Big Data on Vessel Traffic: Nowcasting Trade Flows in Real Time

Arslanalp, Serkan; Marini, Marco; Tumbarello, Patrizia

doi:10.2139/ssrn.3523148

Cited by 11 publications

(13 citation statements)

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“…AIS is also a ship tracking data that can indicate the volume of trade in goods of a region when the amount of seaborne trade is high [23]. Thus, we could use AIS data to forecast trading volumes in real-time by selecting and using the right indicators.…”

Section: Automatic Identification System (Ais)mentioning

confidence: 99%

New recommendation to predict export value using big data and machine learning technique

Nooraeni¹,

Nickelson²,

Rahmadian

et al. 2022

SJI

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Official statistics on monthly export values have a publicity lag between the current period and the published publication. None of the previous researchers estimated the value of exports for the monthly period. This circumstance is due to limitations in obtaining supporting data that can predict the criteria for the current export value of goods. AIS data is one type of big data that can provide solutions in producing the latest indicators to forecast export values. Statistical Methods and Conventional Machine Learning are implemented as forecasting methods. Seasonal ARIMA and Artificial Neural Network (ANN) methods are both used in research to forecast the value of Indonesia’s exports. However, ANN has a weakness that requires high computational costs to obtain optimal parameters. Genetic Algorithm (GA) is effective in increasing ANN accuracy. Based on these backgrounds, this paper aims to develop and select an AIS indicator to predict the monthly export value in Indonesia and optimize ANN performance by combining the ANN algorithm with the genetic algorithm (GA-ANN). The research successfully established five indicators that can be used as predictors in the forecasting model. According to the model evaluation results, the genetic algorithm has succeeded in improving the performance of the ANN model as indicated by the resulting RMSE GA-ANN value, which is smaller than the RMSE of the ANN model.

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Section: Automatic Identification System (Ais)mentioning

confidence: 99%

New recommendation to predict export value using big data and machine learning technique

Nooraeni¹,

Nickelson²,

Rahmadian

et al. 2022

SJI

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“…Nowcasting is often used to analyze macroeconomic variables like those included in the Goal 9 indicators [48][49][50][51][52]. Explanatory variables should be widely available for these indicators.…”

Section: Goal 9: Industry Innovation and Infrastructurementioning

confidence: 99%

Feasibility of nowcasting SDG indicators: A comprehensive survey

Hopp¹,

Fu²,

Peltola³

2022

SJI

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The 2030 Agenda and accompanying Sustainable Development Goals (SDGs) are vital in guiding national and global policy. However, many of the SDG indicators used to measure progress toward those goals suffer from long publication lags. Nowcasting has the potential to address this problem and generate more timely estimates of those indicators. This paper provides resources for achieving that potential by 1) carrying out a comprehensive nowcasting feasibility survey of all SDG indicators to assess their potential to be nowcast, and 2) performing a case study of indicator 9.4.1 to illustrate and shed light on the process of performing a nowcasting exercise. There exist 231 SDG indicators, but due to only examining Tier 1 indicators and the fact that many indicators have multiple sub-indicators, 362 indicators and sub-indicators were eventually surveyed. Of those 362, 150 were found highly likely to be suitable candidates for nowcasting, 87 were found to be likely, and 125 were found to be unsuitable.

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“…STA has experimented with a method to use Big Data on vessel traffic (developed by Arslanalp, Marini, and Tumbarello (2019)) to produce trade estimates in real time for Malta. 3 The methodology was extended by Arslanalp, Koepke and Verschuur (2021) and applied to eleven Pacific Island countries (about half of which are FCS) in view of lags in official statistics.…”

Section: ______________ Box 2 Statistical Innovation and Big Data In ...mentioning

confidence: 99%