76 citations · 82 across the 5 of their papers we have counts for
7 papers
HERCULES: Hierarchical Embedding-based Recursive Clustering Using LLMs for Efficient Summarization
Gabor Petnehazi, Bernadett Aradi
The explosive growth of complex datasets across various modalities necessitates advanced analytical tools that not only group data effectively but also provide human-understandable…
Zero-Shot Forecasting Mortality Rates: A Global Study
Gabor Petnehazi, Laith Al Shaggah, Jozsef Gall +1
This study explores the potential of zero-shot time series forecasting, an innovative approach leveraging pre-trained foundation models, to forecast mortality rates without task-sp…
Volatility Forecasting with 1-dimensional CNNs via transfer learning
Bernadett Aradi, Gábor Petneházi, József Gáll
Volatility is a natural risk measure in finance as it quantifies the variation of stock prices. A frequently considered problem in mathematical finance is to forecast different est…
Mortality rate forecasting: can recurrent neural networks beat the Lee-Carter model?
Gábor Petneházi, József Gáll
This article applies a long short-term memory recurrent neural network to mortality rate forecasting. The model can be trained jointly on the mortality rate history of different co…
Quantile Convolutional Neural Networks for Value at Risk Forecasting
Gábor Petneházi
This article presents a new method for forecasting Value at Risk. Convolutional neural networks can do time series forecasting, since they can learn local patterns in time. A simpl…
Recurrent Neural Networks for Time Series Forecasting
Gábor Petneházi
Time series forecasting is difficult. It is difficult even for recurrent neural networks with their inherent ability to learn sequentiality. This article presents a recurrent neura…
Exploring the predictability of range-based volatility estimators using RNNs
Gábor Petneházi, József Gáll
We investigate the predictability of several range-based stock volatility estimators, and compare them to the standard close-to-close estimator which is most commonly acknowledged…