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Found 6 papers, 2 papers with code
Adapting to the Unknown: Robust Meta-Learning for Zero-Shot Financial Time Series Forecasting
Financial time series forecasting in the zero-shot setting is essential for risk management and investment decision-making, particularly during abrupt market regime shifts or in emerging markets with limited historical data.
PLUTUS: A Well Pre-trained Large Unified Transformer can Unveil Financial Time Series Regularities
To our knowledge, PLUTUS is the first open-source, large-scale, pre-trained financial time series model with over one billion parameters.
AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarking
AbdomenAtlas provides 673K high-quality masks of anatomical structures in the abdominal region annotated by a team of 10 radiologists with the help of AI algorithms.
ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation
As an extension of the ClimSim dataset (Yu et al., 2024), we present ClimSim-Online, which also includes an end-to-end workflow for developing hybrid ML-physics simulators.
Hermite Polynomial-based Valuation of American Options with General Jump-Diffusion Processes
We present a new approximation scheme for the price and exercise policy of American options.
Pairs Trading with Nonlinear and Non-Gaussian State Space Models
The results show that the new approach can achieve a 21. 86% annualized return for the PEP/KO pair and a 31. 84% annualized return for the EWT/EWH pair.
