Time series econometrics and forecasting constitute a dynamic research area that combines sophisticated statistical methodologies with economic theory to model, interpret and predict economic and ...

汪思韦，湖南大学金融与统计学院副教授。2022年从北京大学获得应用经济学博士学位，现主持国家自然科学基金青年科学基金项目。主要研究领域为非参数计量建模、高维数据分析等，研究成果发表在Journal of Econometrics, Oxford Bulletin of Economics and Statistics，Economics Letters以及《系统工程理论与实践》上。

Singular Spectrum Analysis (SSA) is a powerful nonparametric method that has emerged as a vital tool in the analysis and forecasting of time series data. By utilising matrix decomposition techniques, ...

Time series forecasts are used to predict a future value or a classification at a particular point in time. Here’s a brief overview of their common uses and how they are developed. Industries from ...

According to IBM, attention is not all you need when forecasting certain outcomes with generative AI. You also need time. Earlier this year, IBM made its open-source TinyTimeMixer (TTM) model ...

Time series analysis involves identifying attributes of your time series data, such as trend and seasonality, by measuring statistical properties. From stock market analysis to economic forecasting, ...

In the ever-evolving landscape of capital infrastructure projects, government agencies find themselves performing an intricate dance. The heightened focus on the timely and budget-conforming ...

Rainfall prediction has advanced rapidly with the adoption of machine learning, but most models remain optimized for overall ...

In today’s capital market, investment firms need to manage and analyze massive volumes of historical and streaming data (‘tick data’) to help forecast market trends, back-test trading strategies, ...

Time series graphs are intuitive, helping you relate a metric to time. Marketing analysts are often faced with choosing a data visualization that speaks to managers and colleagues interested in ...