Hybridization of intelligent techniques and ARIMA models for time series prediction

Valenzuela, Olga; Rojas, Ignacio; Rojas, Francisco Javier; Pomares, H.; Herrera, José Benavente; Guillén, Alberto; Marquez, L.; Pasadas, M.

doi:10.1016/j.fss.2007.11.003

Cited by 148 publications

(64 citation statements)

References 64 publications

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“…Some studies have focused on the regression models [3], [11], [12]. While others proposed methods based on the time series models such as the ARIMA (Autoregressive Moving Average) model [13]- [16] and the GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model [17]- [19]. The probabilistic models such as Bayesian [1] and other methods [20] have also been explored by some studies.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach for Optimization of Systematic Signal Detection in Financial Trading Systems with Big Data

Karaoglu¹,

Arpaci²

2017

ijisae

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Expert systems for trading signal detection have received considerable attention in recent years. In financial trading systems, investors' main concern is determining the best time to buy or sell a stock. The trading decisions are often influenced by the emotions and feelings of the investors. Therefore, investors and researchers have aimed to develop systematic models to reduce the impact of emotions on trading decisions. Nevertheless, the use of algorithmic systems face another problem called "lack of dynamism". Due to dynamic nature of financial markets, trading robots should quickly learn and adapt as human traders. Recently, a solution for detecting trading signals based on a dynamic threshold selection was proposed. In this study, we extend this approach by adopting several different rule based systems and enhancing it by using the Recurrent Neural Network algorithm. Recurrent Neural Networks learn the connection weights of subsystems with arbitrary sequences of inputs that make them a great fit for time series data. Our model is based on Piecewise Linear Representation and Recurrent Neural Network with the goal of detecting potential excessive movements in noisy stream of time series data. We use an exponential smoothing technique to detect abnormalities. Trading signals are produced using fixed time interval data from Istanbul Stock Exchange. The evaluations indicated that our model produces successful results in trading data. Future work will focus on further improvements and scalability of the model.

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Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach for Optimization of Systematic Signal Detection in Financial Trading Systems with Big Data

Karaoglu¹,

Arpaci²

2017

ijisae

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“…3,4 Various intelligent models, such as artificial neural networks (ANNs), 5,6 particle swarm optimization (PSO), [7][8][9][10] genetic algorithms, 11 and fuzzy logic, [12][13] have been proposed. In the forecasting field, particularly for air pollution problems, 6,7,11,[14][15][16][17][18] ANNs have been successfully applied.…”

Section: Introductionmentioning

confidence: 99%

Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

Filho¹,

Madeiro²,

Fernandes³

et al. 2013

Quím. Nova

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Recebido em 1/8/12; aceito em 8/2/13; publicado na web em 4/6/13This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.

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“…A hybrid of ARIMA and support vector machines was successfully presented by Pai et.al [8] for predicting stock prices problems. Other outstanding hybrid approaches could be found in references [9][10][11]. Most of these hybrid models were implemented as a following process: first, the model-based technique was used to predict the linear relation, then the data-driven based technique was utilized to forecast the residuals between actual values and predicted results obtained from previous step.…”

Section: Introductionmentioning

confidence: 99%

A hybrid of nonlinear autoregressive model with exogenous input and autoregressive moving average model for long-term machine state forecasting

Pham

Tran

Yang

2010

Expert Systems with Applications

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This paper presents an improvement of hybrid of nonlinear autoregressive with exogenous input (NARX) and autoregressive moving average (ARMA) for long-term machine state forecasting based on vibration data. In this study, vibration data is considered as a combination of two components which are deterministic data and error. The deterministic component may describe the degradation index of machine, whilst the error component can depict the appearance of uncertain parts. An improved hybrid forecasting model, namely NARX-ARMA model, is carried out to obtain the forecasting results in which NARX network model which is suitable for nonlinear issue is used to forecast the deterministic component and ARMA model are used to predict the error component due to appropriate capability in linear prediction. The final forecasting results are the sum of the results obtained from these single models. The performance of the NARX-ARMA model is then evaluated by using the data of low methane compressor acquired from condition monitoring routine. In order to corroborate the advances of the proposed method, a comparative study of the forecasting results obtained from NARX-ARMA model and traditional models is also carried out. The comparative results show that NARX-ARMA model is outstanding and could be used as a potential tool to machine state forecasting.

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Hybridization of intelligent techniques and ARIMA models for time series prediction

Cited by 148 publications

References 64 publications

A Deep Learning Approach for Optimization of Systematic Signal Detection in Financial Trading Systems with Big Data

A Deep Learning Approach for Optimization of Systematic Signal Detection in Financial Trading Systems with Big Data

Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

A hybrid of nonlinear autoregressive model with exogenous input and autoregressive moving average model for long-term machine state forecasting

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