As long as there are enough of these transformations, it can learn very complex functions [13]. [PERSON] et al. applied a Convolutional NeuralNetwork (CNN) to stock price prediction [14]. The results show that the prediction effect of this method is better than that of BP and SVM. [PERSON] et al. pointed out that the structure of a Recurrent Neural Network (RNN) is simpler than that of a traditional artificial neural network [15], and it is more suitable for financial TSF prediction. An LSTM neural network is a form of RNN which can effectively deal with the problem of long-term dependence of sequences. [PERSON] and [PERSON]'s experiment showed that LSTM neural networks can extract important information from financial TSF data with a lot of noise [16]. Compared with previous generations of ANNs, SNNs have been the subject of much less research on TSF problems. The training and debugging process of SNNs is relatively complex. Training SNNs is inherently more complex than for traditional neural networks due to their reliance on temporal dynamics and signal accumulation mechanisms. In addition, due to the high biological rationality of SNNs and their relatively complex mathematical models, the debugging process may also be more difficult. Despite the difficulty, there has been progress. [PERSON] compared the performance of traditional neural networks and SNNs for financial TSF [17]. He found that there is a lack of information about encoding methods, learning methods, network structures, and neural models for SNNs, which hinders comparison, reproducibility, and replication. [PERSON] used SNNs in financial time series prediction, and he applied a Polychronous Spiking Network (PSN) to exploit the temporal characteristics of the spiking neural model in an appropriate way [18]. [PERSON] proposed predictive and explainable modeling of stock price time series, integrated with online news, as a method, based on SNNs, for an integrated predictive modeling of multi-modal time series [19]. However, the SNN is still a relatively new and developing field, with some limitations in its use. From the traditional neural networks, through deep neural networks, to the recently emerging LSTM, they all do a good job in stock price forecasting. The SNN is rarely used in predictions compared to the previous generation. One reason is that there are so many hyperparameters in SNNs, so it is too hard to optimize them for making the best network to fit the specific problems. In most cases, these hyperparameters are manually adjusted by researchers, but this method has obvious drawbacks:  
1. Time is often wasted in the intervals between each experiment run. If a trial run ends, researchers cannot guarantee a timely start of the next one. 2. Each experiment requires a considerable amount of effort, and researchers need to analyze the results and determine the direction of the next parameter adjustment. 3. Researchers usually tend to choose aesthetically pleasing numbers as parameters, such as 200, 1150, or 0.55. This approach increases the likelihood of missing the optimal numbers, even though they may not look as appealing. Therefore, biomimetic algorithms are good choices to find better hyperparameters, which can be inspired by the corresponding characteristics of biological systems, providing new design ideas and principles for engineering technology. The CS algorithm is a typical representation of them. But the combination of the original CS and SNNs is not satisfactory enough. Therefore, we proposed the ICS algorithm. The work described in this article about ICS-SNN is summarized as follows:  
1. Change the initialization method of the cuckoo search algorithm and replace the discovery probability with a fixed value with a variable that increases with the number of iterations;
2. Choose the proper hyperparameters for the SNN model and use the ICS to optimize them;
3. Compare the performance of ICS-SNN, CS-SNN, and other models by using futures market data.