Stock price index forecasting using a multiscale modelling strategy based on frequency components analysis and intelligent optimization

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.…”

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“…Discussions on the applying of econometric methods to forecast prices of financial instruments lead to the conclusion that these methods, due to their characteristics and the level of market volatility, may not be suitable for market forecasting. However, some researchers state that econometric models may provide better results than other methods in the case of some non-linear time series (Li et al, 2022). For forecasting more complex non-linear financial time series, algorithms such as: support vector machine (SVR), eXtreme Gradient Boosting (XGBoost), or Multilayer Perceptron (MLP) are becoming increasingly popular (Oukhouya, El Himdi, 2023).…”

Comparing Machine Learning Methods—SVR, XGBoost, LSTM, and MLP— For Forecasting the Moroccan Stock Market

“…It concluded that Deep Learning (DL) was the most commonly utilized model for forecasting stock price trends [1,2]. Traditional econometric methods might require improved performance in relevant nonlinear time series and may not be appropriate for directly forecasting stock prices because of their volatility [3]. However, for complex nonlinear financial time series, methods such as Support Vector Regression (SVR), eXtreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM) can detect nonlinear relationships in the forecasting of stock prices [4] and achieve better fitting results by tuning multiple parameters [5].…”