| Issue |
E3S Web Conf.
Volume 483, 2024
The 3rd International Seminar of Science and Technology (ISST 2023)
|
|
|---|---|---|
| Article Number | 03015 | |
| Number of page(s) | 8 | |
| Section | Trends in Mathematics and Computer Science for Sustainable Living | |
| DOI | https://doi.org/10.1051/e3sconf/202448303015 | |
| Published online | 31 January 2024 | |
Time Series Prediction on Population Dynamics
Udayana University, Mathematics Department, 80361, Badung, Bali, Indonesia
* Corresponding author: dwipayana@unud.ac.id
Predicting the time series is a challenging topic mainly on the era of big data. In this research, data taken from population dynamics of one dimension of logistic map with various parameters that leading the system into chaos. Various machine learning methods is employed for predicting the time series data such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and 1 Dimension of Convolution Neural Network (1D CNN). Several data sizes were considered: 1000, 10000, 50000, 100000 and 1 million points of time series data. As evaluation metric, Root Means Square Error (RMSE) is used to assess the accuracy of each method. The result indicating that the LSTM has the smallest RMSE value among all the three machine learning methods.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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