A hybrid statistical approach for stock market forecasting based on Artificial Neural Network and ARIMA time series models

Ratnayaka, R. M. Kapila Taranga; Seneviratne, Damitha; Jiang, Wei; Arumawadu, Hasitha Indika

doi:10.1109/besc.2015.7365958

Cited by 21 publications

(19 citation statements)

References 23 publications

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“…If the MAPE of the proposed model is less than the benchmark models, then the accuracy of the proposed model is better [42]. The benchmark models that were used for the performance comparison were SWGARCH, GARCH, ARIMA-GARCH, and EWMA.…”

Section: Validation and Evaluation Of The Proposed Modelmentioning

confidence: 99%

A Novel Fuzzy Linear Regression Sliding Window GARCH Model for Time-Series Forecasting

et al. 2020

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Generalized autoregressive conditional heteroskedasticity (GARCH) is one of the most popular models for time-series forecasting. The GARCH model uses a maximum likelihood method for parameter estimation. For the likelihood method to work, there should be a known and specific distribution. However, due to uncertainties in time-series data, a specific distribution is indeterminable. The GARCH model is also unable to capture the influence of each variance in the observation because the calculation of the long-run average variance only considers the series in its entirety, hence the information on different effects of the variances in each observation is disregarded. Therefore, in this study, a novel forecasting model dubbed a fuzzy linear regression sliding window GARCH (FLR-FSWGARCH) model was proposed; a fuzzy linear regression was combined in GARCH to estimate parameters and a fuzzy sliding window variance was developed to estimate the weight of a forecast. The proposed model promotes consistency and symmetry in the parameter estimation and forecasting, which in turn increases the accuracy of forecasts. Two datasets were used for evaluation purposes and the result of the proposed model produced forecasts that were almost similar to the actual data and outperformed existing models. The proposed model was significantly fitted and reliable for time-series forecasting.

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Section: Validation and Evaluation Of The Proposed Modelmentioning

confidence: 99%

A Novel Fuzzy Linear Regression Sliding Window GARCH Model for Time-Series Forecasting

et al. 2020

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“…Also (NAVEENA, 2017) concluded that the hybrid method which combines linear and non-linear models can be an effective way to improve forecasting performance. (RATHNAYAKA, 2015) suggested that the hybrid model is more significant and gives the best solution for predicting future predictions under the high volatility fluctuations than traditional forecasting approaches.…”

Section: Independent Journal Of Management and Production (Ijmandp)mentioning

confidence: 99%

Time series forecasting of styrene price using a hybrid ARIMA and neural network model

Ghahnavieh¹

2019

Ind. Jour. Manag. & Prod.

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Every player in the market has a greater need to know about the smallest change in the market. Therefore, the ability to see what is ahead is a valuable advantage. The purpose of this research is to make an attempt to understand the behavioral patterns and try to find a new hybrid forecasting approach based on ARIMA-ANN for estimating styrene price. The time series analysis and forecasting is an essential tool which could be widely useful for finding the significant characteristics for making future decisions. In this study ARIMA, ANN and Hybrid ARIMA-ANN models were applied to evaluate the previous behavior of a time series data, in order to make interpretations about its future behavior for styrene price. Experimental results with real data sets show that the combined model can be most suitable to improve forecasting accurateness rather than traditional time series forecasting methodologies. As a subset of the literature, the small number of studies have been done to realize the new forecasting methods for forecasting styrene price.

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“…Some believe that hybridization methods [26][27][28][29] may improve the individual strengths as well as remove the weaknesses of each technique. The hybrid approach combines two forecasting approaches, such as Wavelet-ARMA-NARX [30], ANN-ARIMA [31,32], ETS-ANN [33], and OT-SVM [34]. Each hybrid technique has its own characteristics and efficiency.…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithmised Neuro Fuzzy System for Forecasting the Online Journal Visitors

Mahmudy¹,

Wibawa²,

Sari³

et al. 2021

IJC

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Artificial Neural Network (ANN) is recognized as one of effective forecasting engines for various business fields. This approach fits well with non-linear data. In fact, it is a black box system with random weighting, which is hard to train. One way to improve its performance is by hybridizing ANN with other methods. In this paper, a hybrid approach, Genetic Algorithm-Neural Fuzzy System (GA-NFS) is proposed to predict the number of unique visitors of an online journal website. The neural network weight is precisely determined using GA. Afterwards, the best weight has been used for testing data and processed using Sugeno Fuzzy Inference System (FIS) for time-series forecasting. Based on experiment, GA-NFS have been produced accuracy with 0.989 of root mean square error (RMSE) that is lower than the RMSE of a common NFS (2,004). This may indicate that the GA based weighting is able to improve the NFS performance on forecasting the number of journal unique visitors.

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A hybrid statistical approach for stock market forecasting based on Artificial Neural Network and ARIMA time series models

Cited by 21 publications

References 23 publications

A Novel Fuzzy Linear Regression Sliding Window GARCH Model for Time-Series Forecasting

A Novel Fuzzy Linear Regression Sliding Window GARCH Model for Time-Series Forecasting

Time series forecasting of styrene price using a hybrid ARIMA and neural network model

Genetic Algorithmised Neuro Fuzzy System for Forecasting the Online Journal Visitors

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