近日,由汉口学院教授陈兴博士独自撰写的英文学术专著《MACHINE LEARNING MODEL FOR CORPORATE PERFORMANCE FORECASTING》(ISBN 979-11-988609-4-1)获得韩国人文社会科学研究院(KIHSS)选定,并正式面向海内外公开出版发行。 该专著聚焦企业管理中至关重要的股本回报率(ROE)指标,系统探讨了多种先进的机器学习模型在企业绩效...
The present study seeks to quantify how beneficial food demand forecasting can be for the food catering sector. To that end, four food demand forecasting models were developed, i.e. two causal models and two time series models. Each model was based on a different machine learning algorithm, ...
因此,为了确保中期范围内有足够的集合分散程度,我们将研究与流动依赖(flow-dependent )的初始条件扰动方法,以保持FuXi集合在更长的提前期内保持合理的分散程度。【后续工作:FuXi-ENS: A machine learning model for medium-range ensemble weather forecasting】 更进一步来看,我们计划探索使用级联机器学习模型架构用于亚...
Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-...
机器学习与预测 2024秋 11-1 英文 许粲昊 Machine Learning and Forecasting by Thomas Canhao Xu, 视频播放量 215、弹幕量 0、点赞数 16、投硬币枚数 11、收藏人数 13、转发人数 1, 视频作者 许粲昊ThomasCXu, 作者简介 ,相关视频:数据处理工作坊I 2024秋 8-1 英文 许粲
Over the past few years, the rapid development of machine learning (ML) models for weather forecasting has led to state-of-the-art ML models that have superior performance compared to the European Centre for Medium-Range Weather Forecasts (ECMWF)’s high
论文阅读【因果机器学习金融】Machine learning for financial forecasting, planning and analysis 论文翻译 Abstract 这篇文章是关于机器学习在金融预测、规划和分析(FP&A)中的介绍。机器学习似乎非常适合支持FP&A,可以高度自动化地从大量数据中提取信息。然而,由于大多数传统的机器学习技术侧重于预测(预测),我们讨论了在...
Forecasting sales is a common and essential use of machine learning (ML). Sales forecasts can be used to identify benchmarks and determine incremental impacts of new initiatives, plan resources in…
In this work we present a large scale comparison study for the major machine learning models for time series forecasting. Specifically, we apply the models on the monthly M3 time series competition data (around a thousand time series). There have been very few, if any, large scale comparison...
We present a novel workflow for forecasting production in unconventional reservoirs using reduced-order models and machine-learning. Our physics-informed machine-learning workflow addresses the challenges to real-time reservoir management in unconvention