Stock Price Prediction: Comparison of Arima and Artificial Neural Network Methods - An Indonesia Stock's Case

Wijaya, Yohanes Budiman; Kom, S.; Napitupulu, Togar Alam

doi:10.1109/act.2010.45

Cited by 25 publications

(10 citation statements)

References 3 publications

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“…Empirical results obtained also showed that the ANN model is superior to the ARIMA model. Wijaya et al [20] did similar comparison based on the Indonesia stock exchange and got better accuracy with ANN than the ARIMA model. More literature has shown the prevalent use of ANNs as an effective tool for stock price prediction [10,[21][22][23][24][25][26][27][28][29].…”

Section: Related Workmentioning

confidence: 99%

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

Adebiyi

Adediran

Ayo

2014

Journal of Applied Mathematics

443

183

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This paper examines the forecasting performance of ARIMA and artificial neural networks model with published stock data obtained from New York Stock Exchange. The empirical results obtained reveal the superiority of neural networks model over ARIMA model. The findings further resolve and clarify contradictory opinions reported in literature over the superiority of neural networks and ARIMA model and vice versa.

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Section: Related Workmentioning

confidence: 99%

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

Adebiyi

Adediran

Ayo

2014

Journal of Applied Mathematics

443

183

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“…A similar comparison was approached in [28] in which the authors demonstrate that ANN can be engaged to improve financial time-series forecasting. The authors focused on applying ANN and ARIMA models to predict PT Aneka Tambang Tbk (ANTM) by using historical daily values.…”

Section: Comparisonsmentioning

confidence: 88%

Machine Learning for Quantitative Finance Applications: A Survey

et al. 2019

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The analysis of financial data represents a challenge that researchers had to deal with. The rethinking of the basis of financial markets has led to an urgent demand for developing innovative models to understand financial assets. In the past few decades, researchers have proposed several systems based on traditional approaches, such as autoregressive integrated moving average (ARIMA) and the exponential smoothing model, in order to devise an accurate data representation. Despite their efficacy, the existing works face some drawbacks due to poor performance when managing a large amount of data with intrinsic complexity, high dimensionality and casual dynamicity. Furthermore, these approaches are not suitable for understanding hidden relationships (dependencies) between data. This paper proposes a review of some of the most significant works providing an exhaustive overview of recent machine learning (ML) techniques in the field of quantitative finance showing that these methods outperform traditional approaches. Finally, the paper also presents comparative studies about the effectiveness of several ML-based systems.

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“…The ARIMA or Box-Jenkins method was first introduced in 1970s [22]. It has seen numerous applications in various areas for forecasting processes such as stock prices [23], electricity market [24], computer hardware resource [25,26], natural phenomena [27], etc., that yield time-series data. In Reference [26], ARIMA and long short-term memory (LSTM) recurrent network for forecasting performance of CPU (Central Processing Unit) usage of server machines in data centers are compared.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of ARIMA Models for Human–Machine Interface State Sequence Prediction

Singh

Mahmoud

2019

MAKE

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In this paper, auto-regressive integrated moving average (ARIMA) time-series data forecast models are evaluated to ascertain their feasibility in predicting human–machine interface (HMI) state transitions, which are modeled as multivariate time-series patterns. Human–machine interface states generally include changes in their visually displayed information brought about due to both process parameter changes and user actions. This approach has wide applications in industrial controls, such as nuclear power plant control rooms and transportation industry, such as aircraft cockpits, etc., to develop non-intrusive real-time monitoring solutions for human operator situational awareness and potentially predicting human-in-the-loop error trend precursors.

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Stock Price Prediction: Comparison of Arima and Artificial Neural Network Methods - An Indonesia Stock's Case

Cited by 25 publications

References 3 publications

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

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

Machine Learning for Quantitative Finance Applications: A Survey

Evaluation of ARIMA Models for Human–Machine Interface State Sequence Prediction

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