{"id":5579,"date":"2021-05-12T17:52:10","date_gmt":"2021-05-12T15:52:10","guid":{"rendered":"https:\/\/joapen.com\/blog\/?p=5579"},"modified":"2021-05-12T17:52:15","modified_gmt":"2021-05-12T15:52:15","slug":"how-to-solve-time-series-problems-with-machine-learning","status":"publish","type":"post","link":"https:\/\/joapen.com\/blog\/2021\/05\/12\/how-to-solve-time-series-problems-with-machine-learning\/","title":{"rendered":"How to solve Time Series problems with Machine Learning"},"content":{"rendered":"\n<p>There are different techniques or models to solve these type of problems. We can divide the type of models using this diagram:<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"823\" height=\"524\" src=\"https:\/\/joapen.com\/blog\/wp-content\/uploads\/2021\/05\/image-2.png\" alt=\"Type of Time Series Models\" class=\"wp-image-5576\" srcset=\"https:\/\/joapen.com\/blog\/wp-content\/uploads\/2021\/05\/image-2.png 823w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2021\/05\/image-2-300x191.png 300w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2021\/05\/image-2-768x489.png 768w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2021\/05\/image-2-24x15.png 24w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2021\/05\/image-2-36x23.png 36w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2021\/05\/image-2-48x31.png 48w\" sizes=\"auto, (max-width: 823px) 100vw, 823px\" \/><figcaption>Type of Time Series Models<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Traditional Time Series forecasting techniques<\/h2>\n\n\n\n<ul class=\"wp-block-list\"><li>Recursive: we can make predictions for tomorrow, the day after&#8230; to do the prediction of day #4 we will predict first #1, then #2, then #3 and then we will forecast #4.<\/li><li>Easy to expand: if we want predictions for the next 10 days, just keep recursively making predictions.<\/li><li>Thought to get right.<\/li><li>Cannot add time varying features.<\/li><\/ul>\n\n\n\n<p>Examples of techniques:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>ARIMA<\/li><li>SARIMA (seasonal ARIMA)<\/li><li>Prophet<\/li><li>Neural Prophet<\/li><li>Vector Auto-regression (VAR)<\/li><\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Machine Learning models<\/h2>\n\n\n\n<ul class=\"wp-block-list\"><li>Direct: make predictions directly independently of the horizon.<\/li><li>Thought to extend: training data increases linearly as we have more horizons to predict.<\/li><li>Easy to get right.<\/li><li>Can add time varying variables as features.<\/li><\/ul>\n\n\n\n<p>Example of techniques:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Neural network regressor<\/li><li>Catboost regressor<\/li><li>Any regressor<\/li><\/ul>\n","protected":false},"excerpt":{"rendered":"<p>There are different techniques or models to solve these type of problems. We can divide the type of models using this diagram: Traditional Time Series forecasting techniques Recursive: we can make predictions for tomorrow, the day after&#8230; to do the prediction of day #4 we will predict first #1, then #2, then #3 and then &#8230; <a title=\"How to solve Time Series problems with Machine Learning\" class=\"read-more\" href=\"https:\/\/joapen.com\/blog\/2021\/05\/12\/how-to-solve-time-series-problems-with-machine-learning\/\" aria-label=\"Read more about How to solve Time Series problems with Machine Learning\">Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[38,151],"tags":[],"class_list":["post-5579","post","type-post","status-publish","format-standard","hentry","category-learning","category-machine-learning"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>How to solve Time Series problems with Machine Learning -<\/title>\n<meta name=\"description\" content=\"There are different techniques or models to solve these type of problems. 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