| const e=JSON.parse('{"key":"v-2985154a","path":"/UserGuide/V1.3.x/User-Manual/IoTDB-AINode_timecho.html","title":"Endogenous Machine Learning Framework (AINode)","lang":"en-US","frontmatter":{"description":"AINode is the third type of endogenous node provided by IoTDB after ConfigNode and DataNode, which extends the capability of machine learning analysis of time series by interact...","head":[["link",{"rel":"alternate","hreflang":"zh-cn","href":"https://iotdb.apache.org/zh/UserGuide/V1.3.x/User-Manual/IoTDB-AINode_timecho.html"}],["meta",{"property":"og:url","content":"https://iotdb.apache.org/UserGuide/V1.3.x/User-Manual/IoTDB-AINode_timecho.html"}],["meta",{"property":"og:site_name","content":"IoTDB Website"}],["meta",{"property":"og:title","content":"Endogenous Machine Learning Framework (AINode)"}],["meta",{"property":"og:description","content":"AINode is the third type of endogenous node provided by IoTDB after ConfigNode and DataNode, which extends the capability of machine learning analysis of time series by interact..."}],["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":"2024-01-17T01:32:18.000Z"}],["meta",{"property":"article:modified_time","content":"2024-01-17T01:32:18.000Z"}],["script",{"type":"application/ld+json"},"{\\"@context\\":\\"https://schema.org\\",\\"@type\\":\\"Article\\",\\"headline\\":\\"Endogenous Machine Learning Framework (AINode)\\",\\"image\\":[\\"\\"],\\"dateModified\\":\\"2024-01-17T01:32:18.000Z\\",\\"author\\":[]}"]]},"headers":[{"level":2,"title":"1. Advantageous features","slug":"_1-advantageous-features","link":"#_1-advantageous-features","children":[]},{"level":2,"title":"2. Basic Concepts","slug":"_2-basic-concepts","link":"#_2-basic-concepts","children":[]},{"level":2,"title":"3. Installation and Deployment","slug":"_3-installation-and-deployment","link":"#_3-installation-and-deployment","children":[]},{"level":2,"title":"4. Usage Guidelines","slug":"_4-usage-guidelines","link":"#_4-usage-guidelines","children":[{"level":3,"title":"4.1 Registering Models","slug":"_4-1-registering-models","link":"#_4-1-registering-models","children":[]},{"level":3,"title":"4.2 Viewing Models","slug":"_4-2-viewing-models","link":"#_4-2-viewing-models","children":[]},{"level":3,"title":"4.3 Delete Model","slug":"_4-3-delete-model","link":"#_4-3-delete-model","children":[]},{"level":3,"title":"4.4 Using Built-in Model Reasoning","slug":"_4-4-using-built-in-model-reasoning","link":"#_4-4-using-built-in-model-reasoning","children":[]},{"level":3,"title":"4.5 Reasoning with Deep Learning Models","slug":"_4-5-reasoning-with-deep-learning-models","link":"#_4-5-reasoning-with-deep-learning-models","children":[]}]},{"level":2,"title":"5. Privilege Management","slug":"_5-privilege-management","link":"#_5-privilege-management","children":[]},{"level":2,"title":"6. Practical Examples","slug":"_6-practical-examples","link":"#_6-practical-examples","children":[{"level":3,"title":"6.1 Power Load Prediction","slug":"_6-1-power-load-prediction","link":"#_6-1-power-load-prediction","children":[]},{"level":3,"title":"6.2 Power Prediction","slug":"_6-2-power-prediction","link":"#_6-2-power-prediction","children":[]},{"level":3,"title":"6.3 Anomaly Detection","slug":"_6-3-anomaly-detection","link":"#_6-3-anomaly-detection","children":[]}]}],"git":{"createdTime":1705455138000,"updatedTime":1705455138000,"contributors":[{"name":"wanghui42","email":"105700158+wanghui42@users.noreply.github.com","commits":1}]},"readingTime":{"minutes":15.64,"words":4693},"filePathRelative":"UserGuide/V1.3.x/User-Manual/IoTDB-AINode_timecho.md","localizedDate":"January 17, 2024","autoDesc":true}');export{e as data}; |