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SAM Forecasting Technical Architecture

Overview​

SAM's Uni-Variate Forecasting system is built on a sophisticated enterprise architecture that combines AI-driven intelligence, scalable processing, and robust data management to deliver high-performance forecasting at scale.

System Architecture​

High-Level Architecture Diagram​

High-Level Architecture Diagram

Core Components​

1. Data Processing Layer​

  • File Parsing: CSV and Excel file processing with automatic time series recognition
  • Data Validation: Time series format validation and business rule verification
  • Feature Engineering: Lag features, rolling statistics, and trend decomposition
  • Data Preparation: Missing value handling and outlier detection

2. AI Intelligence Engine​

  • Model Selection: AI-driven evaluation and selection of optimal forecasting models
  • Data Characterization: Statistical analysis of time series properties and patterns
  • Performance Prediction: Expected accuracy and processing time estimation for each model
  • Ensemble Optimization: Intelligent combination of complementary forecasting approaches

3. Processing Engine​

  • Background Execution: Non-blocking processing with real-time status tracking
  • Multi-Model Processing: Parallel execution of selected forecasting algorithms
  • Hyperparameter Optimization: Automated parameter tuning using Optuna framework
  • Resource Management: Dynamic CPU/GPU allocation and memory optimization

4. Business Intelligence Layer​

  • Result Processing: Multi-model ensemble scoring with confidence assessment
  • Visual Analytics: Chart generation showing forecast trends and confidence intervals
  • Report Generation: Executive PDF reports with findings and business recommendations
  • Business Metrics: SPYA analysis, growth rate calculation, and stability scoring

5. Model Integrity & Quality Assurance​

  • Cross-Validation Engine: Rigorous out-of-sample testing and performance validation
  • Consensus Scoring: Multi-algorithm agreement assessment for reliability determination
  • Quality Gates: Automated checks ensuring only validated models reach production
  • Business Logic Validation: Results verification against domain knowledge and constraints
  • Confidence Assessment: Real-time reliability scoring and uncertainty quantification

SAM Forecasting Processing​

SAM Forecasting Processing Architecture

Data Flow Architecture​

Processing Pipeline​

Data Processing Pipeline

Background Processing System​

Asynchronous Execution:

  • Non-Blocking Operations: User interface remains responsive during forecast processing
  • Status Monitoring: Real-time progress updates and processing transparency for users
  • Queue Management: Efficient handling of multiple concurrent forecasting requests
  • Error Recovery: Graceful handling of processing failures with automatic retry mechanisms