SAMSUN LİMANI’NDA ELLEÇLENEN YÜKLERİN ZAMAN SERİSİ ANALİZİ VE TAHMİNİ

Yıl 2026, Cilt: 5 Sayı: 1, 118 - 138, 31.01.2026

Elvan Deniz

Öz

Samsun Limanı, Türkiye’nin Karadeniz kıyısında konumlanan ve kuzeye açılan ticaret ve lojistik akışlarda önemli rol üstlenen stratejik bir kapıdır. Bu çalışma, Samsun Limanı’nda 2012–2024 döneminde yıllık toplam elleçlenen yük miktarının gelişimini incelemekte ve 2025–2027 dönemi için kısa vadeli tahminler üretmektedir. Analizde resmî yıllık elleçleme verileri kullanılmış; hesaplamalar ve model karşılaştırmaları Minitab 17 yazılımı ile gerçekleştirilmiştir. Modelleme öncesinde temel tanısal kontrollerden yararlanılmış ve yöntemler arası karşılaştırma için tutarlı bir değerlendirme çerçevesi oluşturulmuştur.
Yöntem kapsamında doğrusal trend, parabolik trend ve üssel trend modelleri ile 3 ve 5 dönem hareketli ortalama, basit üssel düzeltme ve Holt çift üssel düzeltme yöntemleri uygulanmıştır. Modellerin performansı R², düzeltilmiş R² ve tahmin R² ile; tahmin doğruluğu ise MAPE, MAD ve MSD ölçütleriyle değerlendirilmiştir.
Bulgular, toplam yük hacminin 2012’de 8.910.426 ton iken 2023’te 14.176.568 tona yükseldiğini, 2024’te 12.747.789 tona sınırlı bir gerileme yaşandığını, ancak genel eğilimin artış yönünde olduğunu göstermektedir. Hata ölçütlerine göre doğrusal trend modeli en düşük MAPE (≈%4,81) ile öne çıkarken, Holt yöntemi de çok yakın performans sergilemiştir (MAPE ≈%4,85). Elde edilen 2025–2027 öngörüleri; kapasite planlaması, ekipman/işgücü tahsisi ve yatırım zamanlaması gibi operasyonel ve planlama kararları için veri temelli bir çerçeve sunmaktadır.

Anahtar Kelimeler

Samsun limanı , zaman serisi analizi , talep tahmini

TIME SERIES ANALYSIS AND FORECASTING OF CARGO HANDLING AT THE PORT OF SAMSUN

Öz

The Port of Samsun, located on Turkey’s Black Sea coast, serves as a strategic gateway linking domestic production areas with northern maritime routes and nearby markets. By handling diversified cargo associated with agriculture, industry, and energy, the port supports regional trade and logistics. Ongoing improvements in hinterland connectivity and capacity-enhancement investments have also strengthened its competitive position. In this context, reliable cargo-throughput forecasting is critical for capacity planning, resource allocation, and the timing of infrastructure and equipment investments.
This study analyzes the evolution of annual total cargo handled at the Port of Samsun over the period 2012–2024 and develops short-term forecasts for 2025–2027 using classical time-series forecasting methods. The analysis is based on official annual cargo handling statistics for the port. Because the dataset is complete and reported at yearly frequency, no missing-value treatment or data reconstruction was required. All estimations, diagnostics, and model comparisons were performed in Minitab 17 to maintain consistency across methods.
Prior to modelling, key assumptions for regression-type forecasting were assessed. The Anderson–Darling normality test was applied to evaluate distributional suitability, and results supported the normality assumption at conventional significance levels. In addition, Durbin–Watson statistics were used to check autocorrelation in regression residuals. Values close to 2 for both linear and quadratic trend specifications indicated no material serial correlation, supporting the reliability of trend estimation and inference.
To ensure a transparent and practically relevant comparison, seven forecasting approaches commonly used in port demand planning were implemented: linear trend regression, quadratic trend regression, exponential trend modeling, 3-period moving average (3MA), 5-period moving average (5MA), single exponential smoothing, and double exponential smoothing (Holt’s method). Model performance was evaluated using both goodness-of-fit measures (R², adjusted R², predicted R²) and forecast accuracy metrics (MAPE, MAD, MSD), allowing model selection based on explanatory power as well as predictive reliability.
Descriptive findings reveal a generally upward throughput trajectory with limited fluctuations. Total cargo expanded from 8,910,426 tons in 2012 to 14,176,568 tons in 2023, followed by a temporary decline to 12,747,789 tons in 2024. Despite this decrease, the longer-term pattern remains positive, indicating that the port’s functional role within regional logistics networks has broadened over time.
Comparative results show that both trend-based and smoothing-based methods capture the growth structure effectively at annual frequency. Based on the minimum-error criterion, the linear trend model is identified as the preferred baseline, achieving the lowest MAPE (≈4.81%) with competitive MAD and MSD values. Holt’s double exponential smoothing performs similarly well, producing a comparable MAPE (≈4.85%), suggesting that modelling both level and trend components provides a consistent representation of the port’s annual dynamics.
Forecasts for 2025–2027 point to continued growth. The linear trend model projects 14,510,497 tons (2025), 14,939,395 tons (2026), and 15,368,293 tons (2027). Holt’s method yields nearly identical values: 14,504,729 (2025), 14,933,561 (2026), and 15,362,394 (2027). Overall, the study offers a replicable forecasting workflow for ports relying on annual statistics, while noting limitations such as the lack of explicit exogenous drivers and the restricted sample length; future research may incorporate longer or higher-frequency series and driver-based models

Anahtar Kelimeler

Port of Samsun , time series analysis , demand forecasting

Kaynakça

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