Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Khairalla, Mergani A.; XuNing,; AL-Jallad, Nashat T.

doi:10.14569/ijacsa.2017.080945

Cited by 25 publications

(15 citation statements)

References 31 publications

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“…Faruk [15] has mentioned the superiority of the hybrid models over the ARIMA and neural network models for water quality predictions. Khairalla and AL-Jallad [16] have also obtained similar results in the case of financial time series analysis. Results obtained by Rathod et al [17] have revealed that the forecasting accuracy of the hybrid models is better as compared to the single models and among the hybrid models, the ARIMA-NLSVR model has performed superior than the ARIMA-TDNN model.…”

Section: Introductionsupporting

confidence: 58%

Hybrid Time Series Models for Forecasting Maize Production in India

Pandit

Bakshi

Gangadhar

2021

CJAST

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In spite of the immense success of different linear and non-linear time series models in their respective domains, real-world data are rarely pure linear or non-linear in nature. Hence, a hybrid modelling framework with the capability of handling both linear and non-linear patterns can substantially improve the forecasting accuracy. With this backdrop, an effort has been made in this investigation to evaluate the suitability of hybrid models in compassion to single linear or non-linear models for forecasting maize production in India. Data from 1949-50 to 2016-17 have been utilised for the model building purpose while retaining the data from 2017-18 to 2019-20 for the post-sample accuracy assessment. Outcomes emanated from this investigation clearly reveals that the ARIMA-NLSVR model has outperformed all other candidate models employed in this study. It is noteworthy to mention that both the hybrid models have performed better than their individual counterparts. The superior forecasting ability of both the non-linear models over the linear ARIMA model has also been evident.

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Section: Introductionsupporting

confidence: 58%

Hybrid Time Series Models for Forecasting Maize Production in India

Pandit

Bakshi

Gangadhar

2021

CJAST

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“…Then, three approaches were examined, namely, ARIMA, EXP, and ANN. Conclusions indicated the advantages of a combination model on all other examined models based on 0.82% MAPE error computing for precision (Khairalla & AL-Jallad, 2017). Recently, some scholars have applied an ANN model on the ground of a tool of their study.…”

Section: Literature Reviewmentioning

confidence: 91%

Integrating Wavelet Decomposition and Fuzzy Transformation for Improving the Accuracy of Forecasting Crude Oil Price

Saghi

Rezaee

2021

Comput Econ

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In this paper, hybrid methods are proposed to predict OPEC crude oil. In the preprocessing step, the wavelet decomposition has been used to reduce the noise of time series, which divides the original data into five levels. Also, the fuzzy transform (F-transform) is applied for its potency of management uncertainty due to the fluctuation of data. F-transform is for decomposing the time series data and afterward, this decomposed data are employed on the ground of inputs. Because of the nonlinearity of the time series structure, integrating the mentioned proposed algorithms with the multilayer perceptron (MLP), radial basis functions, group method of data handling (GMDH), adaptive neuro-fuzzy inference system (ANFIS), and support vector regression (SVR) are applied for prediction of time series relevant to the crude oil price. By comparing the results of models, L5-ANFIS, L5-MLP, L5-GMDH, L5-SVR are more appropriate than others for predicting OPEC crude oil price.

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“…Machine Learning methods have also been used to improve forecasting results. Much research on this has been done, either applying methods independently or combining them with statistical methods [13][14][15][16][17][18][19][20][21][22]. This study tries to predict the net income for next year by using several financial ratios obtained from four leading banks in Indonesia based on time series data modeling by using ARX model.…”

Section: Introductionmentioning

confidence: 99%

Net income prediction of several leading bank in Indonesia using neural approach

Rofiqoh¹,

Gaffar²,

Setyadi³

et al. 2018

IJET

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The IFRS (International Financial Reporting Standards) defines net income as synonymous with profit and loss. Net income can be used as a consideration for investment decision making for investors who will invest their capital into a company. Net income for the next year cannot be ascertained but can be predicted by using several financial ratios that affect the change in net income. This study tries to predict net income next year by using several financial ratios obtained from four leading banks in Indonesia. The time series data modeling by using Artificial Neural Network (ANN) based Auto-Regressive with Exogenous input (ARX) model. In this study only use one net structure to model time series data in order to improve the efficiency of the model. Back-Propagation (BP) doing backpropagation to fix the weight of each layer of ANN such that to achieve appointed target error.

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Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Cited by 25 publications

References 31 publications

Hybrid Time Series Models for Forecasting Maize Production in India

Hybrid Time Series Models for Forecasting Maize Production in India

Integrating Wavelet Decomposition and Fuzzy Transformation for Improving the Accuracy of Forecasting Crude Oil Price

Net income prediction of several leading bank in Indonesia using neural approach

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