{"id":1088886,"date":"2024-09-28T19:31:01","date_gmt":"2024-09-29T02:31:01","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-blog-post&p=1088886"},"modified":"2024-09-28T19:31:03","modified_gmt":"2024-09-29T02:31:03","slug":"probts-unified-benchmarking-for-time-series-forecasting","status":"publish","type":"msr-blog-post","link":"https:\/\/www.microsoft.com\/en-us\/research\/articles\/probts-unified-benchmarking-for-time-series-forecasting\/","title":{"rendered":"ProbTS: Unified benchmarking for time-series forecasting"},"content":{"rendered":"\n

Author: Machine Learning Group<\/em><\/p>\n\n\n\n

Time-series forecasting is crucial across various industries, including health, energy, commerce, climate, etc. Accurate forecasts over different prediction horizons are essential for both short-term and long-term planning needs across these domains. For instance, during a public health emergency such as the COVID-19 pandemic, projections of infected cases and fatalities over one to four weeks are essential for allocating medical and societal resources effectively. In the energy sector, precise forecasts of electricity demand on an hourly, daily, weekly, and even monthly basis are crucial for power management and renewable energy scheduling. Logistics relies on forecasting short-term and long-term cargo volumes for adaptive route scheduling and efficient supply chain management.<\/p>\n\n\n\n

Beyond covering various prediction horizons, accurate forecasting must extend beyond point estimates to include distributional forecasts that quantify estimation uncertainty. Both the expected estimates and the associated uncertainties are indispensable for subsequent planning and optimization, providing a comprehensive view that informs better decision-making.<\/p>\n\n\n\n

Given the critical need for accurate point and distributional forecasting across diverse prediction horizons, researchers from Microsoft Research Asia revisited existing time-series forecasting studies to assess their effectiveness in meeting these essential demands. The review encompasses state-of-the-art models developed across various research threads:<\/p>\n\n\n\n