Juho Kanniainen

Alexandros Iosifidis, Anastasios Tefas, Moncef Gabbouj, 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data pdf

Alexandros Iosifidis, Anastasios Tefas, Moncef Gabbouj, 2019, Pattern Recognit. Lett..

Practitioner's Guide on the Use of Cloud Computing in Finance

Juho Kanniainen, Binghuan Lin, Rainer Wehkamp, 2015 .

Using Deep Learning for price prediction by exploiting stationary limit order book features pdf

Alexandros Iosifidis, Anastasios Tefas, Moncef Gabbouj, 2018, Appl. Soft Comput..

Solving financial differential equations using differentiation matrices

Robert Piché, Juho Kanniainen, 2007, World Congress on Engineering.

Distributed Calibration of Option Pricing Models with Multiple Contracts Written on Different Underlying Assets

Juho Kanniainen, Marko Koskinen, 2017 .

Option pricing under joint dynamics of interest rates, dividends, and stock prices

Juho Kanniainen, J. Kanniainen, 2011, Oper. Res. Lett..

A fast universal self-tuned sampler within Gibbs sampling

Jukka Corander, Luca Martino, David Luengo, 2014, Digit. Signal Process..

Predicting Jump Arrivals in Stock Prices Using Neural Networks with Limit Order Book Data

Alexandros Iosifidis, Moncef Gabbouj, Juho Kanniainen, 2018 .

Use of distributed computing in derivative pricing

Tommi Mikkonen, Robert Piche, Juho Kanniainen, 2009 .

Temporal Bag-of-Features Learning for Predicting Mid Price Movements Using High Frequency Limit Order Book Data

Alexandros Iosifidis, Anastasios Tefas, Nikolaos Passalis, 2020, IEEE Transactions on Emerging Topics in Computational Intelligence.

Forecasting jump arrivals in stock prices: new attention-based network architecture using limit order book data

Alexandros Iosifidis, Moncef Gabbouj, Juho Kanniainen, 2018, Quantitative Finance.

Mid-price prediction based on machine learning methods with technical and quantitative indicators

Alexandros Iosifidis, Moncef Gabbouj, Juho Kanniainen, 2020, PloS one.

Deep Temporal Logistic Bag-of-features for Forecasting High Frequency Limit Order Book Time Series

Alexandros Iosifidis, Anastasios Tefas, Nikolaos Passalis, 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Deep Adaptive Input Normalization for Time Series Forecasting

Alexandros Iosifidis, Anastasios Tefas, Moncef Gabbouj, 2019, IEEE Transactions on Neural Networks and Learning Systems.

Benchmark Dataset for Mid-Price Prediction of Limit Order Book data

Alexandros Iosifidis, Moncef Gabbouj, Juho Kanniainen, 2017, ArXiv.

Data Normalization for Bilinear Structures in High-Frequency Financial Time-series pdf

Alexandros Iosifidis, Moncef Gabbouj, Juho Kanniainen, 2020, 2020 25th International Conference on Pattern Recognition (ICPR).

Forecasting the Diffusion of Innovation: A Stochastic Bass Model With Log-Normal and Mean-Reverting Error Process

Robert Piché, Juho Kanniainen, Saku J. Mäkinen, 2011, IEEE Transactions on Engineering Management.

Practitioner's Guide on the Use of Cloud Computing in Finance

Juho Kanniainen, Binghuan Lin, Rainer Wehkamp, 2015 .

Time-series classification using neural Bag-of-Features

Alexandros Iosifidis, Anastasios Tefas, Moncef Gabbouj, 2017, 2017 25th European Signal Processing Conference (EUSIPCO).