Version 1
: Received: 20 October 2023 / Approved: 23 October 2023 / Online: 25 October 2023 (10:23:19 CEST)
How to cite:
Aktas, M.; Yilmaz, M.B.; Karabacak, A. An Experimental Study Investigating The Effects of Data Pre-processing Methods on Deep Learning Based Time Series Data Prediction. Preprints2023, 2023101635. https://doi.org/10.20944/preprints202310.1635.v1
Aktas, M.; Yilmaz, M.B.; Karabacak, A. An Experimental Study Investigating The Effects of Data Pre-processing Methods on Deep Learning Based Time Series Data Prediction. Preprints 2023, 2023101635. https://doi.org/10.20944/preprints202310.1635.v1
Aktas, M.; Yilmaz, M.B.; Karabacak, A. An Experimental Study Investigating The Effects of Data Pre-processing Methods on Deep Learning Based Time Series Data Prediction. Preprints2023, 2023101635. https://doi.org/10.20944/preprints202310.1635.v1
APA Style
Aktas, M., Yilmaz, M.B., & Karabacak, A. (2023). An Experimental Study Investigating The Effects of Data Pre-processing Methods on Deep Learning Based Time Series Data Prediction. Preprints. https://doi.org/10.20944/preprints202310.1635.v1
Chicago/Turabian Style
Aktas, M., Mustafa Bugra Yilmaz and Abdulkadir Karabacak. 2023 "An Experimental Study Investigating The Effects of Data Pre-processing Methods on Deep Learning Based Time Series Data Prediction" Preprints. https://doi.org/10.20944/preprints202310.1635.v1
Abstract
Time-series analysis is a widely used technique across various fields and industries, as it helps in understanding, predicting, and forecasting the behavior of data points over time. These fields include but are not limited to finance, economics, healthcare, transportation, etc. In the case of this paper, we have focused on finance. Predicting future values of financial time series offers several benefits. Accurate forecasts can help investors make better decisions about their investments. To predict future values, deep learning algorithms are commonly used since it is an effective method with complex data. In this study, we conduct a study that investigates the use of different data pre-processing techniques on deep learning algorithms in predicting the time series values. To conduct this experimental study, we utilize an open source software, which using long short-term memory technique as the representative deep learning technique, published in github software code repository platform. With this study, we investigate the effects of autoencoder and discrete wavelet transform data pre-processing techniques in time-series prediction. We discuss the details of the experimental study and report our results. The results show that time series prediction (using backtesting methodology) without any data pre-processing leads to 12.6% for mean absolute percentage error. The results also show that, time series prediction with the data preprocessing techniques (Wavelet Transform and Stacked Autoencoder) lead to 3.4%
Keywords
experimental study on time series prediction methods; investigation on time series model predictions; analysis of the effects of data pre-processing methods on time series prediction; long short-term memory models; stacked autoencoder; wavelet transformation
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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.
