Solving financial differential equations using differentiation matrices. - Robert Piché; Juho Kanniainen (World Congress on Engineering, 2007)

Matrix-based numerical modelling of financial differential equations. - Robert Piché; Juho Kanniainen (Int. J. Math. Model. Numer. Optimisation, 2009)

Can properly discounted projects follow geometric Brownian motion? - Juho Kanniainen (Math. Methods Oper. Res., 2009)

Option pricing under joint dynamics of interest rates, dividends, and stock prices. - Juho Kanniainen (Oper. Res. Lett., 2011)

Forecasting the Diffusion of Innovation: A Stochastic Bass Model With Log-Normal and Mean-Reverting Error Process. - Juho Kanniainen; Saku J. Mäkinen; Robert Piché; Alok Chakrabarti (IEEE Trans. Engineering Management, 2011)

Calibration of GARCH models using concurrent accelerated random search. - Juliane Müller 004; Juho Kanniainen; Robert Piché (Appl. Math. Comput., 2013)

A fast universal self-tuned sampler within Gibbs sampling. - Luca Martino; H. Yang; David Luengo; Juho Kanniainen; Jukka Corander (Digit. Signal Process., 2015)

Time-series classification using neural Bag-of-Features. - Nikolaos Passalis; Avraam Tsantekidis; Anastasios Tefas; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis (EUSIPCO, 2017)

Using deep learning to detect price change indications in financial markets. - Avraam Tsantekidis; Nikolaos Passalis; Anastasios Tefas; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis (EUSIPCO, 2017)

Long-range auto-correlations in limit order book markets: Inter-and cross-event analysis. - Martin Magris; Jiyeong Kim; Esa Räsänen; Juho Kanniainen (SSCI, 2017)

Tensor representation in high-frequency financial data for price change prediction. - Dat Thanh Tran; Martin Magris; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis (SSCI, 2017)

Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks. - Avraam Tsantekidis; Nikolaos Passalis; Anastasios Tefas; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis (CBI (1), 2017)

Benchmark Dataset for Mid-Price Prediction of Limit Order Book data. - Adamantios Ntakaris; Martin Magris; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis (CoRR, 2017)

Machine Learning for Forecasting Mid Price Movement using Limit Order Book Data. - Paraskevi Nousi; Avraam Tsantekidis; Nikolaos Passalis; Adamantios Ntakaris; Juho Kanniainen; Anastasios Tefas; Moncef Gabbouj; Alexandros Iosifidis (CoRR, 2018)

Deep Temporal Logistic Bag-of-features for Forecasting High Frequency Limit Order Book Time Series. - Nikolaos Passalis; Anastasios Tefas; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis (ICASSP, 2019)

Machine Learning for Forecasting Mid-Price Movements Using Limit Order Book Data. - Paraskevi Nousi; Avraam Tsantekidis; Nikolaos Passalis; Adamantios Ntakaris; Juho Kanniainen; Anastasios Tefas; Moncef Gabbouj; Alexandros Iosifidis (IEEE Access, 2019)

Feature Engineering for Mid-Price Prediction With Deep Learning. - Adamantios Ntakaris; Giorgio Mirone; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis (IEEE Access, 2019)

Deep Adaptive Input Normalization for Price Forecasting using Limit Order Book Data. - Nikolaos Passalis; Anastasios Tefas; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis (CoRR, 2019)

Data-driven Neural Architecture Learning For Financial Time-series Forecasting. - Dat Thanh Tran; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis (CoRR, 2019)

Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators. - Adamantios Ntakaris; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis (CoRR, 2019)

Temporal Attention-Augmented Bilinear Network for Financial Time-Series Data Analysis. - Dat Thanh Tran; Alexandros Iosifidis; Juho Kanniainen; Moncef Gabbouj (IEEE Trans. Neural Networks Learn. Syst., 2019)

Adaptive Normalization for Forecasting Limit Order Book Data Using Convolutional Neural Networks. - Nikolaos Passalis; Anastasios Tefas; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis (ICASSP, 2020)

Data Normalization for Bilinear Structures in High-Frequency Financial Time-series. - Dat Thanh Tran; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis (ICPR, 2020)

Using Deep Learning for price prediction by exploiting stationary limit order book features. - Avraam Tsantekidis; Nikolaos Passalis; Anastasios Tefas; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis (Appl. Soft Comput., 2020)

Temporal logistic neural Bag-of-Features for financial time series forecasting leveraging limit order book data. - Nikolaos Passalis; Anastasios Tefas; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis (Pattern Recognit. Lett., 2020)

Temporal Bag-of-Features Learning for Predicting Mid Price Movements Using High Frequency Limit Order Book Data. - Nikolaos Passalis; Anastasios Tefas; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis (IEEE Trans. Emerg. Top. Comput. Intell., 2020)

Deep Adaptive Input Normalization for Time Series Forecasting. - Nikolaos Passalis; Anastasios Tefas; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis (IEEE Trans. Neural Networks Learn. Syst., 2020)

Bilinear Input Normalization for Neural Networks in Financial Forecasting. - Dat Thanh Tran; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis (CoRR, 2021)

Structure of Investor Networks and Financial Crises. - Kestutis Baltakys; Hung Le Viet; Juho Kanniainen (Entropy, 2021)

Forecasting Financial Time Series Using Robust Deep Adaptive Input Normalization. - Nikolaos Passalis; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis; Anastasios Tefas (J. Signal Process. Syst., 2021)

Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction. - Mostafa Shabani; Dat Thanh Tran; Martin Magris; Juho Kanniainen; Alexandros Iosifidis (EUSIPCO, 2022)

How informative is the Order Book Beyond the Best Levels? Machine Learning Perspective. - Dat Thanh Tran; Juho Kanniainen; Alexandros Iosifidis (CoRR, 2022)

Trade synchronization and social ties in stock markets. - Margarita Baltakiene; Kestutis Baltakys; Juho Kanniainen (EPJ Data Sci., 2022)

Cryptocurrency Portfolio Optimization by Neural Networks. - Quoc Minh Nguyen; Dat Thanh Tran; Juho Kanniainen; Alexandros Iosifidis; Moncef Gabbouj (SSCI, 2023)

Credit Card Fraud Detection with Subspace Learning-based One-Class Classification. - Zaffar Zaffar; Fahad Sohrab; Juho Kanniainen; Moncef Gabbouj (SSCI, 2023)

Optimum Output Long Short-Term Memory Cell for High-Frequency Trading Forecasting. - Adamantios Ntakaris; Moncef Gabbouj; Juho Kanniainen (CoRR, 2023)

Forecasting Emergency Department Crowding with Advanced Machine Learning Models and Multivariable Input. - Jalmari Tuominen; Eetu Pulkkinen; Jaakko Peltonen; Juho Kanniainen; Niku Oksala; Ari Palomäki; Antti Roine (CoRR, 2023)

Sparse Index Tracking via Topological Learning. - Anubha Goel; Puneet Pasricha; Juho Kanniainen (CoRR, 2023)

Predicting the state of synchronization of financial time series using cross recurrence plots. - Mostafa Shabani; Martin Magris; George Tzagkarakis; Juho Kanniainen; Alexandros Iosifidis (Neural Comput. Appl., 2023)

Augmented bilinear network for incremental multi-stock time-series classification. - Mostafa Shabani; Dat Thanh Tran; Juho Kanniainen; Alexandros Iosifidis (Pattern Recognit., 2023)

Predicting the trading behavior of socially connected investors: Graph neural network approach with implications to market surveillance. - Kestutis Baltakys; Margarita Baltakiene; Negar Heidari; Alexandros Iosifidis; Juho Kanniainen (Expert Syst. Appl., 2023)

Early Warning Software for Emergency Department Crowding. - Jalmari Tuominen; Teemu Koivistoinen; Juho Kanniainen; Niku Oksala; Ari Palomäki; Antti Roine (J. Medical Syst., 2023)

Detachment Problem - Application in Prevention of Information Leakage in Stock Markets. - Henri Hansen; Juho Kanniainen (CoRR, 2024)

Forecasting mortality associated emergency department crowding. - Jalmari Nevanlinna; Anna Eidstø; Jari Ylä-Mattila; Teemu Koivistoinen; Niku Oksala; Juho Kanniainen; Ari Palomäki; Antti Roine (CoRR, 2024)

Time-Series Foundation Model for Value-at-Risk. - Anubha Goel; Puneet Pasricha; Juho Kanniainen (CoRR, 2024)

Valuation of option price in commodity markets described by a Markov-switching model: A case study of WTI crude oil market. - Farshid Mehrdoust; Idin Noorani; Juho Kanniainen (Math. Comput. Simul., 2024)

Learning hidden cascades via classification. - Derrick Gilchrist Edward Manoharan; Anubha Goel; Alexandros Iosifidis; Henri Hansen; Juho Kanniainen (CoRR, 2025)

Variational Graph Convolutional Neural Networks. - Illia Oleksiienko; Juho Kanniainen; Alexandros Iosifidis (CoRR, 2025)

Forecasting Mortality Associated Emergency Department Crowding with LightGBM and Time Series Data. - Jalmari Nevanlinna; Anna Eidstø; Jari Ylä-Mattila; Teemu Koivistoinen; Niku Oksala; Juho Kanniainen; Ari Palomäki; Antti Roine (J. Medical Syst., 2025)