# Robust Analysis of Stock Price Time Series Using CNN and LSTM-Based Deep Learning Models

arXiv: https://arxiv.org/abs/2011.08011 · HTML: https://mlsanity.com/paper/2011.08011/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2020.

## Built on by (most cited first, 11 of 11)

- [Kolmogorov-Arnold Networks (KANs) for Time Series Analysis](https://mlsanity.com/paper/2405.08790/) (2024)
- [AI in Finance: Challenges, Techniques and Opportunities](https://mlsanity.com/paper/2107.09051/) (2021)
- [Diffusion Models for Time Series Forecasting: A Survey](https://mlsanity.com/paper/2507.14507/) (2025)
- [Design and Analysis of Robust Deep Learning Models for Stock Price Prediction](https://mlsanity.com/paper/2106.09664/) (2021)
- [Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Model](https://mlsanity.com/paper/2111.04976/) (2021)
- [Machine Learning in Finance-Emerging Trends and Challenges](https://mlsanity.com/paper/2110.11999/) (2021)
- [iTFKAN: Interpretable Time Series Forecasting with Kolmogorov-Arnold Network](https://mlsanity.com/paper/2504.16432/) (2025)
- [A Comparative Analysis of Portfolio Optimization Using Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian Stock Market](https://mlsanity.com/paper/2305.17523/) (2023)
- [Portfolio Optimization: A Comparative Study](https://mlsanity.com/paper/2307.05048/) (2023)
- [Exploring Sectoral Profitability in the Indian Stock Market Using Deep Learning](https://mlsanity.com/paper/2407.01572/) (2024)
- [Extreme value forecasting using relevance-based data augmentation with deep learning models](https://mlsanity.com/paper/2510.02407/) (2025)

## Abstract (arXiv)

Prediction of stock price and stock price movement patterns has always been a critical area of research. While the well-known efficient market hypothesis rules out any possibility of accurate prediction of stock prices, there are formal propositions in the literature demonstrating accurate modeling of the predictive systems that can enable us to predict stock prices with a very high level of accuracy. In this paper, we present a suite of deep learning-based regression models that yields a very high level of accuracy in stock price prediction. To build our predictive models, we use the historical stock price data of a well-known company listed in the National Stock Exchange (NSE) of India during the period December 31, 2012 to January 9, 2015. The stock prices are recorded at five minutes intervals of time during each working day in a week. Using these extremely granular stock price data, we build four convolutional neural network (CNN) and five long- and short-term memory (LSTM)-based deep learning models for accurate forecasting of the future stock prices. We provide detailed results on the forecasting accuracies of all our proposed models based on their execution time and their root mean square error (RMSE) values.