Deep learning for multivariate financial time series

By Bilberto Batres-Estrada. 2015

67 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Time-Series & Forecasting

Citations in the corpus

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, background): “The authors of [93] combined DBN and MLP to construct a stock portfolio by predicting each stock’s monthly log-return and choosing the only stocks that were expected to perform better than the performance of the median stock.”
Deep Learning for Financial Applications : A Survey (2020, background): “[135] S&P500 1985-2006 monthly and daily log-returns DBN+MLP Validation, Test Error Theano, Python, Matlab”
Deep learning for financial time series forecasting in A-Trader system (2017, background): “One of these models is based on deep learning [13,14].”
NSE Stock Market Prediction Using Deep-Learning Models (2018, methodology): “In [21], Batres-Estrada explains about different applications of DL models on time series analysis.”
Stock market's price movement prediction with LSTM neural networks (2017, background): “Taking a closer look into works related to deep learning in stock markets there are some examples like [10] where a study is made on the usage of a Deep Belief Network (DBN), which is composed of stacked Restricted Boltzmann Machines, coupled to a Multi-level Perceptron (MLP) and using long range lo”
Stock price prediction using LSTM, RNN and CNN-sliding window model (2017, methodology): “Non-linear models involve methods like ARCH,GARCH,[3] TAR,Deep learning algorithms [5].”
BRITS: Bidirectional Recurrent Imputation for Time Series (2018, background): “Multivariate time series data are abundant in many application areas, such as financial marketing [5, 4], health-care [10, 22], meteorology [31, 26], and traffic engineering [29, 35].”