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Forecasting Financial Time Series using a Hybrid Non-stationary Model with ANN

D.K.Shetty 1 , B.Ismail 2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-1 , Page no. 323-326, Jan-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i1.323326

Online published on Jan 31, 2019

Copyright © D.K.Shetty, B.Ismail . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: D.K.Shetty, B.Ismail, “Forecasting Financial Time Series using a Hybrid Non-stationary Model with ANN,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.323-326, 2019.

MLA Style Citation: D.K.Shetty, B.Ismail "Forecasting Financial Time Series using a Hybrid Non-stationary Model with ANN." International Journal of Computer Sciences and Engineering 7.1 (2019): 323-326.

APA Style Citation: D.K.Shetty, B.Ismail, (2019). Forecasting Financial Time Series using a Hybrid Non-stationary Model with ANN. International Journal of Computer Sciences and Engineering, 7(1), 323-326.

BibTex Style Citation:
@article{_2019,
author = {D.K.Shetty, B.Ismail},
title = {Forecasting Financial Time Series using a Hybrid Non-stationary Model with ANN},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2019},
volume = {7},
Issue = {1},
month = {1},
year = {2019},
issn = {2347-2693},
pages = {323-326},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3505},
doi = {https://doi.org/10.26438/ijcse/v7i1.323326}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i1.323326}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3505
TI - Forecasting Financial Time Series using a Hybrid Non-stationary Model with ANN
T2 - International Journal of Computer Sciences and Engineering
AU - D.K.Shetty, B.Ismail
PY - 2019
DA - 2019/01/31
PB - IJCSE, Indore, INDIA
SP - 323-326
IS - 1
VL - 7
SN - 2347-2693
ER -

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Abstract

Forecasting financial time series have been regarded as one of the most challenging applications of modern time series forecasting. Thus, numerous models have been depicted to provide the investors with more precise predictions. In recent years, financial market dynamics forecasting has been a focus of economic research. In this paper, we propose a hybrid non-stationary time series model with artificial neural network (ANN) for forecasting financial time series. The proposed model is non-stationary in trend component with regressor, lagged variable and non-linear component. The proposed model can capture both linear and non-linear structures in the time series. Non-linear structure is capture by Fed-Forward Neural Networks (FNN). The working of the proposed model is examined for SPY and VOO stock prices. Forecast based on the proposed model performs better than existing models in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percent Error (MAPE) criterion.

Key-Words / Index Term

ARIMA-ANN, ARIMA-GARCH, Trend, Hybrid and Accuracy

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