Juho Kanniainen

According to our database1, Juho Kanniainen authored at least 27 papers between 2007 and 2020.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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Bibliography

2020
Deep Adaptive Input Normalization for Time Series Forecasting.
IEEE Trans. Neural Networks Learn. Syst., 2020

Temporal Bag-of-Features Learning for Predicting Mid Price Movements Using High Frequency Limit Order Book Data.
IEEE Trans. Emerg. Top. Comput. Intell., 2020

Temporal logistic neural Bag-of-Features for financial time series forecasting leveraging limit order book data.
Pattern Recognit. Lett., 2020

Data Normalization for Bilinear Structures in High-Frequency Financial Time-series.
CoRR, 2020

Using Deep Learning for price prediction by exploiting stationary limit order book features.
Appl. Soft Comput., 2020

Adaptive Normalization for Forecasting Limit Order Book Data Using Convolutional Neural Networks.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

2019
Temporal Attention-Augmented Bilinear Network for Financial Time-Series Data Analysis.
IEEE Trans. Neural Networks Learn. Syst., 2019

Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators.
CoRR, 2019

Data-driven Neural Architecture Learning For Financial Time-series Forecasting.
CoRR, 2019

Deep Adaptive Input Normalization for Price Forecasting using Limit Order Book Data.
CoRR, 2019

Feature Engineering for Mid-Price Prediction With Deep Learning.
IEEE Access, 2019

Machine Learning for Forecasting Mid-Price Movements Using Limit Order Book Data.
IEEE Access, 2019

Deep Temporal Logistic Bag-of-features for Forecasting High Frequency Limit Order Book Time Series.
Proceedings of the IEEE International Conference on Acoustics, 2019

2018
Machine Learning for Forecasting Mid Price Movement using Limit Order Book Data.
CoRR, 2018

2017
Benchmark Dataset for Mid-Price Prediction of Limit Order Book data.
CoRR, 2017

Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks.
Proceedings of the 19th IEEE Conference on Business Informatics, 2017

Tensor representation in high-frequency financial data for price change prediction.
Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017

Long-range auto-correlations in limit order book markets: Inter-and cross-event analysis.
Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017

Using deep learning to detect price change indications in financial markets.
Proceedings of the 25th European Signal Processing Conference, 2017

Time-series classification using neural Bag-of-Features.
Proceedings of the 25th European Signal Processing Conference, 2017

2015
A fast universal self-tuned sampler within Gibbs sampling.
Digit. Signal Process., 2015

2013
Calibration of GARCH models using concurrent accelerated random search.
Appl. Math. Comput., 2013

2011
Forecasting the Diffusion of Innovation: A Stochastic Bass Model With Log-Normal and Mean-Reverting Error Process.
IEEE Trans. Engineering Management, 2011

Option pricing under joint dynamics of interest rates, dividends, and stock prices.
Oper. Res. Lett., 2011

2009
Can properly discounted projects follow geometric Brownian motion?
Math. Methods Oper. Res., 2009

Matrix-based numerical modelling of financial differential equations.
Int. J. Math. Model. Numer. Optimisation, 2009

2007
Solving financial differential equations using differentiation matrices.
Proceedings of the World Congress on Engineering, 2007


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