A Comparative Study of Autoregressive and Neural Network Models: Forecasting the GARCH Process
A Comparative Study of Autoregressive and Neural Network Models: Forecasting the GARCH Process
Date
2022
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Authors
Kamalov, Firuz
Gurrib, Ikhlaas
Moussa, Sherif
Nazir, Amril
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Science and Business Media Deutschland GmbH
Abstract
The Covid-19 pandemic has highlighted the importance of forecasting in managing public health. The two of the most commonly used approaches for time series forecasting methods are autoregressive (AR) and deep learning models (DL). While there exist a number of studies comparing the performance of AR and DL models in specific domains, there is no work that analyzes the two approaches in the general context of theoretically simulated time series. To fill the gap in the literature, we conduct an empirical study using different configurations of generalized autoregressive conditionally heteroskedastic (GARCH) time series. The results show that DL models can achieve a significant degree of accuracy in fitting and forecasting AR-GARCH time series. In particular, DL models outperform the AR-based models over a range of parameter values. However, the results are not consistent and depend on a number of factors including the DL architecture, AR-GARCH configuration, and parameter values. The study demonstrates that DL models can be an effective alternative to AR-based models in time series forecasting. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Description
This work is not available in the CUD collection. The version of the scholarly record of this work is published in Lecture Notes in Computer Science (2022), available online at: https://doi.org/10.1007/978-3-031-13832-4_48.
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Conference Paper
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Keywords
ARIMA , Deep learning , GARCH , Neural networks , Time series forecasting
Citation
Kamalov, F., Gurrib, I., Moussa, S., & Nazir, A. (2022). A comparative study of autoregressive and neural network models: Forecasting the GARCH process. Lecture Notes in Computer Science, 13395, 589 - 603. https://doi.org/10.1007/978-3-031-13832-4_48