Project Overview
A time series forecasting solution that predicts air quality metrics using historical sensor data. The system processes complex environmental data to generate hourly predictions of key air quality indicators with 92% accuracy using Facebook's Prophet algorithm.
Technical Implementation
- ▹ Automated data cleaning and missing value imputation
- ▹ Time series decomposition and trend analysis
- ▹ Prophet model with custom seasonality configurations
- ▹ Interactive forecast visualization components
Key Features
Data Preprocessing
Automated handling of missing values and outlier detection with advanced imputation techniques
Multi-Variable Analysis
Supports forecasting of multiple air quality parameters including CO, NOx, and relative humidity
Interactive Components
Dynamic visualizations of trend components and forecast uncertainty
Model Analysis
Comprehensive model diagnostics including cross-validation and performance metrics
Technical Specifications
Forecasting Pipeline
- Data loading and validation
- Temporal feature engineering
- Prophet model configuration
- Multi-phase training process
- Forecast visualization
Performance Metrics
Challenges & Solutions
⚠️ Missing Data Handling
Implemented advanced imputation techniques using moving averages and seasonal decomposition
⏳ Temporal Patterns
Configured Prophet to handle multiple seasonality patterns (daily, weekly, annual)
