Frequency-Division Combination Forecasting of Stock Market Based on Wavelet Multiresolution Analysis

Si, Luo; Huo, Jiangyou; Dai, Zian

doi:10.1155/2018/1259156

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Wavelet analysis is particularly attractive in view of epidemiological and environmental time-series and the relationships between them (9). The application of wavelet analysis has advantages in analyzing the underlying relevance in both time and frequency domain (26). WPS estimates the spectrum as a function of time and could reveal the timeseries change of different periodic components over time (27), therefore, WPS was applied to study the outbreak cycles of HFRS in mainland China.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Hemorrhagic Fever With Renal Syndrome Using Wavelet Tools in Mainland China, 2004–2019

Zou

Sun

2020

Front. Public Health

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Introduction : Hemorrhagic fever with renal syndrome (HFRS) is a life-threatening public health problem in China, accounting for ~90% of HFRS cases reported globally. Accurate analysis and prediction of the HFRS epidemic could help to establish effective preventive measures.Materials and Methods : In this study, the geographical information system (GIS) explored the spatiotemporal features of HFRS, the wavelet power spectrum (WPS) unfolded the cyclical fluctuation of HFRS, and the wavelet neural network (WNN) model predicted the trends of HFRS outbreaks in mainland China.Results : A total of 209,209 HFRS cases were reported in mainland China from 2004 to 2019, with the annual incidence ranged from 0 to 13.05 per 100,0000 persons at the province level. The WPS proved that the periodicity of HFRS could be half a year, 1 year, and roughly 7-year at different time intervals. The WNN structure of 12-6-1 was set up as the fittest forecasting model for the HFRS epidemic.Conclusions : This study provided several potential support tools for the control and risk-management of HFRS in China.

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Section: Discussionmentioning

confidence: 99%

Analysis of Hemorrhagic Fever With Renal Syndrome Using Wavelet Tools in Mainland China, 2004–2019

Zou

Sun

2020

Front. Public Health

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“…In [27], the authors proposed a hybrid extreme gradient boosting framework and auto regressive integrated moving average model to predict stock price. In [28], a combined predicting method based on wavelet multiresolution analysis was proposed to predict the stock market more accurately and concisely. In [29], a prediction method of stock price volatility based on time series analysis technology was proposed.…”

Section: Related Workmentioning

confidence: 99%

Research on HMM-Based Efficient Stock Price Prediction

2022

Mobile Information Systems

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Stock market is one of the most important parts of the investment market. Compared with other industries, the stock market not only has a higher rate of return on investment but also has a higher risk, and stock price prediction has always been a close concern of investors. Therefore, the research on stock price prediction methods and how to reduce the error of stock price prediction has become a hot topic for many scholars at home and abroad. In recent years, the development of computer technology such as machine learning and econometric method makes the stock price prediction more reliable. Due to the hidden Markov nature of stock price, this paper proposes a stock price prediction method based on hidden Markov model (HMM). To be specific, since the data of stock price have continuity in time series, it is necessary to extend the discrete HMM to the continuous HMM, and then put forward the up and down trend prediction model based on the continuous HMM. The first-order continuous HMM is extended to the second-order continuous HMM, and the stock price is predicted by combining the prediction method of fluctuation range. As a result, the proposed second-order continuous HMM-based stock price prediction model is simulated on Hang Seng Index (HSI), one of the earliest stock market indexes in Hong Kong. The evaluation results on six months HSI show that the predicted value of the proposed model is very close to the actual value and outperforms three benchmarks in terms of RMSE, MAE, and R2.

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Stock price series prediction optimization framework based on function information stacking and model averaging

Meng¹,

Shi²

2022

BCPBM

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Stock price series prediction has always been a hot issue in the field of quantitative finance. The commonly used models include ARIMA, GARCH, LSTM neural network and BP neural network. Aiming at these models, this paper proposes an optimization framework based on function information stacking and model averaging. The proposed method uses intra-day price information as auxiliary information and extracts functional features based on functional principal component analysis (PCA). Considering that the underlying model structure between the characteristic variables and the residual series obtained from the original time series prediction model is unknown, this paper uses Stacking method to enhance the data of the characteristic variables to reduce the impact of noise information on the prediction model. In addition, to solve the parameter optimization problem of the original model, this paper proposes a model averaging method using distance covariance weighting to deal with it. In the actual data analysis, this paper takes the LSTM neural network as an example to explore the effectiveness and robustness of the proposed method, and the results show that the proposed method has certain competitiveness. Finally, the proposed optimization method can be used to improve other time series prediction models.

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Frequency-Division Combination Forecasting of Stock Market Based on Wavelet Multiresolution Analysis

Cited by 3 publications

References 5 publications

Analysis of Hemorrhagic Fever With Renal Syndrome Using Wavelet Tools in Mainland China, 2004–2019

Analysis of Hemorrhagic Fever With Renal Syndrome Using Wavelet Tools in Mainland China, 2004–2019

Research on HMM-Based Efficient Stock Price Prediction

Stock price series prediction optimization framework based on function information stacking and model averaging

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