no code implementations • 2 Oct 2023 • Quoc Minh Nguyen, Dat Thanh Tran, Juho Kanniainen, Alexandros Iosifidis, Moncef Gabbouj
Many cryptocurrency brokers nowadays offer a variety of derivative assets that allow traders to perform hedging or speculation.
no code implementations • 26 Sep 2023 • Zaffar Zaffar, Fahad Sohrab, Juho Kanniainen, Moncef Gabbouj
The study highlights the potential of subspace learning-based OCC algorithms by investigating the limitations of current fraud detection strategies and the specific challenges of credit card fraud detection.
no code implementations • 31 Aug 2023 • Jalmari Tuominen, Eetu Pulkkinen, Jaakko Peltonen, Juho Kanniainen, Niku Oksala, Ari Palomäki, Antti Roine
In this study, we document the performance of a set of advanced ML models in forecasting ED occupancy 24 hours ahead.
1 code implementation • 17 Apr 2023 • Adamantios Ntakaris, Moncef Gabbouj, Juho Kanniainen
This high-paced stock price forecasting is usually based on vectors that need to be treated as sequential and time-independent signals due to the time irregularities that are inherent in high-frequency trading.
no code implementations • 22 Jan 2023 • Jalmari Tuominen, Teemu Koivistoinen, Juho Kanniainen, Niku Oksala, Ari Palomäki, Antti Roine
We showed that the software could predict next hour crowding with a nominal AUC of 0. 98 and 24 hour crowding with an AUC of 0. 79 using simple statistical models.
no code implementations • 26 Oct 2022 • Mostafa Shabani, Martin Magris, George Tzagkarakis, Juho Kanniainen, Alexandros Iosifidis
Cross-correlation analysis is a powerful tool for understanding the mutual dynamics of time series.
no code implementations • 23 Jul 2022 • Mostafa Shabani, Dat Thanh Tran, Juho Kanniainen, Alexandros Iosifidis
In addition, as the market evolves through time, it is necessary to update the existing models or train new ones when new data is made available.
no code implementations • 14 Jan 2022 • Mostafa Shabani, Dat Thanh Tran, Martin Magris, Juho Kanniainen, Alexandros Iosifidis
Financial time-series forecasting is one of the most challenging domains in the field of time-series analysis.
1 code implementation • 1 Sep 2021 • Dat Thanh Tran, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis
Data normalization is one of the most important preprocessing steps when building a machine learning model, especially when the model of interest is a deep neural network.
no code implementations • 13 Jul 2019 • Adamantios Ntakaris, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis
Stock price prediction is a challenging task, but machine learning methods have recently been used successfully for this purpose.
no code implementations • 31 May 2019 • Margarita Baltakienė, Kęstutis Baltakys, Juho Kanniainen, Dino Pedreschi, Fabrizio Lillo
The complex networks approach has been gaining popularity in analysing investor behaviour and stock markets, but within this approach, initial public offerings (IPO) have barely been explored.
1 code implementation • IEEE Transactions on Neural Networks and Learning Systems 2019 • Dat Thanh Tran, Alexandros Iosifidis, Juho Kanniainen, Moncef Gabbouj
Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market.
no code implementations • 10 Apr 2019 • Adamantios Ntakaris, Giorgio Mirone, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis
Mid-price movement prediction based on limit order book (LOB) data is a challenging task due to the complexity and dynamics of the LOB.
no code implementations • 5 Mar 2019 • Dat Thanh Tran, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis
Forecasting based on financial time-series is a challenging task since most real-world data exhibits nonstationary property and nonlinear dependencies.
3 code implementations • 21 Feb 2019 • Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis
Deep Learning (DL) models can be used to tackle time series analysis tasks with great success.
no code implementations • 24 Jan 2019 • Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis
However, combining existing BoF formulations with deep feature extractors pose significant challenges: the distribution of the input features is not stationary, tuning the hyper-parameters of the model can be especially difficult and the normalizations involved in the BoF model can cause significant instabilities during the training process.
no code implementations • 23 Oct 2018 • Avraam Tsantekidis, Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis
The recent surge in Deep Learning (DL) research of the past decade has successfully provided solutions to many difficult problems.
no code implementations • 19 Sep 2018 • Paraskevi Nousi, Avraam Tsantekidis, Nikolaos Passalis, Adamantios Ntakaris, Juho Kanniainen, Anastasios Tefas, Moncef Gabbouj, Alexandros Iosifidis
Forecasting the movements of stock prices is one the most challenging problems in financial markets analysis.
1 code implementation • 4 Dec 2017 • Dat Thanh Tran, Alexandros Iosifidis, Juho Kanniainen, Moncef Gabbouj
Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market.
no code implementations • 5 Sep 2017 • Dat Thanh Tran, Martin Magris, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis
Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high frequency traders.