# Enhancing Bitcoin Price Fluctuation Prediction Using Attentive LSTM and Embedding Network
## 5 Experiments
### Further Analyses
#### 5.6.2 Study of RQ2  
We further discuss the effect of hyper-parameters on our proposed problem. We first investigate the impact of the fluctuation percentage of Bitcoin price \(\eta\). We vary its value in the range of \([1.3\%,5\%]\) while fixing the other parameters. As shown in Figure 4(a), the result indicates that too small or too large \(\eta\) cannot achieve the best result. Too small \(\eta\) will make our problem close to traditional price movement prediction problem and cannot achieve a better result. Too large \(\eta\) will make the selected samples too small and also cannot achieve the compelling result, as shown in Figure 4(b).  
Then, we investigate the impact of \(\alpha\). The parameter \(\alpha\) is used to balance the weight of similarity and a nonlinear regression in embedding network. We vary its value within \([0,1]\) while fixing the other parameters. The result is demonstrated in Figure 5(a), indicating that too small or too large \(\alpha\) cannot achieve a better result. It also evidences the effectiveness of the combination of similarity and a nonlinear regression that measure the importance of related cryptocurrencies.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Problem** & **Accuracy \%** & **Precision \%** & **Recall \%** & **F1 \%** \\ \hline SMP1 & 52.34 & 52.16 & 52.31 & 52.23 \\ SMP & 54.34 & 54.44 & 54.22 & 54.33 \\ BFD & 61.15 & 61.10 & 60.93 & 61.01 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of different problems.  
We next investigate the impact of \(N_{\text{max}}\) and \(N^{\prime}_{\text{max}}\), where \(N_{\text{max}}\) represents our strictness of selecting fluctuant time series. Results are shown in Figure 5(b). First, we vary the value of \(N_{\text{max}}\) within \([1,10]\) and fix the other parameters. In particular, we set \(N^{\prime}_{\text{max}}=3\). Results are shown in Figure 6(a). It can be seen that F1 score achieves the best when \(N_{\text{max}}=5\). Too large or too small values of \(N_{\text{max}}\) cannot achieve a better performance. Moreover, the change of F1 score is small with the change of \(N_{\text{max}}\). Second, we vary the value of \(N^{\prime}_{\text{max}}\) within \([1,10]\) and fix the other parameters. In particular, we set \(N_{\text{max}}=5\). Results are shown in Figure 6(b). It can be seen that F1 score achieves the best when \(N^{\prime}_{\text{max}}=3\). Besides, the value of \(N^{\prime}_{\text{max}}\) performs more sensitively when comparing Figure 6(a) with Figure 6(b). The main reason is that the larger value of \(N^{\prime}_{\text{max}}\) means more noise introduced.  
Finally, we investigate the parameter \(\tau\), which is the length of intervals. Here, we adopt six different intervals (1 min, 5 min, 15 min, 30 min, 1 h, 4 h) to evaluate our model ALEN. All the other  
Figure 5: The impact of \(\eta\).  
parameters are tuned to the best. The results are shown in Table 4. It demonstrates that the 15 min interval is better than the others and the best \(\eta\) is 2.2%. In particular, the best \(\eta\) at 15 min, 30 min, 1 h and 4 h is not much different.