Hierarchical Loan Forecasting
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Hierarchical Loan Forecasting
This Study applies the hierarchical time series framework to geographically and sector-wise disaggregated loan data
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- 0 Rating
- 0 Reviews
- 2 Students Enrolled
Requirements
- Basic knowledge of time series forecasting/data science/ machine learning
General Overview
Description
This study introduces time series from the private debt markets and their hierarchical structures. Collected loan data from the central bank of italy is forecasted using a combination of SOTA ML algorithms and the Hierarchical Time Series framework. The resulting models are coherent probabilistic end-to-end forecasters, significantly improving performance compared to benhcmarks while guaranteeing hierarchical consistency and thus facilitating downstream applications.
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Meet the instructors !
Wolfgang Karl HÄRDLE attained his Dr. rer. nat. in Mathematics at Universität Heidelberg in 1982 and in 1988 his habilitation at Universität Bonn. He is Ladislaus von Bortkiewicz Professor of Statistics at Humboldt-Universität zu Berlin and the director of the Sino German Graduate School (洪堡大学 + 厦门大学) IRTG1792 on “High dimensional non stationary time series analysis”. He directs IDA Institute for Digital Assets,
University of Economic Studies, Bucharest, RO. His research focuses on data analytics, dimension reduction and quantitative finance. He has published over 30 books and more than 300 papers in top statistical, econometrics and finance journals. He is highly ranked and cited on Google Scholar, REPEC and SSRN. He has professional experience in financial engineering, S.M.A.R.T. (Specific, Measurable, Achievable, Relevant, Timely) data analytics, machine learning and cryptocurrency markets. He has created the www.quantlet.com platform, a cryptocurrency index, CRIX www.royalton-crix.com He is 玉山学者 （Yushan Scholar), web page hu.berlin/wkh