Summary of Forecasting Foreign Exchange Market Prices Using Technical Indicators with Deep Learning and Attention Mechanism, by Sahabeh Saadati et al.
Forecasting Foreign Exchange Market Prices Using Technical Indicators with Deep Learning and Attention Mechanism
by Sahabeh Saadati, Mohammad Manthouri
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper proposes a novel approach for accurately predicting price behavior in the foreign exchange market by leveraging technical indicators and deep neural networks. The architecture combines Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) with an attention mechanism to capture long-term dependencies, local patterns, and temporal relationships. Initially, trend and oscillation technical indicators extract statistical features from Forex currency pair data, providing insights into price trends, market volatility, and overbought and oversold conditions. The LSTM network captures long-term dependencies, while the CNN network extracts local patterns. The outputs are fed into an attention mechanism that learns to weigh the importance of each feature and temporal dependency, generating a context-aware representation. The attention-weighted output is used to predict future price movements, outperforming benchmark models on multiple Forex currency pairs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computers called deep neural networks to try to predict what will happen with the value of different currencies. It looks at old data about how these currencies have changed in the past and tries to figure out patterns that can help it make good predictions. The computer breaks down this data into smaller parts, like looking at short-term trends or long-term patterns, and then combines all this information to make a prediction. It’s kind of like trying to guess what will happen with a stock market by looking at past prices and patterns. |
Keywords
» Artificial intelligence » Attention » Cnn » Lstm » Neural network
