{"id":130355,"date":"2021-06-07T13:56:52","date_gmt":"2021-06-07T17:56:52","guid":{"rendered":"https:\/\/www.sightline.com\/?p=130355"},"modified":"2026-02-17T15:48:10","modified_gmt":"2026-02-17T20:48:10","slug":"data-forecasting","status":"publish","type":"post","link":"https:\/\/www.sightline.com\/br\/data-forecasting\/blogs\/","title":{"rendered":"Previs\u00e3o de dados"},"content":{"rendered":"<h2>Um resumo do setor<\/h2><h2>O que \u00e9 previs\u00e3o de dados<\/h2><div class='yarpp yarpp-related yarpp-related-shortcode yarpp-template-list'>\n<!-- YARPP List -->\n<h3>Publica\u00e7\u00f5es relacionadas:<\/h3><ul>\n<li><a href=\"https:\/\/www.sightline.com\/br\/sightline-edm-security-event-detection\/use-cases\/\" rel=\"bookmark\" title=\"Detec\u00e7\u00e3o de eventos de seguran\u00e7a da Sightline EDM: Fortalecendo os bancos europeus para testes de estresse e regulamenta\u00e7\u00f5es rigorosas da UE\">Detec\u00e7\u00e3o de eventos de seguran\u00e7a da Sightline EDM: Fortalecendo os bancos europeus para os rigorosos testes de estresse e regulamenta\u00e7\u00f5es da UE<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/br\/q-a-unleashing-productivity-in-manufacturing\/blogs\/\" rel=\"bookmark\" title=\"An\u00e1lise avan\u00e7ada na manufatura 101: Liberando a produtividade e a lucratividade\">An\u00e1lise avan\u00e7ada na manufatura 101: Liberando a produtividade e a lucratividade<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/br\/gnsol-partnership\/blogs\/\" rel=\"bookmark\" title=\"Sightline Systems faz parceria com a GNSOL para oferecer solu\u00e7\u00f5es de TI no exterior\">Sightline Systems faz parceria com a GNSOL para oferecer solu\u00e7\u00f5es de TI no exterior<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/br\/case-study-federal-defense-capacity-planning-2\/use-cases\/\" rel=\"bookmark\" title=\"Estudo de caso: Descubra como a Sightline melhorou o planejamento da capacidade de defesa federal por meio de an\u00e1lises avan\u00e7adas e estrat\u00e9gias orientadas por dados.\">Estudo de caso: Descubra como a Sightline melhorou o planejamento da capacidade de defesa federal por meio de an\u00e1lises avan\u00e7adas e estrat\u00e9gias orientadas por dados.<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/br\/data-analysis-iiot-systems-guide\/blogs\/\" rel=\"bookmark\" title=\"Aplica\u00e7\u00e3o de ferramentas de an\u00e1lise de dados para sistemas IIoT\">Aplica\u00e7\u00e3o de ferramentas de an\u00e1lise de dados para sistemas IIoT<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/br\/iiot\/blogs\/\" rel=\"bookmark\" title=\"IIoT: Internet Industrial das Coisas\">IIoT: Internet Industrial das Coisas<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/br\/what-is-mqtt\/blogs\/\" rel=\"bookmark\" title=\"Protocolo MQTT\">Protocolo MQTT<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/br\/zero-trust-data-security\/blogs\/\" rel=\"bookmark\" title=\"Seguran\u00e7a de dados Zero-Trust\">Seguran\u00e7a de dados Zero-Trust<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/br\/correlation-data-analysis-engine-brief\/blogs\/\" rel=\"bookmark\" title=\"Correla\u00e7\u00e3o de dados\">Correla\u00e7\u00e3o de dados<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/br\/case-study-global-travel-company-root-cause-analysis\/use-cases\/\" rel=\"bookmark\" title=\"Estudo de caso: Empresa global de viagens usa an\u00e1lise de causa raiz e an\u00e1lise preditiva para 20.000 servidores\">Estudo de caso: Empresa global de viagens usa an\u00e1lise de causa raiz e an\u00e1lise preditiva para 20.000 servidores<\/a><\/li>\n<\/ul>\n<\/div>\n<h5>A Sightline tem experi\u00eancia em coletar, reter e analisar dados de s\u00e9ries temporais para implementar a previs\u00e3o de dados.<\/h5>\n<p>&nbsp;<\/p>\n<h2>O que s\u00e3o \"dados de s\u00e9rie temporal\"?<\/h2>\n<p>Os dados de s\u00e9ries temporais s\u00e3o uma cole\u00e7\u00e3o de quantidades reunidas em intervalos regulares de tempo e ordenadas cronologicamente.  As caracter\u00edsticas estat\u00edsticas dos dados de s\u00e9ries temporais frequentemente violam as suposi\u00e7\u00f5es dos m\u00e9todos estat\u00edsticos convencionais. Por esse motivo, a an\u00e1lise de dados de s\u00e9ries temporais requer um conjunto exclusivo de ferramentas e m\u00e9todos, conhecidos coletivamente como an\u00e1lise de s\u00e9ries temporais.<\/p>\n<p><strong>A previs\u00e3o \u00e9 uma das maneiras de analisar s\u00e9ries temporais para descobrir tend\u00eancias futuras dos dados de entrada fornecidos.<\/strong><\/p>\n<h2>H\u00e1 duas classes de t\u00e9cnicas populares de previs\u00e3o de dados<\/h2>\n<p><strong>S\u00e9ries temporais univariadas:<\/strong> Apenas uma vari\u00e1vel est\u00e1 variando ao longo do tempo. Por exemplo, dados coletados de um sensor que mede a velocidade de um motor a cada segundo. Portanto, a cada segundo, voc\u00ea ter\u00e1 apenas um valor unidimensional, que \u00e9 a velocidade.<\/p>\n<p><strong>S\u00e9ries temporais multivariadas:<\/strong> V\u00e1rias vari\u00e1veis est\u00e3o variando ao longo do tempo. Por exemplo, um aceler\u00f4metro tri-axial. H\u00e1 tr\u00eas acelera\u00e7\u00f5es, uma para cada eixo (x,y,z) e elas variam simultaneamente ao longo do tempo.<\/p>\n<p>H\u00e1 v\u00e1rios m\u00e9todos ou f\u00f3rmulas de previs\u00e3o dispon\u00edveis em cada t\u00e9cnica. GARCH, SARIMA etc. s\u00e3o exemplos de t\u00e9cnicas univariadas. VAR, VECM etc. s\u00e3o exemplos de t\u00e9cnicas multivariadas.<\/p>\n<h2>Como escolher o modelo adequado de previs\u00e3o de dados<\/h2>\n<p>Al\u00e9m das t\u00e9cnicas univariadas versus multivariadas, a estrutura (ou) o comportamento dos dados \u00e9 o que influencia a escolha do \"modelo\" ou da \"f\u00f3rmula de previs\u00e3o\" adequados. Alguns aspectos que afetam o comportamento dos dados s\u00e3o: sazonalidade, tend\u00eancia (linear ou n\u00e3o linear) etc., nos dados.<\/p>\n<p>Por exemplo, ao usar dados univariados, o SARIMA com par\u00e2metros apropriados pode ser um bom modelo a ser usado ao lidar com dados de s\u00e9ries temporais com sazonalidade. Se n\u00e3o houver sazonalidade nos dados, o ARIMA pode ser uma boa abordagem.<\/p>\n<p><strong>SARIMA:<\/strong> Refere-se ao ARIMA sazonal, que serve basicamente para <em>s\u00e9ries com componente de sazonalidade <\/em>neles que n\u00e3o foram tratados de forma alguma. Aqui precisamos fornecer <em>7 par\u00e2metros<\/em> onde 3 para ARIMA (AR, I, MA) e 3 para ARIMA sazonal (Season AR, Season I, Season MA) e um para a dura\u00e7\u00e3o da sazonalidade (12 meses, 6 meses, de acordo com os dados)<\/p>\n<p><strong>ARIMA:<\/strong> \u00c9<em> AutoRegress\u00e3o M\u00e9dia m\u00f3vel integrada<\/em>. Aqui, o termo integrado se refere \u00e0 diferencia\u00e7\u00e3o, ou seja, por exemplo, ao calcular o 5\u00ba termo com ordem 3, ele seria o 4\u00ba + (4\u00ba-3\u00ba)+(3\u00ba-2\u00ba) termos.<\/p>\n<p>Os recursos anal\u00edticos do Sightline EDM processam a previs\u00e3o de dados de s\u00e9ries temporais para ajudar os clientes a identificar tend\u00eancias futuras dos dados de suas m\u00e1quinas usando as t\u00e9cnicas descritas acima. A vantagem do Sightline EDM \u00e9 que ele analisa automaticamente os dados para identificar a sazonalidade\/estacionariedade dos dados e aplica m\u00e9todos de previs\u00e3o apropriados para identificar tend\u00eancias futuras.<\/p><h2>Conte\u00fado relacionado:<\/h2>","protected":false},"excerpt":{"rendered":"<p>A previs\u00e3o usa dados hist\u00f3ricos como insumos para fazer estimativas informadas que s\u00e3o preditivas para determinar a dire\u00e7\u00e3o das tend\u00eancias futuras. A escolha das t\u00e9cnicas de previs\u00e3o adequadas depende do tipo de dados que est\u00e1 sendo usado e do comportamento dos dados. Por exemplo, as t\u00e9cnicas de previs\u00e3o usadas para \"dados financeiros\" podem n\u00e3o ser apropriadas para dados de s\u00e9ries temporais como \"dados de servidores de computador\" ou \"dados de atividade de rede de data center\" (embora os princ\u00edpios de previs\u00e3o subjacentes permane\u00e7am os mesmos).<\/p>","protected":false},"author":30,"featured_media":225027,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_uf_show_specific_survey":0,"_uf_disable_surveys":false,"footnotes":""},"categories":[4032],"tags":[261,3968],"class_list":["post-130355","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blogs","tag-forecasting","tag-industry-briefs"],"yoast_head":"<!-- This site is optimized with the 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