An Alternative Forecasting Using Holt-Winter Damped Trend for Soekarno-Hatta Airport Passenger Volume

Primandari, Arum Handini

doi:10.20885/eksakta.vol17.iss1.art1

Cited by 2 publications

(3 citation statements)

References 1 publication

(1 reference statement)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Long-Short Term Memory (LSTM) is a deep machine learning method developed by Hochreiter and Schmidhuber in 1997. This development is applied to overcome the vanishing gradient problem found in the Recurrent Neural Network (RNN) architecture [6]. The LSTM architecture studies long-term dependencies and allows the network to handle and normalize an unlimited number of states [7].…”

Section: Long-short Term Memorymentioning

confidence: 99%

The Application of the Long-Short Term Memory (LSTM) Forecasting Method on the Impact of Tropical Cyclones in Indonesia

Siswono,

Lina,

Pricila

2023

View full text Add to dashboard Cite

Effective disaster mitigation strategies are paramount in the realm of risk management concerning natural calamities, with the primary objective of mitigating potential devastation. A pragmatic and impactful method involves predicting the contributory aspects of such disasters, encompassing variables such as torrential rainfall and formidable wind velocities that tropical cyclones bring. In this study, a comparative analysis of forecasting methodologies is undertaken, precisely the Long Short-Term Memory (LSTM) technique and the Holt Winter approach, both directed toward gauging the impact of tropical cyclones. This investigation focuses on two critical factors: the forecast of precipitation intensity and the estimation of maximum wind speed. The outcomes underscore the superior predictive capabilities of the LSTM method, unequivocally revealing its aptitude for predicting rainfall and wind speed. Impressively, the LSTM method yields remarkable precision levels of 97.433% for rainfall and an even higher accuracy of 99.018% for maximum wind speed forecasting. In essence, this study highlights LSTM's efficacy in disaster prediction with substantial accuracy.

show abstract

Section: Long-short Term Memorymentioning

confidence: 99%

The Application of the Long-Short Term Memory (LSTM) Forecasting Method on the Impact of Tropical Cyclones in Indonesia

Siswono,

Lina,

Pricila

2023

View full text Add to dashboard Cite

show abstract

“…Holt-Winters seasonality has been employed in forecasting research. A modification of Holt-Winters called Holt-Winters damped model was used by Primandari, (2017). Holt-Winters damped model is usually applied for increasing trend.…”

Section: Introductionmentioning

confidence: 99%

“…Holt-Winters damped model is usually applied for increasing trend. The prediction will lower future forecast because it is very common that the increasing trend is not sustainable, so a damping parameter will be added to lower future expectations (Primandari, 2017). The forecasting object is passengers arriving at Soekarno-Hatta airport.…”

Section: Introductionmentioning

confidence: 99%

Foreign Tourists Arrival Forecasting at Major Airports in Indonesia:

Ponziani

2021

ijebd

View full text Add to dashboard Cite

Purpose: This research purports to forecast the number of foreign tourists arriving at major airport in Indonesia. The airports chosen are Soekarno Hatta, Juanda, I Gusti Ngurah Rai, and Kualanamu international airports. Design/methodology/approach: The data used were foreign tourists arrival at major airports located in Jakarta, Surabaya, Medan, and Denpasar. The data extended from January 2014 until December 2018. Two time-series methods were employed, namely Holt-Winter Seasonality and Exponential Smoothing with maximum likelihood. The forecasts would reveal the fitted numbers of foreign tourists arriving from January 2019 until December 2019. The fitted numbers would then be compared to the actual numbers of January 2019 to December 2019. Findings: The results showed that, overall, Holt-Winters seasonality excel at forecasting foreign tourists arrival at Soekarno Hatta and Juanda international airports. While Exponential Smoothing perform better for prediction at I Gusti Ngurah Rai and Kualanamu international airports. The MAPE for Holt-Winters at Soekarno Hatta and Juanda international airports were 26.1585% and 14.538%. The MAPE for Exponential Smoothing at at I Gusti Ngurah Rai and Kualanamu international airports were 7.76% and 15.6791%. Research limitations/implications: Forecasting for foreign tourist arrival at Soekarno Hatta and Juanda international airports should employ Holt-Winters approach. Forecasting for foreign tourists arrival at I Gusti Ngurah Rai and Kualanamu international airports should employ Exponential Smoothing with maximum likelihood. Practical implications: Certain forecasting methods work better than the others at certain international airports. Many forercasting methods are available. Two methods are specifically prominent for detecting seasonality and trend, i.e Holt-Winters and Exponential Smoothing with maximum likelihood. Originality/value: Most research focus on one method at a time. This research compares two methods so that we can know better which method is suitable for certain airports. Four international airports are sampled in this study. Not many research focus on several places at a time. Paper type: Research paper

show abstract

An Alternative Forecasting Using Holt-Winter Damped Trend for Soekarno-Hatta Airport Passenger Volume

Cited by 2 publications

References 1 publication

The Application of the Long-Short Term Memory (LSTM) Forecasting Method on the Impact of Tropical Cyclones in Indonesia

The Application of the Long-Short Term Memory (LSTM) Forecasting Method on the Impact of Tropical Cyclones in Indonesia

Foreign Tourists Arrival Forecasting at Major Airports in Indonesia:

Contact Info

Product

Resources

About