Forecasting the Istanbul Freight Index: A Comparative Analysis of SARIMA, XGBoost, and Facebook Prophet Across Multiple Time Horizons

Abstract

Accurate freight rate forecasting is of critical importance for maritime stakeholders in making informed decisions amid volatile market conditions. This study evaluates the performance of three forecasting models, Seasonal Autoregressive Integrated Moving Average (SARIMA), Facebook Prophet, and Extreme Gradient Boosting (XGBoost), in predicting the Istanbul Freight Index (ISTFIX), a key benchmark for small coaster and mini-bulk carrier markets across Black Sea and Mediterranean routes. Weekly ISTFIX data covering the period from January 7, 2008 to March 17, 2025 are employed in the analysis. A rolling-window split strategy is applied to test forecast accuracy across four time horizons: 1-month, 3-month, 6-month, and 1-year. Model performance is evaluated using MAPE, MAE, and RMSE. The results demonstrate that XGBoost achieves superior short-term accuracy with a MAPE of 6.12% at the 1-month horizon, maintaining near-reasonable performance at longer horizons. SARIMA performs consistently across all horizons with MAPE values ranging from 7.16% to 26.63%. Prophet, by contrast, fails to capture the memory-dependent and volatile nature of ISTFIX, recording a MAPE of 50.09% even at the 1-month horizon. The findings demonstrate that while traditional trend-based models struggle with the extreme volatility of freight markets, tree-based machine learning algorithms excel at capturing complex, non-linear price movements.

Keywords

• Time series forecasting
• Facebook Prophet
• XGBoost
• SARIMA
• freight index
• ISTFIX

İstanbul Navlun Endeksinin Tahminlenmesi: Çoklu Zaman Aralıklarında SARIMA, XGBoost ve Facebook Prophet Modellerinin Karşılaştırmalı Analizi

Abstract

Doğru navlun oranı tahmini, denizcilik paydaşlarının oynak piyasa koşullarında bilinçli kararlar alabilmesi açısından kritik öneme sahiptir. Bu çalışma, İstanbul Navlun Endeksi'nin (ISTFIX) tahminlenmesinde üç farklı modelin, Mevsimsel Otoregresif Bütünleşik Hareketli Ortalama (SARIMA), Facebook Prophet ve XGBoost, performansını karşılaştırmalı olarak değerlendirmektedir. ISTFIX, Karadeniz ve Akdeniz güzergâhlarındaki küçük tonajlı koster ve mini dökme yük gemisi pazarlarının temel göstergesi niteliğindedir. Analizde, 7 Ocak 2008 ile 17 Mart 2025 tarihleri arasını kapsayan haftalık ISTFIX verileri kullanılmaktadır. Model performansı; 1 ay, 3 ay, 6 ay ve 1 yıllık olmak üzere dört farklı tahmin ufkunda kayan pencere yöntemiyle test edilmiş; MAPE, MAE ve RMSE ölçütleriyle değerlendirilmiştir. Bulgular, XGBoost'un 1 aylık aralıkta %6,12 MAPE değeriyle en yüksek kısa vadeli doğruluğa ulaştığını ortaya koymaktadır. SARIMA, tüm tahmin aralıklarında %7,16 ile %26,63 arasında tutarlı bir performans sergilemiştir. Prophet ise hafıza bağımlılığı ve yüksek oynaklık gösteren ISTFIX yapısını yakalayamamış; 1 aylık aralıkta bile %50,09 MAPE değeriyle yetersiz kalmıştır. Elde edilen bulgular, navlun fiyatlarındaki yüksek dalgalanmaların Prophet gibi trend odaklı modellerle tahmin edilmesinin zorluğunu göstermektedir. Buna karşılık, XGBoost gibi ağaç tabanlı (tree-based) makine öğrenmesi yöntemlerinin, piyasanın karmaşık ve geçmişe bağımlı yapısını yakalamada çok daha başarılı olduğu sonucuna varılmıştır.

Keywords

• Zaman serisi tahminlemesi
• Facebook Prophet
• XGBoost
• SARIMA
• navlun endeksi
• ISTFIX

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