SARIMA model for forecasting foreign tourists at the Kualanamu International Airport

Rusyana, Asep; Nurhasanah,; Marzuki,; Flancia, Mia

doi:10.1109/icmsa.2016.7954329

Cited by 10 publications

(7 citation statements)

References 2 publications

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“…It's been a considerable shift in the last couple of decades. There are no longer any forests, and animals and plants struggle to survive due to water (Rusyana & Flancia, 2016). Over the past three decades, the quality of water resources, particularly groundwater, has deteriorated significantly.…”

Section: Figure 1: Modulating Influences For Climate Changementioning

confidence: 99%

Climate Change Forecasting Using Machine Learning SARIMA Model

Zia

2021

iRASD J. Comp. & Info Tech.

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Every country's population will have to deal with the effects of climate change. The meteorological department needs to implement effective forecasting methods to deal with climate changes. Accurate temperature forecasts help in protecting people and property is an essential aspect of government, business, and the general public planning. Early predictions help farmers and industrialists to make approaches and store crops more effectively. When the climate continuously changes, it is not easy to make accurate predictions for the meteorological department and government authorities. Artificial intelligence (AI) algorithms have stimulated improvements in various fields. Machine learning (ML) may find teleconnections where complicated feedbacks make it challenging to determine how proposed work from a straightforward analysis and observations. Our proposed research uses the machine learning algorithm, SARIMA Model, to comprehend and utilize existing datasets and simulations.

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Section: Figure 1: Modulating Influences For Climate Changementioning

confidence: 99%

Climate Change Forecasting Using Machine Learning SARIMA Model

Zia

2021

iRASD J. Comp. & Info Tech.

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“…The authors have used the seasonal auto-regressive integrated model (SARIMA) to forecast foreign tourists at the Kualanamu International Airport [13]. The authors have used a 72 periods dataset from January 2008 to December 2013, to determine the model and 23 periods dataset from January 2014 to November 2015 for testing the accuracy of the model.…”

Section: Related Workmentioning

confidence: 99%

An Unemployment Prediction Rate for Indian Youth Through Time Series Forecasting

Sharma

Soni

2021

Algorithms for Intelligent Systems

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“…Stationary test results on variants in the input series are presented in Table 1 below. Box-Cox way is a method to change data so they become stationary, introduced by Box and Tiao Cox [5]. Using the rank transformation in Table 1, the values of λ of each input series and the series of output are equal to 1.…”

Section: • Stationary Testing Of Variantsmentioning

confidence: 99%

Forecasting rainfall using transfer function in Aceh province, Indonesia

Rusyana¹,

Salwa²,

Marziah³

2019

ams

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Aceh is a province located in the western Indonesia and Aceh has varied climate conditions. As one element of weather, rainfall is an important element in determining the weather. Components that affect rainfall include air temperature and wind speed. This study predicts rainfall based on its components with using transfer function. Transfer function is a model that mixes time series approach with causal approach.

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SARIMA model for forecasting foreign tourists at the Kualanamu International Airport

Cited by 10 publications

References 2 publications

Climate Change Forecasting Using Machine Learning SARIMA Model

Climate Change Forecasting Using Machine Learning SARIMA Model

An Unemployment Prediction Rate for Indian Youth Through Time Series Forecasting

Forecasting rainfall using transfer function in Aceh province, Indonesia

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