A Bi-GRU-based encoder–decoder framework for multivariate time series forecasting

Drought forecasting is crucial for minimizing the effects of drought, alerting people to its dangers, and assisting decision-makers in taking preventative action. This article suggests an encoder–decoder framework for multivariate times series (EDFMTS) forecasting. EDFMTS is composed of three layers...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Vol. 28; no. 9-10; pp. 6775 - 6786
Main Authors: Balti, Hanen, Ben Abbes, Ali, Farah, Imed Riadh
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2024
Springer Nature B.V
Subjects:
Accuracy
Application of Soft Computing
Artificial Intelligence
Artificial neural networks
Autoregressive models
Big Data
Climate change
Coders
Computational Intelligence
Control
Correlation coefficients
Datasets
Deep learning
Drought
Engineering
Forecasting
Forecasting techniques
General circulation models
Mathematical Logic and Foundations
Mechatronics
Multivariate analysis
Neural networks
Optimization
Precipitation
Rain
Remote sensing
Remote sensors
Robotics
Root-mean-square errors
Sensors
Support vector machines
Time series
Variables
China
Deep learning
Encoder–decoder
Multivariate time series
Drought forecasting
ISSN:1432-7643, 1433-7479
Online Access:Get full text
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Summary:Drought forecasting is crucial for minimizing the effects of drought, alerting people to its dangers, and assisting decision-makers in taking preventative action. This article suggests an encoder–decoder framework for multivariate times series (EDFMTS) forecasting. EDFMTS is composed of three layers: a temporal attention context layer, a gated recurrent unit (GRU)-based decoder component, and a bidirectional gated recurrent unit (Bi-GRU)-based encoder component. The proposed framework was evaluated using multivariate gathered from various sources in China (remote-sensing sensors, climate sensors, biophysical sensors, and so on). According to experimental results, the proposed framework outperformed the baseline methods in univariate and multivariate times series (TS) forecasting. The correlation coefficient of determination ( R 2 ), root-mean-squared error (RMSE), and the mean absolute error (MAE) were used for the evaluation of the framework performance. The R 2 , RMSE, and MAE are 0.94, 0.20, and 0.13, respectively, for EDFMTS. In contrast, the RMSE provided by autoregressive integrated moving average (ARIMA), PROPHET, long short-term memory (LSTM), GRU and convolutional neural network (CNN)-LSTM are 0.72, 0.92, 0.36, 0.40, and 0.27, respectively.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-023-09531-9