DeepAI AI Chat
Log In Sign Up

Bilinear Input Normalization for Neural Networks in Financial Forecasting

by   Dat Thanh Tran, et al.
Tampere Universities

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 because deep neural network optimized with stochastic gradient descent is sensitive to the input variable range and prone to numerical issues. Different than other types of signals, financial time-series often exhibit unique characteristics such as high volatility, non-stationarity and multi-modality that make them challenging to work with, often requiring expert domain knowledge for devising a suitable processing pipeline. In this paper, we propose a novel data-driven normalization method for deep neural networks that handle high-frequency financial time-series. The proposed normalization scheme, which takes into account the bimodal characteristic of financial multivariate time-series, requires no expert knowledge to preprocess a financial time-series since this step is formulated as part of the end-to-end optimization process. Our experiments, conducted with state-of-the-arts neural networks and high-frequency data from two large-scale limit order books coming from the Nordic and US markets, show significant improvements over other normalization techniques in forecasting future stock price dynamics.


page 1

page 2

page 3

page 4


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

Financial time-series analysis and forecasting have been extensively stu...

Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Financial forecasting is an example of a signal processing problem which...

Prior knowledge distillation based on financial time series

One of the major characteristics of financial time series is that they c...

A Survey on Knowledge integration techniques with Artificial Neural Networks for seq-2-seq/time series models

In recent years, with the advent of massive computational power and the ...

Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis

Financial time-series forecasting has long been a challenging problem be...

DMIDAS: Deep Mixed Data Sampling Regression for Long Multi-Horizon Time Series Forecasting

Neural forecasting has shown significant improvements in the accuracy of...