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Overview

What is Uni-Variate Forecasting?​

Uni-Variate Forecasting predicts future values for a single time-series variable based on historical patterns. Our system combines multiple AI algorithms with automated model selection to deliver accurate forecasting solutions for business planning and decision-making.

Key Capabilities​

Supervised Agentic Modelliing (SAM) for AI-Powered Model Selection​

  • Automatic Analysis: System analyzes your time series data to identify patterns and characteristics
  • Intelligent Selection: AI chooses optimal models from 15+ available algorithms based on data properties
  • Multi-Model Approach: Combines multiple forecasting methods for improved accuracy and reliability

Advanced Forecasting Algorithms​

  • Statistical Models: ARIMA, SARIMA, Exponential Smoothing, Theta, TBATS
  • Neural Networks: N-HiTS, TFT, GRU, TCN, NBEATS, Informer
  • Machine Learning: XGBoost for pattern recognition
  • Specialized Models: Prophet algorithm for business time series
  • Simple Models: Moving averages for baseline comparisons

Data Processing​

  • Automated Analysis: Seasonality detection, trend analysis, and data quality assessment
  • Background Processing: Non-blocking execution with status updates
  • Hyperparameter Optimization: Automatic model tuning for optimal performance

Model Integrity & Reliability​

Automated Quality Assurance​

  • Cross-Validation: Rigorous out-of-sample testing ensures reliable performance estimates
  • Statistical Significance: Comprehensive validation of model accuracy and confidence intervals
  • Ensemble Consensus: Multi-model agreement reduces prediction uncertainty and improves reliability
  • Performance Monitoring: Real-time accuracy tracking with automatic quality alerts

Business Transparency​

  • Model Selection Rationale: Clear explanations of why specific algorithms were chosen for your data
  • Confidence Scoring: Reliability grades (High/Medium/Low) for informed decision-making
  • Uncertainty Quantification: Error bounds and prediction ranges for risk assessment
  • Quality Metrics: 25+ accuracy indicators translated into business-relevant insights

Trust Through Verification:

  • 99%+ Data Integrity: Comprehensive validation of input data quality and consistency
  • Multi-Algorithm Verification: Independent validation across different forecasting approaches
  • Business Logic Validation: Results checked against domain knowledge and business constraints
  • Automated Quality Gates: Only reliable models with proven accuracy reach production use

Core Workflow​

  1. Upload Data: Provide your time series data in CSV or Excel format
  2. Configure Forecast: Select variables to forecast and set time horizon
  3. AI Processing: System analyzes data and selects optimal models automatically
  4. Generate Forecast: Multiple models create predictions with confidence intervals
  5. Review Results: Access forecasts, charts, and performance metrics

Output Deliverables​

Forecast Results​

  1. Forecast Data: Standardized CSV with historical fit, validation, and future predictions
  2. Visual Analytics: Interactive charts showing actual vs predicted values with error bands
  3. Executive Summary: Professional PDF report with model comparisons and recommendations
  4. Performance Metrics: Comprehensive accuracy indicators including RMSE, MAPE, and reliability scores

Getting Started​

Data Requirements​

  • Minimum History: Sufficient historical data for reliable statistical analysis
  • Frequency: Weekly time series data
  • Format: Any structured data source (CSV, Excel, Database)
  • Categories: Support for multiple product/region/segment breakdowns

Quick Start Process​

  1. Connect Your Data: Upload files or connect to databases
  2. Select Variables: Choose the field to forecast and any category breakdowns
  3. Configure Parameters: Set forecast horizon and any specific requirements
  4. Launch Analysis: Our AI handles model selection and execution automatically
  5. Review Results: Access forecasts, charts, and executive summaries

Expected Timeline​

  • Analysis Phase: 2-5 minutes for dataset profiling and model selection
  • Execution Phase: 5-30 minutes depending on data size and selected models
  • Results Delivery: Immediate access to downloadable forecasts and reports