4 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2025
Efficient Causal Discovery for Autoregressive Time Series
Mohammad Fesanghary, Achintya Gopal
In this study, we present a novel constraint-based algorithm for causal structure learning specifically designed for nonlinear autoregressive time series. Our algorithm significant…
q-fin.ST2025
DELPHYNE: A Pre-Trained Model for General and Financial Time Series
Xueying Ding, Aakriti Mittal, Achintya Gopal
Time-series data is a vital modality within data science communities. This is particularly valuable in financial applications, where it helps in detecting patterns, understanding m…
q-fin.ST2023★ 4 cited
Causal Discovery in Financial Markets: A Framework for Nonstationary Time-Series Data
Agathe Sadeghi, Achintya Gopal, Mohammad Fesanghary
This paper introduces a new causal structure learning method for nonstationary time series data, a common data type found in fields such as finance, economics, healthcare, and envi…