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

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

doi:10.1186/s40537-017-0092-5

Cited by 31 publications

(19 citation statements)

References 21 publications

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“…They have been used to improve data analysis and forecast. In general, when high-dimensional data exist, their fluctuations do not allow the intrinsic characteristics immersed in the data to be easily determined [19,20].…”

Section: Trend Analysis Of Datamentioning

confidence: 99%

“…The Hodrick-Prescott filter is a mathematical tool used in macroeconomics, specifically in the real business cycle theory, to eliminate a time series of a cyclical component. This algorithm provides an extracted trend considering variations of sequences, through which intervals of growth or decrease are determined by the stochastic trend obtained with a smoothing condition [20]. This economics research area is oriented to the determination of the time series trend, where the series is decomposed as presented in ( 1) [21].…”

Section: Trend Analysis Of Datamentioning

confidence: 99%

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Trend Analysis of Modal Identification based Real-time Power System Oscillations using L1 Trend Filtering

Oscullo

Cepeda²,

Gallardo

et al. 2021

IJRCS

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This paper is looking to show to use of system data collected from wide-area monitoring systems (WAMS). They allow monitoring of the dynamics of power systems. Among the WAMS applications, there is the modal identification algorithm, which identifies critical oscillatory modes from PMU measurements. This application permits using data processors for estimating of frequency, damping, and amplitude of dominant mode oscillations observable in a specific electric signal (e.g., active power, frequency) recorded for the analyzed period. However, since modal identification of real-time measurements is based on an online optimization, the results usually have considerable fluctuations. Thus, it is essential to consider the complementary implementation of trend analysis for acquiring convenient early-warning indicators of oscillatory problems. This consideration allows avoiding erroneous information of the systems oscillatory behavior of the system real-time that modal identification of crude results could deliver. In this paper, the application of a l1 filter for determining the trend analysis of high-dimensional data set resulted from a commercial modal identification is explored. The algorithm is applied to an oscillatory event registered by the WAMS of the Ecuadorian National Interconnected System with promising results.

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Section: Trend Analysis Of Datamentioning

confidence: 99%

Section: Trend Analysis Of Datamentioning

confidence: 99%

Trend Analysis of Modal Identification based Real-time Power System Oscillations using L1 Trend Filtering

Oscullo

Cepeda²,

Gallardo

et al. 2021

IJRCS

View full text Add to dashboard Cite

show abstract

“…Ouahilal et al [11] optimizing stock market price using novel hybrid model based on Hodrick-Prescott filter and support vector regression algorithm. The results show a very good accuracy of stock market price prediction with a very minimalist execution time.…”

Section: Literature Reviewmentioning

confidence: 99%

Indonesian Stock Price Prediction including Covid19 Era Using Decision Tree Regression

Hindrayani

Fahrudin

Aji

et al. 2020

2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

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Predicting stock prices is an interesting field in DataMining. There are many variables affecting stock prices. Especially in this covid19 era which impacts in economy, the stock prices become unpredictable. Telecommunications companies are observed in this research as it is one of the sectors that's still very much in demand in this pandemic situation. Fundamental data will be used to predict the Indonesian telecommunications stock price. Regression techniques will be used as the proposed model. The correlation coefficient shows that despite the covid19 era, fundamental data still play a role in stock market price. Decision Tree Regression produced competitive results compared to other methods.

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“…Meanwhile, in the Tourism sector, big data forecasting plays a significant role [11][12][13]. In summary, it is accurate to say that ample scientific articles use big data to make a prediction, in Politics to predict Indonesian presidential election's result [14], in Finance to predict capital market price [15], so on and so forth.…”

Section: Related Studiesmentioning

confidence: 99%

Cyber risk prediction through social media big data analytics and statistical machine learning

Subroto

Apriyana²

2019

J Big Data

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

Cited by 31 publications

References 21 publications

Trend Analysis of Modal Identification based Real-time Power System Oscillations using L1 Trend Filtering

Trend Analysis of Modal Identification based Real-time Power System Oscillations using L1 Trend Filtering

Indonesian Stock Price Prediction including Covid19 Era Using Decision Tree Regression

Cyber risk prediction through social media big data analytics and statistical machine learning

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