@article{ajams20231124,
author={{Hettige, Navoda and Basnayake, WMND},
title={Horizontal Divide and Conquer Methods for Non-Linear Time Series Forecasting Using LSTM-ANN; a Comparative Study Based on Simulated Data},
journal={American Journal of Applied Mathematics and Statistics},
volume={11},
number={2},
pages={63--69},
year={2023},
url={http://pubs.sciepub.com/ajams/11/2/4},
issn={2328-7292},
abstract={Time series forecasting holds a vital significance across diverse domains; however, accurately predicting the future is challenging due to the inherent complexity and non-linear nature of the data. One promising strategy for tackling non-linear time series data is the utilization of hybrid models, which have the potential to enhance forecasting accuracy. In this research paper, a comparative study is conducted, focusing on different approaches to horizontally partition data and fit Long short-term memory artificial neural network models (LSTM ANN). By using simulated data, this study effectively evaluates the efficacy of these approaches. The results demonstrate that DWT-LSTM, EEMD-LSTM, and CEEMDAN-LSTM techniques outperformed the EMD-LSTM and Threshold-LSTM models.},
doi={10.12691/ajams-11-2-4}
publisher={Science and Education Publishing}
}