# Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022

arXiv: https://arxiv.org/abs/2305.04811 · HTML: https://mlsanity.com/paper/2305.04811/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 131 references that are in the map.
- First posted on arXiv 21 Apr 2023.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects](https://mlsanity.com/paper/2004.02806/) (2020)
- [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017)
- [Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019](https://mlsanity.com/paper/1911.13288/) (2019)
- [Efficient Learning for Deep Quantum Neural Networks](https://mlsanity.com/paper/1902.10445/) (2019)
- [Adaptive Conformal Predictions for Time Series](https://mlsanity.com/paper/2202.07282/) (2022)
- [How to Construct Deep Recurrent Neural Networks](https://mlsanity.com/paper/1312.6026/) (2013)
- [Applications of deep learning in stock market prediction: recent progress](https://mlsanity.com/paper/2003.01859/) (2020)
- [An Experimental Review on Deep Learning Architectures for Time Series Forecasting](https://mlsanity.com/paper/2103.12057/) (2021)
- [Using Deep Learning for price prediction by exploiting stationary limit order book features](https://mlsanity.com/paper/1810.09965/) (2018)
- [Review of automated time series forecasting pipelines](https://mlsanity.com/paper/2202.01712/) (2022)
- [MRC-LSTM: A Hybrid Approach of Multi-scale Residual CNN and LSTM to Predict Bitcoin Price](https://mlsanity.com/paper/2105.00707/) (2021)
- [Forecasting Crude Oil Price Using Event Extraction](https://mlsanity.com/paper/2111.09111/) (2021)

## Built on by (most cited first, 17 of 17)

- [LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting](https://mlsanity.com/paper/2410.11674/) (2024)
- [S4M: S4 for multivariate time series forecasting with Missing values](https://mlsanity.com/paper/2503.00900/) (2025)
- [Forecasting Company Fundamentals](https://mlsanity.com/paper/2411.05791/) (2024)
- [Recurrent Neural Networks for Dynamic VWAP Execution: Adaptive Trading Strategies with Temporal Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2502.18177/) (2025)
- [Combining supervised and unsupervised learning methods to predict financial market movements](https://mlsanity.com/paper/2409.03762/) (2024)
- [Trading through Earnings Seasons using Self-Supervised Contrastive Representation Learning](https://mlsanity.com/paper/2409.17392/) (2024)
- [Local Attention Mechanism: Boosting the Transformer Architecture for Long-Sequence Time Series Forecasting](https://mlsanity.com/paper/2410.03805/) (2024)
- [Concealed Adversarial attacks on neural networks for sequential data](https://mlsanity.com/paper/2502.20948/) (2025)
- [VWAP Execution with Signature-Enhanced Transformers: A Multi-Asset Learning Approach](https://mlsanity.com/paper/2503.02680/) (2025)
- [1D-CapsNet-LSTM: A Deep Learning-Based Model for Multi-Step Stock Index Forecasting](https://mlsanity.com/paper/2310.02090/) (2023)
- [Enhancing Black-Litterman Portfolio via Hybrid Forecasting Model Combining Multivariate Decomposition and Noise Reduction](https://mlsanity.com/paper/2505.01781/) (2025)
- [Enhancing Forecasting with a 2D Time Series Approach for Cohort-Based Data](https://mlsanity.com/paper/2508.15369/) (2025)
- [TABL-ABM: A Hybrid Framework for Synthetic LOB Generation](https://mlsanity.com/paper/2510.22685/) (2025)
- [XGBoost Forecasting of NEPSE Index Log Returns with Walk Forward Validation](https://mlsanity.com/paper/2601.08896/) (2026)
- [ARTEMIS: A Neuro Symbolic Framework for Economically Constrained Market Dynamics](https://mlsanity.com/paper/2603.18107/) (2026)
- [Beyond Sequential Prediction: Learning Financial Market Dynamics in Volatile and Non-Stationary Environments through Sentiment-Conditioned Generative Modelling](https://mlsanity.com/paper/2604.22801/) (2026)
- [MACTS-EM: Multi-Agent Collaborative Time Series Forecasting with Emergent Memory](https://mlsanity.com/paper/2610.02255/) (2026)

## Abstract (arXiv)

Accurately predicting the prices of financial time series is essential and challenging for the financial sector. Owing to recent advancements in deep learning techniques, deep learning models are gradually replacing traditional statistical and machine learning models as the first choice for price forecasting tasks. This shift in model selection has led to a notable rise in research related to applying deep learning models to price forecasting, resulting in a rapid accumulation of new knowledge. Therefore, we conducted a literature review of relevant studies over the past three years with a view to aiding researchers and practitioners in the field. This review delves deeply into deep learning-based forecasting models, presenting information on model architectures, practical applications, and their respective advantages and disadvantages. In particular, detailed information is provided on advanced models for price forecasting, such as Transformers, generative adversarial networks (GANs), graph neural networks (GNNs), and deep quantum neural networks (DQNNs). The present contribution also includes potential directions for future research, such as examining the effectiveness of deep learning models with complex structures for price forecasting, extending from point prediction to interval prediction using deep learning models, scrutinising the reliability and validity of decomposition ensembles, and exploring the influence of data volume on model performance.