249 citations · 1.2k across the 77 of their papers we have counts for
5 papers · 1 filter
Bilinear Input Normalization for Neural Networks in Financial Forecasting
Dat Thanh Tran, Juho Kanniainen, Moncef Gabbouj +1
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. This is…
Augmenting transferred representations for stock classification
Elizabeth Fons, Paula Dawson, Xiao-jun Zeng +2
Stock classification is a challenging task due to high levels of noise and volatility of stocks returns. In this paper we show that using transfer learning can help with this task,…
Evaluating data augmentation for financial time series classification
Elizabeth Fons, Paula Dawson, Xiao-jun Zeng +2
Data augmentation methods in combination with deep neural networks have been used extensively in computer vision on classification tasks, achieving great success; however, their us…
Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators
Adamantios Ntakaris, Juho Kanniainen, Moncef Gabbouj +1
Stock price prediction is a challenging task, but machine learning methods have recently been used successfully for this purpose. In this paper, we extract over 270 hand-crafted fe…
Feature Engineering for Mid-Price Prediction with Deep Learning
Adamantios Ntakaris, Giorgio Mirone, Juho Kanniainen +2
Mid-price movement prediction based on limit order book (LOB) data is a challenging task due to the complexity and dynamics of the LOB. So far, there have been very limited attempt…