# Analyzing and Forecasting Vessel Traffic Through the Panama Canal: A Comparative Study
## 1 Introduction
[17] applied the ARIMA model to predict the monthly volume of maritime traffic at the port of Incheon, South Korea. Their findings showed that this model outperformed the exponential smoothing method by more accurately capturing the seasonal variations in vessel traffic. Similarly, [PERSON]. [18] employed the SARIMA model to analyze the time series of maritime traffic flow at the port of Jingzhou, demonstrating that this model adequately represents the seasonality present in the monthly vessel flow. In recent years, modern approaches based on neural networks and deep learning techniques have been adopted, allowing for the modeling of nonlinear [19] and complex dynamic relationships, thereby improving forecasting accuracy across various fields. Unlike traditional statistical models, which assume linear relationships, the NNAR is capable of capturing nonlinear patterns present in the data, such as seasonal fluctuations, trends, meteorological phenomena, and irregular global-scale behaviors. For example, [PERSON]. [20] validated the use of artificial neural networks as a complementary approach to classical models in maritime traffic forecasting. Along the same lines, [PERSON]. [16] implemented seasonal SARIMA models and neural networks to forecast container ship traffic between 2020 and 2025 at the port of Rajaee, Iran's largest port, concluding that neural networks provided superior predictive performance compared to the SARIMA approach. Likewise, [PERSON]. [21] analyzed the maritime cargo traffic series in Spanish ports between 1997 and 2017, employ ing both linear and nonlinear univariate models, such as SARIMA, exponential smoothing, and neural networks. Based on their results, the authors concluded that prediction accuracy varies depending on the type of cargo transported, with linear models generally outperforming nonlinear ones. Furthermore, [PERSON]. [22] proposed a hybrid approach combining empirical mode decomposition with traditional neural networks to predict high- and low-frequency components in maritime traffic time series. In the same vein, [PERSON]. [23] applied neural networks to forecast monthly traffic in the Suez Canal, achieving greater accuracy than ARIMA models. Similarly, [PERSON]. [24] implemented artificial intelligence models, specifically neural networks, to estimate maritime traffic, tackling the complexity of flow data in ports and the challenge of capturing dynamic spatiotemporal dependencies. In a recent advancement, [PERSON]. [25] proposed a dynamic spatiotemporal model called the Temporal Convolutional Network-Bidirectional Gated Recurrent Unit-Pearson Correlation Coefficient-Graph Attention Network (TG-PGAT), specifically designed to address the complexity of maritime traffic flow in ports. This model demonstrated greater prediction accuracy and stability compared to other classical forecasting methods used in this field. Despite advancements in maritime traffic forecasting, most studies have concentrated on ports and shipping lanes outside the interoceanic context of the Panama Canal. Given the strategic significance of this waterway at both regional and global levels, this article aims to identify a statistical time series model capable of accurately forecasting the number of vessels transiting the Panama Canal. To date, no prior research has been found that applies time series methodologies to model vessel traffic through the Panama Canal--except for the work of [PERSON] et al. [26]. To address this, a comparison is proposed between traditional techniques--such as the seasonal SARIMA model and the Holt-Winters method--and artificial intelligence-based models, such as Neural Network Autoregressive (NNAR) models. This integration responds to the need to assess whether classical approaches, widely used for their interpretability and effectiveness in linear contexts, can be outperformed or complemented by more recent techniques capable of capturing nonlinear dynamics and complex patterns in the data.