ResNLS: An Improved Model for Stock Price Forecasting
Quick summary
arXiv:2312.01020v3 Announce Type: cross Abstract: Stock prices forecasting has always been a challenging task. Although many research projects try to address the problem, few of them pay attention to the varying degrees of dependencies between stock prices. In this paper, we introduce a hybrid model that improves the prediction of stock prices by emphasizing the dependencies between adjacent stock prices. The proposed model, ResNLS, is mainly composed of two neural architectures, ResNet and LSTM. ResNet serves as a feature extractor to identify dependencies between stock prices, while LSTM ana
Key takeaways
- arXiv:2312.01020v3 Announce Type: cross Abstract: Stock prices forecasting has always been a challenging task.
- Although many research projects try to address the problem, few of them pay attention to the varying degrees of dependencies between stock prices.
- In this paper, we introduce a hybrid model that improves the prediction of stock prices by emphasizing the dependencies between adjacent stock prices.
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
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