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Data
Intelligence

Our data intelligence framework transforms diverse raw data into structured analytical inputs, subject to internal review, used in forecasts, models and planning scenarios.

Diverse input sources flow into a central database and out to forecasts, charts, world maps and distributions

1. Input Data

The data used for forecasting trends in transportation, commodity and raw-material markets include historical time series processed using data normalization, statistical and econometric analysis, market-structure analysis, production-cost indicators, supply-and-demand factors and other relevant economic, commercial and industry-specific data.

Global data sources across a dotted world map

2. Data Processing

Forecasting of future commodity prices and transportation costs across different trade routes is carried out through the application of mathematical models used and further developed by the Company for the analysis of dynamic nonlinear systems, incorporating the principles of multi-criteria analysis and a Bayesian approach to probabilistic forecasting.

MULTI-CRITERIA ANALYSIS
Overlapping-sets diagram
  • Pareto Optimality
  • Multi-Attribute Utility Theory (MAUT)
  • Multi-Objective Optimization (MOO)
  • Soft Set Theory
BAYESIAN APPROACH
Probability distribution curve
  • Prior probability estimation
  • Relationship identification using neural-network and correlation analysis
  • Information updating and ranking
FORECASTING OUTPUT
Forecast trend lines
  • Price forecasts
  • Cost forecasts
  • Probabilistic scenarios
  • Exposure assessment

3. Proprietary Framework

All research, forecasts and analytical materials prepared by the Company use an analytical framework comprising mathematical models, statistical and econometric methods, data-processing algorithms, software applications and other analytical tools maintained and refined by Foristra.

MATHEMATICAL MODELS
STATISTICAL & ECONOMETRIC METHODS
DATA-PROCESSING ALGORITHMS
SOFTWARE APPLICATIONS
ANALYTICAL TOOLS