𝗗𝗮𝘆-𝟯𝟯𝟴 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗙𝗮𝗰𝗲𝗯𝗼𝗼𝗸 𝗔𝗜 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗲𝘀 ‘𝗡𝗲𝘂𝗿𝗮𝗹𝗣𝗿𝗼𝗽𝗵𝗲𝘁’: 𝗔 𝗛𝘆𝗯𝗿𝗶𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗕𝗮𝘀𝗲𝗱 𝗼𝗻 𝗣𝘆𝗧𝗼𝗿𝗰𝗵 𝗔𝗻𝗱 𝗧𝗿𝗮𝗶𝗻𝗲𝗱 𝗪𝗶𝘁𝗵 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗲𝘁𝗵𝗼𝗱𝘀 Follow me for a similar post: 🇮🇳 Ashish Patel ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 Paper: 𝗡𝗲𝘂𝗿𝗮𝗹𝗣𝗿𝗼𝗽𝗵𝗲𝘁: 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝗮𝘁 𝗦𝗰𝗮𝗹𝗲 🔸 This paper is published arxiv2021. 🔸 Meta/Facebook AI introduces ‘Neural Prophet‘, a simple forecasting package that provides a solution to some of the most prevalent needs of customers, seeking to maximize the scalability and flexibility of time series forecasts based on Meta’s own internal data scientists and requests from external industry practitioners. Whether it’s estimating infection rates for disease management programs or projecting product demand to store inventory properly, the expanding data size necessitates new methodologies. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 We introduce NeuralProphet, a successor to Facebook Prophet, which set an industry standard for explainable, scalable, and user-friendly forecasting frameworks. With the proliferation of time series data, explainable forecasting remains a challenging task for business and operational decision making. Hybrid solutions are needed to bridge the gap between interpretable classical methods and scalable deep learning models. We view Prophet as a precursor to such a solution. However, Prophet lacks local context, which is essential for forecasting the near-term future and is challenging to extend due to its Stan backend. 🔸 NeuralProphet is a hybrid forecasting framework based on PyTorch and trained with standard deep learning methods, making it easy for developers to extend the framework. Local context is introduced with auto-regression and covariate modules, which can be configured as classical linear regression or as Neural Networks. Otherwise, NeuralProphet retains the design philosophy of Prophet and provides the same basic model components. 🔸 Our results demonstrate that NeuralProphet produces interpretable forecast components of equivalent or superior quality to Prophet on a set of generated time series. NeuralProphet outperforms Prophet on a diverse collection of real-world datasets. For short to medium-term forecasts, NeuralProphet improves forecast accuracy by 55 to 92 percent. ------------------------------------------------------------------- #computervision #artificialintelligence #deeplearning -------------------------------------------------------------------
🚮