Optimizing stock market price prediction using a hybrid approach based on HP filter and support vector regression

Ouahilal, Meryem; Mohajir, Mohammed El; Chahhou, Mohamed; Mohajir, Badr Eddine El

doi:10.1109/cist.2016.7805059

Cited by 13 publications

(8 citation statements)

References 3 publications

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“…To assess the performance of the overall three algorithms, several experiments have been conducted using real world financial time series. According to our comparative study we could clearly conclude that the support vector regression is the best prediction algorithm that can be execute to provide the financial time series forecasting [24,28]. The architecture of our proposed framework is shown in Fig.…”

Section: Methodsmentioning

confidence: 83%

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A novel hybrid model based on Hodrick–Prescott filter and support vector regression algorithm for optimizing stock market price prediction

et al. 2017

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Section: Methodsmentioning

confidence: 83%

“…Therefore, we propose a new Framework of financial time series prediction based on our hybrid approach [28].…”

Section: Methodsmentioning

confidence: 99%

A novel hybrid model based on Hodrick–Prescott filter and support vector regression algorithm for optimizing stock market price prediction

et al. 2017

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“…HP filtering, proposed by Hodrick and Prescott in 1981, is widely used in economic analysis, but can be generally applied on data containing fluctuations, to extract trend components [18,19]. Ouahilal et al [20] used HP filter in the stock price forecasting field and proposed an approach combining Support Vector Regression with HP filter. e experimental results confirm that the proposed model is more powerful in terms of predicting stock prices.…”

Section: Degradation Trend Extraction From Monitoring Datamentioning

confidence: 99%

“…For a specific set of b and σ B , given that the two partial derivatives of equations (20) and (21) equal zero, the maximum likelihood estimates for μ λ and σ λ can be calculated as…”

Section: Overall Model Parameter Estimationmentioning

confidence: 99%

Centralized Maintenance Time Prediction Algorithm for Freight Train Wheels Based on Remaining Useful Life Prediction

Shi

Yang

2020

Mathematical Problems in Engineering

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Many freight trains for special lines have in common the characteristics of a fixed group. Centralized Condition-Based Maintenance (CCBM) of key components, on the same freight train, can reduce maintenance costs and enhance transportation efficiency. To this end, an optimization algorithm based on the nonlinear Wiener process is proposed, for the prediction of the train wheels Remaining Useful Life (RUL) and the centralized maintenance timing. First, Hodrick–Prescott (HP) filtering algorithm is employed to process the raw monitoring data of wheel tread wear, extracting its trend components. Then, a nonlinear Wiener process model is constructed. Model parameters are calculated with a maximum likelihood estimation and the general deterioration parameters of wheel tread wear are obtained. Then, the updating algorithm for the drift coefficient is deduced using Bayesian formula. The online updating of the model is realized, based on individual wheel monitoring data, while a probability density function of individual wheel RUL is obtained. A prediction method of RUL for centralized maintenance is proposed, based on two set thresholds: “maintenance limit” and “the ratio of limit-arriving.” Meanwhile, a CCBM timing prediction algorithm is proposed, based on the expectation distribution of individual wheel RUL. Finally, the model is validated using a 500-day online monitoring data on a fixed group, consisting of 54 freight train cars. The validation result shows that the model can predict the wheels RUL of the train for CCBM. The proposed method can be used to predict the maintenance timing when there is a large number of components under the same working conditions and following the same path of degradation.

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“…In [ 9 ], a framework is proposed to apply SVR strategy to predict the stock price. In [ 10 ], SVR and Hodrick–Prescott filter are combined together in order to get better result in prediction of the stock price. Hou et al [ 11 ] proposed a new method combining SVR and Grey Relation Analysis for short trend stock market prediction which takes advantage of SVR's ability in analyzing small-size and multidimensional samples.…”

Section: Introductionmentioning

confidence: 99%

RLSTM: A New Framework of Stock Prediction by Using Random Noise for Overfitting Prevention

Zheng

Zhou

Chen

2021

Computational Intelligence and Neuroscience

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An accurate prediction of stock market index is important for investors to reduce financial risk. Although quite a number of deep learning methods have been developed for the stock prediction, some fundamental problems, such as weak generalization ability and overfitting in training, need to be solved. In this paper, a new deep learning model named Random Long Short-Term Memory (RLSTM) is proposed to get a better predicting result. RLSTM includes prediction module, prevention module, and three full connection layers. Input of the prediction module is a stock or an index which needs to be predicted. That of the prevention module is a random number series. With the index of Shanghai Securities Composite Index (SSEC) and Standard & Poor’s 500 (S&P500), simulations show that the proposed RLSTM can mitigate the overfitting and outperform others in accuracy of prediction.

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Optimizing stock market price prediction using a hybrid approach based on HP filter and support vector regression

Cited by 13 publications

References 3 publications

A novel hybrid model based on Hodrick–Prescott filter and support vector regression algorithm for optimizing stock market price prediction

A novel hybrid model based on Hodrick–Prescott filter and support vector regression algorithm for optimizing stock market price prediction

Centralized Maintenance Time Prediction Algorithm for Freight Train Wheels Based on Remaining Useful Life Prediction

RLSTM: A New Framework of Stock Prediction by Using Random Noise for Overfitting Prevention

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