{"id":1650,"date":"2023-06-07T11:20:51","date_gmt":"2023-06-07T05:50:51","guid":{"rendered":"https:\/\/www.analyticsvidhya.com\/datahack-summit-2023\/?page_id=1650"},"modified":"2023-07-19T19:08:28","modified_gmt":"2023-07-19T13:38:28","slug":"state-of-the-art-techniques-for-time-series-forecasting","status":"publish","type":"page","link":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/state-of-the-art-techniques-for-time-series-forecasting\/","title":{"rendered":"State-of-the-art Techniques for Time Series Forecasting"},"content":{"rendered":"<p>&#8220;This session will introduce state-of-the-art time series forecasting techniques using PyTorch and PyMC3. Sales and demand forecasting is a highly value-adding application of forecasting. This use case will be demonstrated using non-hierarchical methods such as LSTMs, Temporal Fusion Transformers, NeuralProphet and DeepAR with PyTorch, as well as hierarchical forecasting methods such as mixed-effect models using PyMC3. Participants will also learn about feature engineering methods and ML deployment methods for forecasting popularly used in the industry.<\/p>\n<p><strong>Key Takeaways:<\/strong><\/p>\n<ul>\n<li>Feature Engineering for Forecasting using tsfresh library<\/li>\n<li>Demand Forecasting with PyTorch\n<ul>\n<li>Long Short-term Memory Networks (LSTMs)<\/li>\n<li>Temporal Fusion Transformers<\/li>\n<li>NeuralProphet by Meta AI<\/li>\n<li>DeepAR<\/li>\n<\/ul>\n<\/li>\n<li>Hierarchical Demand Forecasting with PyMC3\n<ul>\n<li>Mixed-effect models<\/li>\n<\/ul>\n<\/li>\n<li>ML Deployment for Forecasting\n<ul>\n<li>SHAP for interpretability<\/li>\n<li>Monitoring ex-ante vs. ex-post forecasts&#8221;<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>&#8220;This session will introduce state-of-the-art time series forecasting techniques using PyTorch and PyMC3. Sales and demand forecasting is a highly value-adding application of forecasting. This use case will be demonstrated using non-hierarchical methods such as LSTMs, Temporal Fusion Transformers, NeuralProphet and DeepAR with PyTorch, as well as hierarchical forecasting methods such as mixed-effect models using [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1651,"parent":1126,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"session-details.php","meta":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>State-of-the-art Techniques for Time Series Forecasting - DataHack Summit 2023<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/session\/state-of-the-art-techniques-for-time-series-forecasting\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"State-of-the-art Techniques for Time Series Forecasting - DataHack Summit 2023\" \/>\n<meta property=\"og:description\" content=\"&#8220;This session will introduce state-of-the-art time series forecasting techniques using PyTorch and PyMC3. 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