[HTML][HTML] Machine learning techniques and data for stock market forecasting: A literature review
In this literature review, we investigate machine learning techniques that are applied for
stock market prediction. A focus area in this literature review is the stock markets …
stock market prediction. A focus area in this literature review is the stock markets …
Literature review: Machine learning techniques applied to financial market prediction
BM Henrique, VA Sobreiro, H Kimura - Expert Systems with Applications, 2019 - Elsevier
The search for models to predict the prices of financial markets is still a highly researched
topic, despite major related challenges. The prices of financial assets are non-linear …
topic, despite major related challenges. The prices of financial assets are non-linear …
Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis
M Nabipour, P Nayyeri, H Jabani, S Shahab… - Ieee …, 2020 - ieeexplore.ieee.org
The nature of stock market movement has always been ambiguous for investors because of
various influential factors. This study aims to significantly reduce the risk of trend prediction …
various influential factors. This study aims to significantly reduce the risk of trend prediction …
Multivariate time series forecasting via attention-based encoder–decoder framework
Time series forecasting is an important technique to study the behavior of temporal data and
forecast future values, which is widely applied in many fields, eg air quality forecasting …
forecast future values, which is widely applied in many fields, eg air quality forecasting …
[HTML][HTML] Time series forecasting using artificial neural networks methodologies: A systematic review
A Tealab - Future Computing and Informatics Journal, 2018 - Elsevier
This paper studies the advances in time series forecasting models using artificial neural
network methodologies in a systematic literature review. The systematic review has been …
network methodologies in a systematic literature review. The systematic review has been …
Fusion in stock market prediction: a decade survey on the necessity, recent developments, and potential future directions
A Thakkar, K Chaudhari - Information Fusion, 2021 - Elsevier
Investment in a financial market is aimed at getting higher benefits; this complex market is
influenced by a large number of events wherein the prediction of future market dynamics is …
influenced by a large number of events wherein the prediction of future market dynamics is …
A survey on machine learning models for financial time series forecasting
Financial time series (FTS) are nonlinear, dynamic and chaotic. The search for models to
facilitate FTS forecasting has been highly pursued for decades. Despite major related …
facilitate FTS forecasting has been highly pursued for decades. Despite major related …
A survey of data fusion in smart city applications
The advancement of various research sectors such as Internet of Things (IoT), Machine
Learning, Data Mining, Big Data, and Communication Technology has shed some light in …
Learning, Data Mining, Big Data, and Communication Technology has shed some light in …
Forecasting stock index price using the CEEMDAN-LSTM model
Y Lin, Y Yan, J Xu, Y Liao, F Ma - The North American Journal of Economics …, 2021 - Elsevier
This paper uses a mixture model that Long Short-Term Memory (LSTM) combines with
Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to …
Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to …
Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques
This paper addresses problem of predicting direction of movement of stock and stock price
index for Indian stock markets. The study compares four prediction models, Artificial Neural …
index for Indian stock markets. The study compares four prediction models, Artificial Neural …