An Evaluation of Comparison between Multivariate Fuzzy Time Series with Traditional Time Series Model for Forecasting Taiwan Export

Wong, Hsien-Lun; Tu, Yi-Hsien; Wang, Chi-Chen

doi:10.1109/csie.2009.827

Cited by 12 publications

(2 citation statements)

References 10 publications

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“…Fuzzy time series models have also emerged as an alternative for trade forecasting. Wong et al [20] assessed the performance of multivariate fuzzy time series models against traditional time series models for forecasting Taiwan's exports. Teir fndings suggested that fuzzy time series models could surpass ARIMA in short-term forecasting, although ARIMA was more adept for long-term predictions.…”

Section: Related Workmentioning

confidence: 99%

Meta-Learning Enhanced Trade Forecasting: A Neural Framework Leveraging Efficient Multicommodity STL Decomposition

Ma,

Xue,

Chen

et al. 2024

International Journal of Intelligent Systems

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In the dynamic global trade environment, accurately predicting trade values of diverse commodities is challenged by unpredictable economic and political changes. This study introduces the Meta-TFSTL framework, an innovative neural model that integrates Meta-Learning Enhanced Trade Forecasting with efficient multicommodity STL decomposition to adeptly navigate the complexities of forecasting. Our approach begins with STL decomposition to partition trade value sequences into seasonal, trend, and residual elements, identifying a potential 10-month economic cycle through the Ljung–Box test. The model employs a dual-channel spatiotemporal encoder for processing these components, ensuring a comprehensive grasp of temporal correlations. By constructing spatial and temporal graphs leveraging correlation matrices and graph embeddings and introducing fused attention and multitasking strategies at the decoding phase, Meta-TFSTL surpasses benchmark models in performance. Additionally, integrating meta-learning and fine-tuning techniques enhances shared knowledge across import and export trade predictions. Ultimately, our research significantly advances the precision and efficiency of trade forecasting in a volatile global economic scenario.

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Section: Related Workmentioning

confidence: 99%

Meta-Learning Enhanced Trade Forecasting: A Neural Framework Leveraging Efficient Multicommodity STL Decomposition

Ma,

Xue,

Chen

et al. 2024

International Journal of Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…As principais técnicas inteligentes que são usadas em previsão de séries temporais são as Redes Neurais Artificiais (RNA) (Zhang, 2007;Qingle, 2010;Chai, 2010) e os Sistemas de Inferência Fuzzy (SIF) (Chen, 2011;Chen, 2010;Wong, 2009) …”

Section: Técnicas De Inteligência Computacionalunclassified

Esnpredictor: Ferramenta De Previsão De Séries Temporais Baseada Em Echo State Networks Otimizadas Por Algoritmos Genéticos E Particle Swarm Optimization

RUEDA¹

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Prediction and Optimization of Export Opportunities Using Trade Data and Portfolio

Khan

Yusuf

2019

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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An Evaluation of Comparison between Multivariate Fuzzy Time Series with Traditional Time Series Model for Forecasting Taiwan Export

Cited by 12 publications

References 10 publications

Meta-Learning Enhanced Trade Forecasting: A Neural Framework Leveraging Efficient Multicommodity STL Decomposition

Meta-Learning Enhanced Trade Forecasting: A Neural Framework Leveraging Efficient Multicommodity STL Decomposition

Esnpredictor: Ferramenta De Previsão De Séries Temporais Baseada Em Echo State Networks Otimizadas Por Algoritmos Genéticos E Particle Swarm Optimization

Prediction and Optimization of Export Opportunities Using Trade Data and Portfolio

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