Machine Learning Models for Container Demand Forecasting: A Practical Guide

Shipping in the modern world has become more challenging than ever before. Its wild swings cannot be followed by old-school forecasting. The global volumes reached 15.4 million TEU in January 2025. That constitutes a pretty good 5.8 percent increase as compared to the previous year.

Here’s the thing as IT experts working with logistics clients across the globe, we see these forecasting headaches play out daily. Supply chain chaos leads to missed deadlines and blown budgets.

But machine learning changes the game for predicting demand accurately. It crunches multiple data streams to spot patterns humans miss. Container demand forecasting shifts from educated guessing to precision planning. This blog breaks down the ML models that deliver results.

Knowing Container Demand Forecasting

Let us first nail down the basics before delving into machine learning models. Unless you know what you are predicting and the reasons why it is important, then you are preparing to succeed.

What is Container Demand Forecasting?

Container demand forecasting is a prediction of the number of containers and when you will require one. It is the foundation of smart supply chain management, which can aid firms in avoiding shortages, as well as waste. Here’s what makes it essential:

• Definition: Anticipating future container volumes based on multiple data inputs.
• Significance: Eliminates unproductive shelfs, minimizes storage, maximizes resource utilization.
• Notable metric: TEUs (Twenty-foot Equivalent Units) which is the standard metric used in the industry.

The Conventional Vs. Machine Learning approaches

The traditional approaches were based on the previous year and expected trends to occur again. But shipping markets move too fast for that now. Here’s how the approaches stack up:

• Traditional limitations: Static models, historical data only, can’t adapt quickly
• ML advantages: Dynamic predictions that evolve with new data
• Multi-source integration: Pulls weather, economics, and social trends simultaneously
• Granular insights: Spots correlations human analysts miss

Defining Your Forecasting Objectives

Different timelines serve different business needs in your container demand forecasting strategy. Consider these planning horizons:

• Short-term (day-to-day/week to week): Work planning, personnel management, daily route optimization.
• Long-term (monthly/yearly): Strategy, fleet investments, decisions regarding expansion of facilities.

Critical Data Sources Required for Container Demand Forecasting

Machine learning models are as good as the data they will be fed. The optimal combination of internal and external data sources develops predictability which cannot be matched with the conventional techniques.

Internal Data

Your company’s historical records form the foundation of any forecasting model. Consider it your functional DNA. They normally include past (past quarter, past year) volumes and TEU records of past years and quarters.

• Historical container volumes and TEU records from previous quarters and years
• Shipping routes and established patterns showing seasonal variations
• Operational events and disruptions that affected the schedule of delivery

Economic Indicators

Shipping demand is impacted by macro-economic factors in very strong ways. The China economy grew at 5.4% per annum in Q4 2024, indicating that the growth of GDP is directly proportional to trade. Key indicators include:

• Rates of GDP growth and inflation that are indicators of economic wellbeing
• Currency fluctuations and exchange rates which impact trade costs
• Export/import trade volumes reflecting global commerce activity

External Events and Disruptions

Significant upheavals transform shipping patterns at night and have to be constantly monitored. The maritime trading was reduced by 3.8% in 2020 as compared to 2019 due to the COVID-19 pandemic, with the schedule reliability decreasing to 30% and waiting times skyrocketing in the following years. Watch for:

• Health crisis and pandemic implications on world trade
• Trends in congestion of ports that form bottlenecks in significant hubs
• Alterations in the policy and trade regulations which change shipping lanes

Weather and Seasonal Data

Weather isn’t just small talk, it’s serious forecasting data here. Asia-pacific routes are always disrupted by typhoon season, and North Atlantic shipping is affected by winter storms. Track these factors:

• Retail-based seasonal shipping preferences
• Delay by weather-related route disruptions
• Temperature impact on demand for refrigerated containers

Social and Market Factors

The demand in the container is induced by consumer behaviour in a manner, which surprises most forecasters. In May 2024, the volume of container trade was 74 million TEUs, which is an increase of 7.5 percent over 2023, indicating the effect of the ongoing consumer demand. Monitor these trends:

• The seasonal demand peaks during the major shopping seasons such as the Black Friday
• Consumer attitude analysis forecasting consumer expenditure trends
• Social media trend indicators revealing emerging product demands

Machine Learning Model Types for Container Demand Forecasting

This is where we start the real stuff, the real models which run the modern container demand forecasting. The sweet spot with each method is that one should select the correct method based on the complexity of their data and the business requirements.

Traditional Statistical Models

ARIMA/SARIMA

AutoRegressive Integrated Moving Average ARIMA and an adjusted SARIMA have been decades-long workhorses in forecasting. These models are excellent when your data follows obvious and repetitive trends across time. Here’s what you need to know:

• Ideal applications: Monthly/ quarterly container volumes with regular seasonal ups and downs.
• Limitations: Problems with non-linear relationships between variables or abrupt changes in the market.
• When to use statistical models: Your data is clean, you have limited computing resources or you have stakeholders who want to understand the exploratory predictions.

Supervised Machine Learning Models

Linear Regression

Think of linear regression as your baseline, it’s simple, fast, and easy to explain. This model draws straight-line relationships between your input features and container demand. Consider these aspects:

• Baseline model benefits: Quick to train, highly interpretable for business teams.
• Strengths: Works well when relationships are actually linear, provides clear coefficient insights.
• Weaknesses: Fails to capture complicated weather, economics and demand dynamics.
• Use cases: Early proof-of-concept or when the stakeholders require clear forecasts.

Random Forests and Boosted Decision Trees

This is where ensemble learning is very effective in the context of container forecasting.These models build hundreds of decision trees and combine their predictions. Research shows they deliver consistently high accuracy for shipping demand. Key advantages include:

• Ensemble learning power: Combines multiple weak predictors into one strong model.
• High accuracy: Handles non-linear relationships and interactions between features automatically.
• Feature importance: Shows which variables matter most like GDP growth versus weather patterns.
• Strong performance: Not susceptible to overfitting as compared to single decision trees.

Support Vector Regression (SVR)

SVR assumes a different strategy, whereby it identifies the ideal boundary that models your data patterns. It is especially applied in cases of complex and non-linear relationships. Applications worth noting:

• Pattern recognition: Identifies intricate relationships between multiple demand drivers.
• Flexibility: Works with different kernel functions to match your data structure.
• Demand prediction scenarios: Forecasting during volatile periods with multiple conflicting signals.

Deep Learning Models

Artificial Neural Networks (ANN)

The neural networks replicate the brain process of information processing by means of interconnected layers. Feed-forward ANNs are capable of learning quite intricate patterns in data on container demand. Here’s what makes them valuable:

• Non-linear pattern learning: Captures relationships traditional models miss entirely.
• Feed-forward architecture: Processes multiple inputs simultaneously for container availability predictions.
• Scalability: The more the data sources and historical records one has, the better.

Recurrent Neural Networks (RNN) and LSTMs

LSTMs (Long Short-Term Memory networks) are specifically designed for time-series forecasting. They remember past patterns while processing new information. These capabilities matter:

• Time-series specialization: Built to understand sequences and temporal dependencies.
• Long-term memory: Remembers seasonal patterns from years ago while adapting to recent trends.
• Sequential analysis: Perfect for volume forecasting where last month’s data influences this month’s predictions.

CNN-LSTM Hybrid Models

The CNNs in combination with LSTMs make a formidable forecasting machine. CNNs do the same with the spatial features and LSTMs with the time. This hybrid approach delivers:

• Feature extraction: CNNs automatically identify important patterns across multiple data sources.
• Time-series strength: LSTMs then use these features for accurate future predictions.
• Multi-variable handling: Integrates weather, economics, and operational data simultaneously for superior accuracy.

Hybrid and Novel Approaches

Prophet

Meta’s Prophet tool makes container demand forecasting accessible without deep ML expertise. It automatically handles the tricky parts of time-series analysis. Key features include:

• Automated detection: Identifies trends, seasonality, and special events without manual tuning.
• Holiday effects: Incorporates Chinese New Year, Christmas, or regional holidays affecting shipping.
• Flexibility: Easily adds custom events like port strikes or policy changes.

Hybrid Model Combinations

In other cases, a combination of techniques is the best solution and works optimally. Hybrid ARIMAX-LSTM is already being used in shipping forecasting. Here’s when they work best:

• Statistical foundation: ARIMAX captures baseline seasonal patterns and external variables.
• Deep learning enhancement: LSTM layers add ability to learn complex non-linear relationships.
• Best of both worlds: Statistical interpretability meets deep learning power.
• Use scenarios: High-stakes forecasting where accuracy justifies additional model complexity.

Step-by-Step Implementation Guide

Ready to make container demand forecasting your superpower? Let’s walk through six steps to build a machine learning model that predicts container needs like a pro, all while keeping it as easy as ordering takeout.

Step 1: Nail Down Your Goals

First, figure out what you’re chasing. Need daily forecasts to schedule dock workers? Or yearly predictions to plan fleet upgrades? Pinpoint your timeline and tie it to what your business needs, less waste, fewer delays, more high-fives from the boss.

Step 2: Grab the Right Data

Mentalize about data as your forecasting ingredients. Drag historical TEU figures, trade routes and fat external carrots such as GDP data or weather forecasts. Clean it up, toss out weird outliers (like that one-off port strike) and standardize everything so your model doesn’t choke on mismatched numbers.

Step 3: Get Creative with Features

Now, turn raw data into gold. Create “lag features” to track past demand (like last month’s TEUs). Bundle up weekly volumes to spot trends. Mix in external stuff, like holiday shopping spikes or trade policy shifts. This is where you make your data sing for container demand forecasting.

Step 4: Pick and Train Your Model

Time to choose your weapon, start simple with linear regression or go fancy with LSTMs for time-series magic. Try a couple of models (Random Forests, Prophet, any of them) and find one that hits it. Imagine that you are trying shoes, see which fits your data best.

Step 5: Test and Tweak

Monitor the performance of your model using statistics such as RMSE or MAE (unaffectionate expressions of the same thing as how close are your predictions?). Adjust parameters, such as learning rates, and introduce extras, such as holiday influences. Continue to sharpen, until you have forecasts that are worthy of admiration.

Step 6: Launch and Keep an Eye Out

Get your model live, hooked into your supply chain system. Watch it like a hawk, retraining with fresh data (like new TEU reports) to stay accurate. Schedule updates to handle surprises like storms or trade wars, keeping your container demand forecasting game strong.

Special Focus: Empty Container Forecasting

Container demand forecasting is puzzle solving with missing pieces. The lack of full containers is difficult to monitor, as the repositioning is unpredictable, and the congestion is very high in the ports, presenting logistics planners with specific problems. To address this, game changers such as Mixture Density Networks (MDNs) predict the probability distributions of where empties can be found. Here’s how it works:

• Historical Journey Data: Analyze past container movements to spot repositioning patterns.
• Location and Availability: Forecast where empties will be and when they’re free.
• Integration: Blend empty container predictions with overall container demand forecasting for seamless supply chain planning.

This approach cuts costs and keeps containers moving efficiently.

Stop Losing Money and Utilize Container Demand Forecasting Now

Sick of supply chain chaos killing your profits? Container demand forecasting powered by machine learning delivers pinpoint accuracy, slashing waste and boosting efficiency. Intech’s Vessel Planner is your secret weapon. It plans 1,000 container moves in just 1.5 minutes and improves crane efficiency by 10%. Forget guesswork.

Embrace data-driven wins. Update your models regularly to outsmart disruptions like port congestion. Inefficiencies shouldn’t sink your terminal. Act now. Connect with Intech to revolutionize your container demand forecasting and turn your operations into a lean, profit-pumping machine. Don’t wait. Your competitors won’t.

Frequently Asked Questions on Container Demand Forecasting