most citedSparse Index Tracking via Topological Learning

1 citations · 2 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CE20231 cited

Sparse Index Tracking via Topological Learning

Anubha Goel, Puneet Pasricha, Juho Kanniainen

In this research, we introduce a novel methodology for the index tracking problem with sparse portfolios by leveraging topological data analysis (TDA). Utilizing persistence homolo…

cs.LG20231 cited

Cryptocurrency Portfolio Optimization by Neural Networks

Quoc Minh Nguyen, Dat Thanh Tran, Juho Kanniainen +2

Many cryptocurrency brokers nowadays offer a variety of derivative assets that allow traders to perform hedging or speculation. This paper proposes an effective algorithm based on…

cs.LG2023

Credit Card Fraud Detection with Subspace Learning-based One-Class Classification

Zaffar Zaffar, Fahad Sohrab, Juho Kanniainen +1

In an increasingly digitalized commerce landscape, the proliferation of credit card fraud and the evolution of sophisticated fraudulent techniques have led to substantial financial…

cs.LG2023

Forecasting Emergency Department Crowding with Advanced Machine Learning Models and Multivariable Input

Jalmari Tuominen, Eetu Pulkkinen, Jaakko Peltonen +4

Emergency department (ED) crowding is a significant threat to patient safety and it has been repeatedly associated with increased mortality. Forecasting future service demand has t…

cs.LG2023

Optimum Output Long Short-Term Memory Cell for High-Frequency Trading Forecasting

Adamantios Ntakaris, Moncef Gabbouj, Juho Kanniainen

High-frequency trading requires fast data processing without information lags for precise stock price forecasting. This high-paced stock price forecasting is usually based on vecto…

eess.SY2023

Early Warning Software for Emergency Department Crowding

Jalmari Tuominen, Teemu Koivistoinen, Juho Kanniainen +3

Emergency department (ED) crowding is a well-recognized threat to patient safety and it has been repeatedly associated with increased mortality. Accurate forecasts of future servic…