Analysis
The article describes a continuation of a tutorial on stock price prediction using Recurrent Neural Networks (RNNs). The focus is on enhancing the model from Part 1 to handle multiple stocks by incorporating stock symbol embedding vectors as input. This suggests an approach to improve the model's ability to differentiate patterns across different stock price sequences.
Key Takeaways
- •The tutorial builds upon a previous part, indicating a progressive learning approach.
- •The core concept is to use RNNs for stock price prediction.
- •Stock symbol embeddings are used to differentiate between multiple stocks.
- •The goal is to improve the model's ability to analyze different stock patterns.
Reference / Citation
View Original"In order to distinguish the patterns associated with different price sequences, I use the stock symbol embedding vectors as part of the input."