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

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Stock Market Forecasting Methods Neural Networks and Applications Time Series Analysis and Forecasting

Abstract

• Logistic Neural Bag-of-Features are employed for financial time series analysis. • The proposed method can be efficiently used in deep learning architectures. • An adaptive scaling method is proposed to ensure the smooth flow of information. • A logistic kernel is used to estimate the feature vector densities. • The proposed method outperforms the competitive methods on a large-scale dataset. Time series forecasting is a crucial component of many important applications, ranging from forecasting the stock markets to energy load prediction. The high-dimensionality, velocity and variety of the data collected in many of these applications pose significant and unique challenges that must be carefully addressed for each of them. In this work, a novel Temporal Logistic Neural Bag-of-Features approach, that can be used to tackle these challenges, is proposed. The proposed method can be effectively combined with deep neural networks , leading to powerful deep learning models for time series analysis. 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. The proposed method is capable of overcoming these limitations by a employing a novel adaptive scaling mechanism and replacing the classical Gaussian-based density estimation involved in the regular BoF model with a logistic kernel. The effectiveness of the proposed approach is demonstrated using extensive experiments on a large-scale limit order book dataset that consists of more than 4 million limit orders.

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References

1960A Coefficient of Agreement for Nominal Scales 2007Giving Content to Investor Sentiment: The Role of Media in the Stock Market 2003Support vector machine with adaptive parameters in financial time series forecasting 2016Deep learning for stock prediction using numerical and textual information 2017Natural language based financial forecasting: a survey 2017Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks 2017Using deep learning to detect price change indications in financial markets 2015Modelling high-frequency limit order book dynamics with support vector machines

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