A Hybrid Convolutional Neural Network and Bidirectional LSTM Architecture for Multi-Sector Export Forecasting: A Macroeconomic Time Series Analysis of Indonesia

Anggreani, Desi and Nurmisba and Aedah, Abd Rahman (2025) A Hybrid Convolutional Neural Network and Bidirectional LSTM Architecture for Multi-Sector Export Forecasting: A Macroeconomic Time Series Analysis of Indonesia. Indonesian Journal of Data and Science, 6 (3). pp. 538-547.

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Abstract

Accurately predicting export values is key for a country in formulating its economic plans. Unfortunately, export data often exhibits complex time series patterns that are difficult to predict, characterized by non-linearity, high volatility, and complex temporal dependencies. This study offers a solution by testing a combined deep learning model, specifically a fusion of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM), to address the challenges of export time series forecasting. This study uses this approach to forecast Indonesia's monthly export time series data from 2016 to 2023, covering various sectors ranging from oil and gas, non-oil and gas, agriculture, industry, mining, and others. The core idea is to leverage the CNN's ability to identify hidden features within time series patterns, while the BiLSTM is tasked with understanding the temporal flow of data from both directions to capture the inherent long-term temporal dependencies within economic time series data. As a result, this combined model proved to be far superior to the standard BiLSTM model in handling the complexity of export time series. In the Non-Oil and Gas sector, the proposed model achieved a high level of accuracy with an MSE value of 3,330,239.74, an RMSE of 1,824.89, and an average prediction error (MAPE) of only 8.17%, representing a significant improvement of 69% over the baseline BiLSTM model. Similar success was also found in all other sectors, proving that this hybrid approach is highly promising for complex economic time series analysis

Item Type:	Journal
Uncontrolled Keywords:	Bidirectional LSTM, Convolutional Neural Network, Hybrid Architecture, Export Forecasting, Time Series Analysis
Divisions:	School of Science and Technology
Depositing User:	Muhamad Aizat Nazmi Mohd Nor Hamin
Date Deposited:	24 Aug 2026 03:26
Last Modified:	24 Aug 2026 03:26
URI:	http://ur.aeu.edu.my/id/eprint/1501

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