## 5 Discussion  
In this paper, we propose a hybrid neural network based on multiple time scale feature learning for stock market index trend prediction. Because there are multi-scale features in financial time series, it makes sense to combine them to predict future trends. First, the proposed model only utilizes one CNN to extract multiple time scale features, instead of using multiple networks like other models. It simplifies the model and makes more accurate predictions. Second, time dependences in the multiple time scale features are learned by three LSTMs. Finally, the information learned by LSTMs is fused through fully connected layers to predict the price trend.  
The experimental results demonstrate that such a hybrid network can indeed enhance the predictive performance compared with benchmark networks. Firstly, by comparing with the models based on \(F_{1}\), \(F_{2}\) and \(F_{3}\), we conclude that combining multiple time scale features can promote accurate prediction. Secondly, in comparison with the Simplistic Model, we found that the proposed model can learn valuable information. SVM, CNN, and LSTM all learn the features in price series from a single scale. However, there are multiple scales in financial time series, which makes these methods sometimes unable to perform accurate predictions. Finally, both the Multiple Pipeline Model and MFNN are models based on multi-scale feature learning, but they use several branches or networks to extract multi-scale features, making the network huge and complex. The hybrid neural network we proposed uses only a CNN to extract multi-scale features, simplifying the model and predicting more accurately.  
However, we can find that the proposed model cannot accurately predict trends for some data samples. There may be two reasons for this issue. First, the three time scales features are not enough to reflect the internal law of price series. Second, these samples are seriously affected by other factors that  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Forecast Horizon** & **Model** & **Accuracy (\%)** \\ \hline \multirow{8}{*}{One week} & Simplistic Model & 54.82 \\  & SVM & 61.98 \\  & LSTM & 65.05 \\  & CNN & 59.34 \\  & Multiple Pipeline Model & 63.30 \\  & NFNN & 65.93 \\  & The proposed model & **66.59** \\ \hline \multirow{8}{*}{One month} & Simplistic Model & 56.92 \\  & SVM & 70.91 \\  & LSTM & 71.59 \\ \cline{1-1}  & CNN & 67.95 \\ \cline{1-1}  & Multiple Pipeline Model & 72.05 \\ \cline{1-1}  & MFNN & 72.27 \\ \cline{1-1}  & The proposed model & **74.55** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons with existing models in accuracy.  
we have not considered, such as political policy, industrial development, natural factors, and so on. It provides directions for our future work. We can consider using a multi-layer CNN to extract more scale features for prediction. At the same time, we can extract useful information from more sources, such as macroeconomic indicators, news, market sentiment and so on.  
[PERSON] proposed the basic framework of hybrid neural network and completed the model construction, experimental research, and thesis writing. Throughout the process, [PERSON] guided and gave a lot of suggestions. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.