Summary of A Stock Price Prediction Approach Based on Time Series Decomposition and Multi-scale Cnn Using Ohlct Images, by Zhiyuan Pei et al.
A Stock Price Prediction Approach Based on Time Series Decomposition and Multi-Scale CNN using OHLCT Images
by Zhiyuan Pei, Jianqi Yan, Jin Yan, Bailing Yang, Ziyuan Li, Lin Zhang, Xin Liu, Yang Zhang
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Statistical Finance (q-fin.ST)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed SMSFR-CNN model leverages image-based methods for stock prediction by fusing multi-scale features and sequence information. Building upon previous work, this paper develops a novel approach that captures complex visual patterns and spatial correlations in stock prices, offering improved interpretability compared to traditional time series models. The SMSFR-CNN architecture combines the strengths of convolutional neural networks (CNNs) with sequence-based modeling to predict stock price movements in the China A-share market. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to predict how stock prices will change using pictures and special computer models. Instead of just looking at numbers, this method looks at patterns in pictures too. This helps make predictions more reliable and easy to understand. The new model, SMSFR-CNN, works by combining lots of different pieces of information about the stock market. |
Keywords
» Artificial intelligence » Cnn » Time series
