bound</code>, the anomaly detection will be performed. Otherwise, nothing will be output.</p><h3 id="examples-4" tabindex="-1"><a class="header-anchor" href="#examples-4"><span>Examples</span></a></h3><h4 id="assigning-lower-and-upper-bound" tabindex="-1"><a class="header-anchor" href="#assigning-lower-and-upper-bound"><span>Assigning Lower and Upper Bound</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>+-----------------------------+---------------+ |
| | Time|root.test.d1.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| 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| 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>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> range<span class="token punctuation">(</span>s1<span class="token punctuation">,</span><span class="token string">"lower_bound"</span><span class="token operator">=</span><span class="token string">"101.0"</span><span class="token punctuation">,</span><span class="token string">"upper_bound"</span><span class="token operator">=</span><span class="token string">"125.0"</span><span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test<span class="token punctuation">.</span>d1 <span class="token keyword">where</span> <span class="token keyword">time</span> <span class="token operator"><=</span> <span class="token number">2020</span><span class="token operator">-</span><span class="token number">01</span><span class="token operator">-</span><span class="token number">01</span> <span class="token number">00</span>:<span class="token number">00</span>:<span class="token number">30</span> |
| </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 |range(root.test.d1.s1,"lower_bound"="101.0","upper_bound"="125.0")| |
| +-----------------------------+------------------------------------------------------------------+ |
| |2020-01-01T00:00:02.000+08:00| 100.0| |
| |2020-01-01T00:00:28.000+08:00| 126.0| |
| +-----------------------------+------------------------------------------------------------------+ |
| </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></div><h2 id="twosidedfilter" tabindex="-1"><a class="header-anchor" href="#twosidedfilter"><span>TwoSidedFilter</span></a></h2><h3 id="usage-5" tabindex="-1"><a class="header-anchor" href="#usage-5"><span>Usage</span></a></h3><p>The function is used to filter anomalies of a numeric time series based on two-sided window detection.</p><p><strong>Name:</strong> TWOSIDEDFILTER</p><p><strong>Input Series:</strong> Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE</p><p><strong>Output Series:</strong> Output a single series. The type is the same as the input. It is the input without anomalies.</p><p><strong>Parameter:</strong></p><ul><li><p><code>len</code>: The size of the window, which is a positive integer. By default, it's 5. When <code>len</code>=3, the algorithm detects forward window and backward window with length 3 and calculates the outlierness of the current point.</p></li><li><p><code>threshold</code>: The threshold of outlierness, which is a floating number in (0,1). By default, it's 0.3. The strict standard of detecting anomalies is in proportion to the threshold.</p></li></ul><h3 id="examples-5" tabindex="-1"><a class="header-anchor" href="#examples-5"><span>Examples</span></a></h3><p>Input series:</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+------------+ |
| | Time|root.test.s0| |
| +-----------------------------+------------+ |
| |1970-01-01T08:00:00.000+08:00| 2002.0| |
| |1970-01-01T08:00:01.000+08:00| 1946.0| |
| |1970-01-01T08:00:02.000+08:00| 1958.0| |
| |1970-01-01T08:00:03.000+08:00| 2012.0| |
| |1970-01-01T08:00:04.000+08:00| 2051.0| |
| |1970-01-01T08:00:05.000+08:00| 1898.0| |
| |1970-01-01T08:00:06.000+08:00| 2014.0| |
| |1970-01-01T08:00:07.000+08:00| 2052.0| |
| |1970-01-01T08:00:08.000+08:00| 1935.0| |
| |1970-01-01T08:00:09.000+08:00| 1901.0| |
| |1970-01-01T08:00:10.000+08:00| 1972.0| |
| |1970-01-01T08:00:11.000+08:00| 1969.0| |
| |1970-01-01T08:00:12.000+08:00| 1984.0| |
| |1970-01-01T08:00:13.000+08:00| 2018.0| |
| |1970-01-01T08:00:37.000+08:00| 1484.0| |
| |1970-01-01T08:00:38.000+08:00| 1055.0| |
| |1970-01-01T08:00:39.000+08:00| 1050.0| |
| |1970-01-01T08:01:05.000+08:00| 1023.0| |
| |1970-01-01T08:01:06.000+08:00| 1056.0| |
| |1970-01-01T08:01:07.000+08:00| 978.0| |
| |1970-01-01T08:01:08.000+08:00| 1050.0| |
| |1970-01-01T08:01:09.000+08:00| 1123.0| |
| |1970-01-01T08:01:10.000+08:00| 1150.0| |
| |1970-01-01T08:01:11.000+08:00| 1034.0| |
| |1970-01-01T08:01:12.000+08:00| 950.0| |
| |1970-01-01T08:01:13.000+08:00| 1059.0| |
| +-----------------------------+------------+ |
| </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 class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="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> TwoSidedFilter<span class="token punctuation">(</span>s0<span class="token punctuation">,</span> <span class="token string">'len'</span><span class="token operator">=</span><span class="token string">'5'</span><span class="token punctuation">,</span> <span class="token string">'threshold'</span><span class="token operator">=</span><span class="token string">'0.3'</span><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>Output series:</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+------------+ |
| | Time|root.test.s0| |
| +-----------------------------+------------+ |
| |1970-01-01T08:00:00.000+08:00| 2002.0| |
| |1970-01-01T08:00:01.000+08:00| 1946.0| |
| |1970-01-01T08:00:02.000+08:00| 1958.0| |
| |1970-01-01T08:00:03.000+08:00| 2012.0| |
| |1970-01-01T08:00:04.000+08:00| 2051.0| |
| |1970-01-01T08:00:05.000+08:00| 1898.0| |
| |1970-01-01T08:00:06.000+08:00| 2014.0| |
| |1970-01-01T08:00:07.000+08:00| 2052.0| |
| |1970-01-01T08:00:08.000+08:00| 1935.0| |
| |1970-01-01T08:00:09.000+08:00| 1901.0| |
| |1970-01-01T08:00:10.000+08:00| 1972.0| |
| |1970-01-01T08:00:11.000+08:00| 1969.0| |
| |1970-01-01T08:00:12.000+08:00| 1984.0| |
| |1970-01-01T08:00:13.000+08:00| 2018.0| |
| |1970-01-01T08:01:05.000+08:00| 1023.0| |
| |1970-01-01T08:01:06.000+08:00| 1056.0| |
| |1970-01-01T08:01:07.000+08:00| 978.0| |
| |1970-01-01T08:01:08.000+08:00| 1050.0| |
| |1970-01-01T08:01:09.000+08:00| 1123.0| |
| |1970-01-01T08:01:10.000+08:00| 1150.0| |
| |1970-01-01T08:01:11.000+08:00| 1034.0| |
| |1970-01-01T08:01:12.000+08:00| 950.0| |
| |1970-01-01T08:01:13.000+08:00| 1059.0| |
| +-----------------------------+------------+ |
| </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 class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="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><h2 id="outlier" tabindex="-1"><a class="header-anchor" href="#outlier"><span>Outlier</span></a></h2><h3 id="usage-6" tabindex="-1"><a class="header-anchor" href="#usage-6"><span>Usage</span></a></h3><p>This function is used to detect distance-based outliers. For each point in the current window, if the number of its neighbors within the distance of neighbor distance threshold is less than the neighbor count threshold, the point in detected as an outlier.</p><p><strong>Name:</strong> OUTLIER</p><p><strong>Input Series：</strong> Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.</p><ul><li><code>r</code>：the neighbor distance threshold.</li><li><code>k</code>：the neighbor count threshold.</li><li><code>w</code>：the window size.</li><li><code>s</code>：the slide size.</li></ul><p><strong>Output Series:</strong> Output a single series. The type is the same as the input.</p><h3 id="examples-6" tabindex="-1"><a class="header-anchor" href="#examples-6"><span>Examples</span></a></h3><h4 id="assigning-parameters-of-queries" tabindex="-1"><a class="header-anchor" href="#assigning-parameters-of-queries"><span>Assigning Parameters of Queries</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>+-----------------------------+------------+ |
| | Time|root.test.s1| |
| +-----------------------------+------------+ |
| |2020-01-04T23:59:55.000+08:00| 56.0| |
| |2020-01-04T23:59:56.000+08:00| 55.1| |
| |2020-01-04T23:59:57.000+08:00| 54.2| |
| |2020-01-04T23:59:58.000+08:00| 56.3| |
| |2020-01-04T23:59:59.000+08:00| 59.0| |
| |2020-01-05T00:00:00.000+08:00| 60.0| |
| |2020-01-05T00:00:01.000+08:00| 60.5| |
| |2020-01-05T00:00:02.000+08:00| 64.5| |
| |2020-01-05T00:00:03.000+08:00| 69.0| |
| |2020-01-05T00:00:04.000+08:00| 64.2| |
| |2020-01-05T00:00:05.000+08:00| 62.3| |
| |2020-01-05T00:00:06.000+08:00| 58.0| |
| |2020-01-05T00:00:07.000+08:00| 58.9| |
| |2020-01-05T00:00:08.000+08:00| 52.0| |
| |2020-01-05T00:00:09.000+08:00| 62.3| |
| |2020-01-05T00:00:10.000+08:00| 61.0| |
| |2020-01-05T00:00:11.000+08:00| 64.2| |
| |2020-01-05T00:00:12.000+08:00| 61.8| |
| |2020-01-05T00:00:13.000+08:00| 64.0| |
| |2020-01-05T00:00:14.000+08:00| 63.0| |
| +-----------------------------+------------+ |
| </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 class="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> outlier<span class="token punctuation">(</span>s1<span class="token punctuation">,</span><span class="token string">"r"</span><span class="token operator">=</span><span class="token string">"5.0"</span><span class="token punctuation">,</span><span class="token string">"k"</span><span class="token operator">=</span><span class="token string">"4"</span><span class="token punctuation">,</span><span class="token string">"w"</span><span class="token operator">=</span><span class="token string">"10"</span><span class="token punctuation">,</span><span class="token string">"s"</span><span class="token operator">=</span><span class="token string">"5"</span><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>Output series:</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+--------------------------------------------------------+ |
| | Time|outlier(root.test.s1,"r"="5.0","k"="4","w"="10","s"="5")| |
| +-----------------------------+--------------------------------------------------------+ |
| |2020-01-05T00:00:03.000+08:00| 69.0| |
| +-----------------------------+--------------------------------------------------------+ |
| |2020-01-05T00:00:08.000+08:00| 52.0| |
| +-----------------------------+--------------------------------------------------------+ |
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