Application of Machine Learning in Forecasting International Trade Trends

Batarseh, Feras A.; Gopinath, Munisamy; Nalluru, Ganesh; Beckman, Jayson

doi:10.48550/arxiv.1910.03112

Cited by 3 publications

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“…More recently, researchers across multidisciplinary fields have identified international trade as well suited for machine learning and network analysis (Bhattacharya et al, 2007;Aggarwal 2016Aggarwal , 2017aAggarwal , 2017b. By harnessing machine learning methods, researchers have been able to better predict agricultural exports trade patterns using Gradient Tree Boosting, ARIMA, and XGBoosting (Batarseh et al, 2019;Chakraborty, 2021, 2022). Use of shallow neural networks also improves international trade forecasting (Wohl and Kennedy 2018).…”

Section: Literature Surveymentioning

confidence: 99%

Research on Anomaly Detection in Time Series: Exploring United States Exports and Imports Using Long Short-Term Memory

AGGARWAL

2023

JORIT

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This survey aims to offer a thorough and organized overview of research on anomaly detection, which is a significant problem that has been studied in various fields and application areas. Some anomaly detection techniques have been tailored for specific domains, while others are more general. Anomaly detection involves identifying unusual patterns or events in a dataset, which is important for a wide range of applications including fraud detection and medical diagnosis. Not much research on anomaly detection techniques has been conducted in the field of economic and international trade. Therefore, this study attempts to analyse the time-series data of United Nations exports and imports for the period 1992 – 2022 using LSTM based anomaly detection algorithm. Deep learning, particularly LSTM networks, are becoming increasingly popular in anomaly detection tasks due to their ability to learn complex patterns in sequential data. This paper presents a detailed explanation of LSTM architecture, including the role of input, forget, and output gates in processing input vectors and hidden states at each timestep. The LSTM based anomaly detection approach yields promising results by modelling small-term as well as long-term temporal dependencies. 

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Section: Literature Surveymentioning

confidence: 99%

Research on Anomaly Detection in Time Series: Exploring United States Exports and Imports Using Long Short-Term Memory

AGGARWAL

2023

JORIT

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“…Machine learning techniques base their predictions on inferred models from data. There are multiple machine learning applications in many domains, as healthcare, education and sports, but has only recently been applied to commercial transactions, so its application to the study of international trade trends is limited (Batarseh et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Two deep learning approaches to forecasting disaggregated freight flows: convolutional and encoder–decoder recurrent

et al. 2021

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Time series forecasting of disaggregated freight flow is a key issue in decision-making by port authorities. For this purpose and to test new deep learning techniques we have selected seven time series of imported goods from Morocco to Spain through the port of Algeciras, and we have tested two forecasting deep neural networks models: dilated causal convolutional and encoder-decoder recurrent. We have experimented with four different granularities for each series: quarterly, monthly, weekly and daily. The results show that our neural network models can manage these raw series without first removing seasonality or trend. We also highlight the ability of neural models to work with a fixed input size of one year, being able to make good predictions using the same input size for all granularities. The two deep learning models have globally improved the benchmarks of the M4 Competition of forecasting. Each neural network model obtains its best results under different circumstances: the recurrent one with daily granularity and intermittent series, and the convolutional one with weekly and monthly granularities.

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AO-SAKEL: arithmetic optimization-based self-adaptive kernel extreme learning for international trade prediction

Gupta

Kumar

2023

Evolving Systems

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Application of Machine Learning in Forecasting International Trade Trends

Cited by 3 publications

References 0 publications

Research on Anomaly Detection in Time Series: Exploring United States Exports and Imports Using Long Short-Term Memory

Research on Anomaly Detection in Time Series: Exploring United States Exports and Imports Using Long Short-Term Memory

Two deep learning approaches to forecasting disaggregated freight flows: convolutional and encoder–decoder recurrent

AO-SAKEL: arithmetic optimization-based self-adaptive kernel extreme learning for international trade prediction

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