An extension of X13-ARIMA-SEATS to forecast islamic holidays effect on logistic activities

Sarhani, Malek; Afia, Abdellatif El

doi:10.1109/gol.2014.6887423

Cited by 4 publications

(6 citation statements)

References 17 publications

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“…The methods chosen are TRAMO-SEATS [62][63][64], ETS [28,65,66], ARIMA [67][68][69], X-13 ARIMA SEATS [70], X11, STL decomposition, the grey model [54,55,71], ANN [42,72], and MLP [73,74], which are widely used forecasting methods in the literature. Each of the aforementioned techniques is initially applied simply to the study of tourist data, and then it is integrated with the ANN technique to incorporate the exogenous factors listed below: the global financial crisis, Turkey-Russia warplane crash crisis, the COVID-19 pandemic, and USD/TRY exchange rates.…”

Section: Originality Of This Studymentioning

confidence: 99%

Forecasting the Tourist Arrival Volumes and Tourism Income with Combined ANN Architecture in the Post COVID-19 Period: The Case of Turkey

Kayral,

Sarı,

Tandoğan Aktepe

2023

Sustainability

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Accurate forecasting of tourism demand and income holds paramount importance for both the tourism industry and the national economy. This study aims to address several objectives: (1) specify the best forecasting model in the prediction of tourist arrival volumes and tourism income for Turkey; (2) assess the degree of impact exerted by various determinants on the tourism forecasts; (3) generate forecasts for tourist arrival volumes and tourism income using the most suitable models; and (4) examine potential scenarios illustrating the ramifications of the Russia-Ukraine war on tourist arrival volumes and tourism income. The forecasting models employed in this study encompass a comprehensive set of statistical methods, including ETS, ARIMA, TRAMO-SEATS, X13, X11, STL, Grey, and their combinations with ANN. In the ANN models, exogenous variables such as the global financial crisis, the Turkey-Russia warplane crash crisis, the COVID-19 pandemic, and USD/TRY exchange rates are incorporated. The results unveil the identification of five superior models: ETS, Grey, hybrid ETS-ANN, hybrid Grey-ANN, and hybrid ARIMA-ANN models, which exhibit the lowest MAPE and sMAPE values. Forecasts for the forthcoming quarters are examined under two scenarios: assuming the continuity or cessation of the Russia-Ukraine war. Comparative analysis of the relative effects of exogenous variables indicates that COVID-19 has the most substantial impact on tourist arrival volumes, and tourism income is primarily influenced by the USD/TRY exchange rate.

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Section: Originality Of This Studymentioning

confidence: 99%

Forecasting the Tourist Arrival Volumes and Tourism Income with Combined ANN Architecture in the Post COVID-19 Period: The Case of Turkey

Kayral,

Sarı,

Tandoğan Aktepe

2023

Sustainability

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“…In the paper of (Sarhani & El Afia, 2014a), we presented a comparison of the two methods X-12-ARIMA and TRAMO-SEATS. We concluded the new approach X-13ARIMA-SEATS which combines the features of the two programs is the more adaptable for modeling the calendar effect.…”

Section: Existing Approaches In Modeling the Calendar Effectmentioning

confidence: 99%

“…To perform the third experiment, we have to adapt the parameters of X-13ARIMA-SEATS to include Islamic holidays. For this purpose, we use the option User-Defined Regressors of the Genhol program to define regressors for Islamic holidays in a same manner as our previous work (Sarhani & El Afia, 2014a)…”

Section: The Elimination Of the Calendar Effectmentioning

confidence: 99%

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Forecasting Demand with Support Vector Regression Technique Combined with X13-ARIMA-SEATS Method in the Presence of Calendar Effect

Sarhani

Afia

2014

International Journal of Applied Logistics

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In order to better manage and optimize supply chain, a reliable prediction of future demand is needed. The difficulty of forecasting demand is due mainly to the fact that heterogeneous factors may affect it. Analyzing such kind of data by using classical time series forecasting methods, will fail to capture such dependency of factors. This paper is released to present a forecasting approach of two stages which combines the recent methods X13-ARIMA-SEATS and Support Vector Regression (SVR). The aim of the first one is to remove the calendar effect, while the purpose of the second one is to forecast the demand after the removal of this effect. This approach is applied to three different case studies and compared to the forecasting method based on SVR alone.

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“…Pergeseran waktu ini berpengaruh pada peramalan data time series musiman, karena model Autoregressive Integrated Moving Average (ARIMA) musiman hanya sesuai untuk musiman dengan periode yang sama, hal ini mengakibatkan peramalan yang kurang tepat. Sarhani and Afia (2014) mengemukakan bahwa Biro Sensus Amerika Serikat dan Bank Spanyol mengembangkan X-13-ARIMA-SEATS yang merupakan gabungan antara metode regARIMA dengan metode seasonal adjustment. Metode regARIMA bertujuan untuk menghilangkan moving holiday effect.…”

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PERAMALAN EKSPOR NONMIGAS DENGAN VARIASI KALENDER ISLAM MENGGUNAKAN X-13-ARIMA-SEATS (Studi Kasus: Ekspor Nonmigas Periode Januari 2013 sampai Desember 2017)

Lestari

Widiharih

Rahmawati

2018

J.Gauss

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Non-oil and gas exports are one of the largest foreign exchange earners for Indonesia. Non-oil and gas exports always experience a decline in the month of Eid Al-Fitr due to delays in the delivery of export goods because the loading and unloading of goods at the port is reduced during Eid Al-Fitr. The shift of the Eid Al-Fitr month on the data will form a pattern or season with an unequal period called the moving holiday effect. The time series forecasting method that usually used the ARIMA method. Because the ARIMA method only suitable for time series data with the same seasonal period and can’t handle the moving holiday effect, the X-13-ARIMA-SEATS method used two steps. First, regARIMA modeling is a linear regression between time series data and the weight of Eid Al-Fitr and the residuals follow the ARIMA process. The weighting is based on three conditions, namely pre_holiday, post_holiday, and multiple. Second, X-12-ARIMA decomposition method for seasonal adjustments that produces trend-cycle components, seasonal, and irregular. Based on the analysis carried out on the monthly non-oil and gas export data for the period January 2013 to December 2017, the X-13-ARIMA-SEATS (1,1,0) model was obtained in the post_holiday condition as the best model. The forecasting results in 2018 show the largest decline in non-oil and gas exports in June 2018 which coincided with the Eid Al-Fitr holiday. MAPE value of 10.90% is obtained which shows that the forecasting ability is good.Keywords: time series, non-oil and gas, X-13-ARIMA-SEATS, moving holiday

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An extension of X13-ARIMA-SEATS to forecast islamic holidays effect on logistic activities

Cited by 4 publications

References 17 publications

Forecasting the Tourist Arrival Volumes and Tourism Income with Combined ANN Architecture in the Post COVID-19 Period: The Case of Turkey

Forecasting the Tourist Arrival Volumes and Tourism Income with Combined ANN Architecture in the Post COVID-19 Period: The Case of Turkey

Forecasting Demand with Support Vector Regression Technique Combined with X13-ARIMA-SEATS Method in the Presence of Calendar Effect

PERAMALAN EKSPOR NONMIGAS DENGAN VARIASI KALENDER ISLAM MENGGUNAKAN X-13-ARIMA-SEATS (Studi Kasus: Ekspor Nonmigas Periode Januari 2013 sampai Desember 2017)

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