Summary of Off-the-shelf Neural Network Architectures For Forex Time Series Prediction Come at a Cost, by Theodoros Zafeiriou et al.
Off-the-Shelf Neural Network Architectures for Forex Time Series Prediction come at a Cost
by Theodoros Zafeiriou, Dimitris Kalles
First submitted to arxiv on: 17 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Mathematical Finance (q-fin.MF)
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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 study compares the performance and resource requirements of different Long Short-Term Memory (LSTM) neural network architectures with an ANN specialized architecture for forecasting the forex market. The analysis examines execution time, memory consumption, and computational power usage. The goal is to show that the specialized architecture not only yields better results but also uses fewer resources and takes less time compared to LSTM architectures. This comparison provides valuable insights into the suitability of these two types of architectures for time series prediction in the forex market. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study compares different neural network models to see which one works best for predicting the forex market. The researchers tested how fast each model was, how much memory it used, and how much computer power it needed. They found that a special kind of architecture did better than others at predicting the forex market and used fewer resources while doing so. This helps us understand which type of model is best to use for this task. |
Keywords
» Artificial intelligence » Lstm » Neural network » Time series
