Stock Market Forecast Using Multivariate Analysis with Bidirectional and Stacked (LSTM, GRU)

Khaled Al-Thelaya · El-Sayed M. El-Alfy · Salahadin A. Mohammed

119 citations0/0 files storedSaudi Computer Society National Computer Conference (NCC)

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Stock Market Forecasting Methods Hydrological Forecasting Using AI Time Series Analysis and Forecasting

Abstract

Deep learning has recently received growing interest and attention. It has been successfully applied to many fields. Stock market time-series forecasting is one the most challenging problems for a variety of learning methodologies. In this paper, we studied the integration of deep learning methodologies into stock market forecasting. We evaluated and compared a number of variants of Deep Recurrent Neural Network based on LSTM and GRU. Both bidirectional and unidirectional stacked architectures with multivariate inputs were employed to perform short- and long-term forecasting. The deep learning architectures were also compared to shallow neural networks using S&P500 index historical data. It has been noticed that a stacked LSTM architecture has demonstrated the highest forecasting performance for both short- and long-term.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 119 citations exist upstream.

References

2017A deep learning framework for financial time series using stacked autoencoders and long-short term memory 2016Artificial Neural Networks architectures for stock price prediction: comparisons and applications 2017Sentiment-aware stock market prediction: A deep learning method

Cited by

2024Hidden Markov guided Deep Learning models for forecasting highly volatile agricultural commodity prices 2021An LSTM and GRU based trading strategy adapted to the Moroccan market 2023GRU Neural Network Based on CEEMDAN–Wavelet for Stock Price Prediction 2023Hybrid Information Mixing Module for Stock Movement Prediction 2024RETRACTED ARTICLE: Stock market forecasting using deep learning with long short-term memory and gated recurrent unit 2022Forecasting the Volatility of the Stock Index with Deep Learning Using Asymmetric Hurst Exponents 2024Memory based neural network for cumin price forecasting in Gujarat, India

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.