Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction

Adebiyi, Ayodele Ariyo; Adediran, Adewumi; Ayo, C. K.

doi:10.1155/2014/614342

Cited by 440 publications

(234 citation statements)

References 22 publications

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“…In fact, even if there are a large sample data, but it does not necessarily find rule, even if there is a statistical rule, but it is also not a typical. The other is the artificial intelligence technique such as artificial neural network (ANN) [3][4][5], genetic algorithm (GA) [6,7], and many hybrid intelligent algorithms [8][9][10][11]. The hybrid intelligent algorithms have more flexibility to solve the complex models, so more and more researchers tend to use them to deal with forecasting problems.…”

Section: Introductionmentioning

confidence: 99%

An Improved Hybrid Algorithm Based on PSO and BP for Stock Price Forecasting

Gao¹

2015

TOCSJ

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Stock price prediction is the main concern for financial firms and private investors. In this paper, we proposed a hybrid BP neural network combining adaptive PSO algorithm (HBP-PSO) to predict the stock price. HBP-PSO takes full use of the global searching capability of PSO and the local searching advantages of BP Neural Network. The PSO algorithm is applied for training the connection weights and thresholds of BP, in order to take advantage of BP, each particle in PSO swarm will be optimized by error correcting method of BP in probability. The trained BP neural network is used to predict the stock price. The empirical analysis using the real data of Chinese stock market demonstrates the feasibility and effectiveness of this method.

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Section: Introductionmentioning

confidence: 99%

An Improved Hybrid Algorithm Based on PSO and BP for Stock Price Forecasting

Gao¹

2015

TOCSJ

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“…The ARIMA(A1), plays a role of a reference model being an implementation of the simple bandwidth predictor specified in (5). For the estimation of ARIMA parameters, we used the routines implemented in the arima library, which is a part of the standard CRAN R distribution [21].…”

Section: Modelmentioning

confidence: 99%

“…measuring the time needed to download a chunk of video, or from a model of network traffic. In this case, the estimator returns the output of the models detailed in Table 3, taking into account that the output of ARIMA(A1) is just an averaged measurement of network traffic intensity in several previous periods (5).…”

Section: Models Applicationmentioning

confidence: 99%

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Analysis and modelling of traffic produced by adaptive HTTP-based video

Biernacki

2016

Multimed Tools Appl

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The increase of HTTP-based video popularity causes that broadband and Internet service providers' links transmit mainly multimedia content. Network planning, traffic engineering or congestion control requires an understanding of the statistical properties of network traffic; therefore, it is desirable to investigate the characteristic of traffic traces generated by systems which employ adaptive bit-rate streaming. Our first contribution is an investigation of traffic originating from 120 client-server pairs, situated in an emulated content distribution network, and multiplexed onto a single network link. We show that the structure of the traffic is distinct from the structure generated by the first and second generation of HTTP video systems, and furthermore, not similar to the structure of general Internet traffic. The obtained traffic exhibits negative and positive correlations, anti-persistence, and its distribution function is skewed to the right. Our second contribution is an approximation of the traffic by ARIMA/FARIMA processes blue and artificial neural networks. As we show, the obtained traffic models are able to enhance the performance of an adaptive streaming algorithm.

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“…Results obtained revealed that the ARIMA model has a strong potential for short-term prediction. The forecasting performance of ARIMA and artificial neural networks model [5] has been examined with New York Stock Exchange data. The empirical results obtained prove the superiority of neural networks model over ARIMA model.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Stock Market using C-means Clustering and Particle Filter

Darwish¹,

Hilal²

2017

IJCA

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In this article, Particle Filter and C-means are used to predict a value of a point in a time series. Similar data in a time-series are grouped using C-means algorithm. Afterward, a number of particle filters are used as sub-predictors. These sub-predictors start from different points, which are the centers of clusters resulted from clustering algorithm. Outputs from all filters were used to obtain Final prediction result. A weighted average method is used to aggregate the outputs of the filters. Particle filters are used in here to model non-Gaussian time series. Benchmark datasets were used to evaluate the proposed algorithm. To measure its prediction performance, the results derived from the proposed model were compared with those of other algorithms. The comparison proved the effectiveness and accuracy of the proposed method.

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Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction

Cited by 440 publications

References 22 publications

An Improved Hybrid Algorithm Based on PSO and BP for Stock Price Forecasting

An Improved Hybrid Algorithm Based on PSO and BP for Stock Price Forecasting

Analysis and modelling of traffic produced by adaptive HTTP-based video

Prediction of Stock Market using C-means Clustering and Particle Filter

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