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OPTIMIZING TIME SERIES FORECASTING: A COMPARATIVE STUDY OF ADAM AND NESTEROV
ACCELERATED GRADIENT ON LSTM AND GRU NETWORKS USING STOCK MARKET DATA

Ahmad Makinde

Papers from arXiv.org

Abstract: Several studies have discussed the impact different optimization
techniques in the context of time series forecasting across different Neural
network architectures. This paper examines the effectiveness of Adam and
Nesterov's Accelerated Gradient (NAG) optimization techniques on LSTM and GRU
neural networks for time series prediction, specifically stock market
time-series. Our study was done by training LSTM and GRU models with two
different optimization techniques - Adam and Nesterov Accelerated Gradient
(NAG), comparing and evaluating their performance on Apple Inc's closing price
data over the last decade. The GRU model optimized with Adam produced the lowest
RMSE, outperforming the other model-optimizer combinations in both accuracy and
convergence speed. The GRU models with both optimizers outperformed the LSTM
models, whilst the Adam optimizer outperformed the NAG optimizer for both model
architectures. The results suggest that GRU models optimized with Adam are
well-suited for practitioners in time-series prediction, more specifically stock
price time series prediction producing accurate and computationally efficient
models. The code for the experiments in this project can be found at
https://github.com/AhmadMak/Time-Series-Optimization-Research Keywords:
Time-series Forecasting, Neural Network, LSTM, GRU, Adam Optimizer, Nesterov
Accelerated Gradient (NAG) Optimizer

Date: 2024-09
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2410.01843 Latest version (application/pdf)

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