Improvement of LSTM-Based Forecasting with NARX Model through Use of an Evolutionary Algorithm

The reported work aims to improve the performance of LSTM-based (Long Short-Term Memory) forecasting algorithms in cases of NARX (Nonlinear Autoregressive with eXogenous input) models by using evolutionary search. The proposed approach, ES-LSTM, combines a two-membered ES local search procedure (2ME...

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Bibliographic Details
Published in:Electronics (Basel) 2022-09, Vol.11 (18), p.2935
Main Authors: Cocianu, Cătălina Lucia, Uscatu, Cristian Răzvan, Avramescu, Mihai
Format: Article
Language:English
Subjects:
Accuracy
Artificial intelligence
Autoregressive models
Comparative analysis
Economic forecasting
Energy consumption
Error analysis
Evolutionary algorithms
Forecasting
Genetic algorithms
Methods
Neural networks
Optimization
Parameters
Performance enhancement
Securities markets
Stock exchanges
Technology application
Time series
Time-series analysis
Training
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Summary:The reported work aims to improve the performance of LSTM-based (Long Short-Term Memory) forecasting algorithms in cases of NARX (Nonlinear Autoregressive with eXogenous input) models by using evolutionary search. The proposed approach, ES-LSTM, combines a two-membered ES local search procedure (2MES) with an ADAM optimizer to train more accurate LSTMs. The accuracy is measured from both error and trend prediction points of view. The method first computes the learnable parameters of an LSTM, using a subset of the training data, and applies a modified version of 2MES optimization to tune them. In the second stage, all available training data are used to update the LSTM’s weight parameters. The performance of the resulting algorithm is assessed versus the accuracy of a standard trained LSTM in the case of multiple financial time series. The tests are conducted on both training and test data, respectively. The experimental results show a significant improvement in the forecasting of the direction of change without damaging the error measurements. All quality measures are better than in the case of the standard algorithm, while error measures are insignificantly higher or, in some cases, even better. Together with theoretical consideration, this proves that the new method outperforms the standard one.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11182935