Objective
To predict future network traffic volume based on historical data using time-series analysis and forecasting. This project demonstrates how to build robust time-series models that can accurately forecast future bandwidth demand, enabling proactive network management and capacity planning.
Business Value
Accurate traffic volume forecasting provides critical advantages for network operations:
- Capacity Planning: Justifies and guides network upgrades by predicting when and where future capacity will be needed
- Resource Allocation: Dynamically allocate resources in virtualized network environments based on predicted demand
- Congestion Prevention: Proactively identify future peak usage periods to prevent service degradation and ensure high quality of experience
- Cost Optimization: Optimize infrastructure investments by accurately predicting future bandwidth requirements
- SLA Management: Ensure service level agreements are met by anticipating demand spikes
Core Libraries
- pandas: For robust time-series data manipulation and preprocessing
- matplotlib & seaborn: For comprehensive data visualization and forecast result analysis
- prophet: Facebook's powerful and user-friendly forecasting library designed for time-series with strong seasonal patterns
- scikit-learn: For model evaluation metrics and performance assessment
- numpy: For numerical computations and array operations
Dataset
Primary Dataset: Internet Traffic Time Series Dataset from Kaggle (user: shenba)- Description: Daily traffic data from an ISP with clear temporal structure
- Key Features:
- Historical network traffic measurements
- Clear seasonality patterns (daily, weekly, yearly)
- Real-world noise and anomalies
- Suitable for demonstrating multiple forecasting horizons
Alternative Dataset: Hourly Energy Consumption (Kaggle Link)- Why it's suitable: Energy consumption patterns mirror network traffic with similar seasonal behaviors
- Advantages: Strong multi-level seasonality, long-term trends, realistic noise patterns
Implementation Steps
Step 1: Environment Setup
# Create project environment
mkdir network-traffic-forecasting
cd network-traffic-forecasting
python -m venv venv
source venv/bin/activate
# Install required libraries
pip install pandas matplotlib seaborn prophet scikit-learn numpy jupyterlab kaggle
# Start Jupyter Lab
jupyter lab
Step 2: Data Acquisition and Loading
- Set up Kaggle API credentials
- Download Internet Traffic Time Series dataset
- Load and inspect data structure
- Handle missing values and data quality issues
Step 3: Exploratory Data Analysis
- Visualize historical traffic patterns
- Identify seasonality components (daily, weekly, yearly)
- Analyze trend patterns and outliers
- Understand data distribution and statistical properties
Step 4: Data Preprocessing
- Convert datetime columns to proper format
- Set datetime as index for time-series operations
- Prepare data in Prophet's required format (ds, y columns)
- Split data into training and testing sets for evaluation
Step 5: Model Training
- Initialize Prophet model with appropriate seasonality settings
- Configure yearly, weekly, and daily seasonality parameters
- Fit the model to historical training data
- Validate model convergence and parameter estimation
Step 6: Forecasting and Prediction
- Create future dataframe for prediction horizon
- Generate forecasts with uncertainty intervals
- Extract prediction components (trend, seasonalities)
- Analyze forecast confidence intervals
Step 7: Model Evaluation
- Compare predictions against held-out test data
- Calculate performance metrics (MAE, MSE, RMSE)
- Assess forecast accuracy across different time horizons
- Validate seasonal pattern detection
Step 8: Results Visualization
- Plot historical data with forecast overlay
- Visualize forecast components (trend, seasonalities)
- Display uncertainty intervals and prediction confidence
- Create comprehensive forecast interpretation
Technical Implementation
The project uses Facebook's Prophet library, which automatically handles:
- Seasonality Detection: Identifies daily, weekly, and yearly patterns
- Trend Analysis: Captures long-term growth or decline patterns
- Holiday Effects: Accounts for irregular events affecting traffic
- Missing Data: Robust handling of gaps in time-series data
- Uncertainty Quantification: Provides confidence intervals for predictions
Success Criteria
- Data Processing: Successfully load, parse, and index time-series data using pandas
- Model Training: Prophet model trains without errors and converges properly
- Forecasting: Generate accurate forecasts extending 3-12 months into the future
- Visualization: Produce interpretable forecast plots showing historical data, predictions, and uncertainty intervals
- Component Analysis: Generate and explain forecast components (trend, seasonalities)
- Evaluation: Achieve reasonable performance metrics on held-out test data (RMSE < 15% of mean traffic)
Key Insights and Learnings
1. Seasonality Patterns: Network traffic typically shows strong weekly patterns (lower on weekends) and yearly patterns (seasonal variations)
2. Trend Analysis: Long-term traffic growth reflects business expansion and technology adoption
3. Uncertainty Management: Prophet provides realistic uncertainty intervals that grow with forecast horizon
4. Component Interpretation: Understanding trend and seasonal components enables better business decision-making
Next Steps and Extensions
Advanced Modeling
- External Regressors: Incorporate holiday calendars, promotional events, or economic indicators
- Model Comparison: Compare Prophet against SARIMA, exponential smoothing, or deep learning models
- Ensemble Methods: Combine multiple forecasting approaches for improved accuracy
Operational Integration
- Real-time Forecasting: Implement automated daily/weekly forecast updates
- Alert Systems: Create threshold-based alerts for predicted capacity issues
- Dashboard Development: Build interactive dashboards for network operations teams
Advanced Analytics
- Anomaly Detection: Identify unusual traffic patterns that deviate from seasonal norms
- Scenario Planning: Model different growth scenarios and their impact on infrastructure needs
- Multi-variate Forecasting: Incorporate multiple network segments or services simultaneously
Business Impact
This forecasting capability enables:
- Proactive Infrastructure Planning: Avoid costly emergency upgrades through predictive capacity management
- Budget Optimization: More accurate capital expenditure planning based on data-driven forecasts
- Service Reliability: Maintain high service quality by preventing congestion before it occurs
- Strategic Decision-Making: Support business growth planning with reliable traffic projections
Files Structure
003_Network_Traffic_Volume_Forecasting/
├── README.md # This comprehensive guide
├── notebook.ipynb # Complete implementation with Prophet
├── requirements.txt # Python dependencies
└── data/ # Dataset storage (create locally)
└── internet_traffic_data.csv