Horizontal Divide and Conquer Methods for Non-Linear Time Series Forecasting Using LSTM-ANN; a Comparative Study Based on Simulated Data

Navoda Hettige^1, and WMND Basnayake¹

¹Department of Statistics, University of Colombo, Colombo, Sri Lanka

Cite this paper

Navoda Hettige and WMND Basnayake. Horizontal Divide and Conquer Methods for Non-Linear Time Series Forecasting Using LSTM-ANN; a Comparative Study Based on Simulated Data. American Journal of Applied Mathematics and Statistics. 2023; 11(2):63-69. doi: 10.12691/AJAMS-11-2-4

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.

Keywords

complex, nonlinear, hybrid, LSTM

Copyright

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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