Financial sequence prediction based on swarm intelligence algorithms and internet of things

A financial time series data mining method with different time granularity based on trend Division

“…The visualization results are shown in Figure 6. The extreme points extracted are: maximum points: [30, 13,6,13,15,12,20,9]; Minimum point: [1,7,2, -15,8,8,0,3]. The results are consistent with the expected results, which shows that the proposed method is effective.…”

“…The methods of stock forecasting mainly focus on deep learning and fusion models. Deng, C. R., et al Developed a hybrid stock price index prediction modeling framework using long-term and short-term memory (LSTM) and multivariate empirical mode decomposition (MEMD), which can capture the intrinsic characteristics of the complex dynamics of the stock price index time series [13]; Gao, R. Z., et al Proposed a deep learning method combined with genetic algorithm to predict the target stock market index [14]; Gao, Z., et al Proposed a prediction algorithm integrating multiple support vector regression (SVR) models, and used reasonable weight to combine the prediction results of multiple models to improve the accuracy of the model [15]; Gupta, U., et al In order to overcome the problem of overfitting, a new data enhancement method was proposed in the StockNet model based on Gru [16]; He, Q. Q., et al proposed a new case-based deep transfer learning model with attention mechanism [17]; Kanwal, A., et al Proposed a prediction model based on hybrid deep learning (DL), which combines deep neural network, short-term memory and one-dimensional convolutional neural network (CNN) [18]; Kumar, R., et al Proposed a three-stage fusion model to process time series data and improve the accuracy of stock market prediction [19]; Li, R. R., et al Proposed a multi-scale modeling strategy based on machine learning methods and econometric models [20].…”

“…In order to verify the feasibility of the above algorithm, a group of time series data is randomly selected to extract the extreme points. Taking the time series of [1,6,30,20,15,7,13,9, 2, 6, 0, -7, -15, -6, 4, 13, 8, 15, 8, 12, 3,0, 20, 7, 3, 9] as an example, using the method proposed in this paper, the step size is 1, and the extreme points are identified. The visualization results are shown in Figure 6.…”