Helios offers export, wholesale, and farmgate cash/spot price forecasting & historical prices for 50+ fresh commodities & its derivatives.
We use crop-specific machine learning models, and they prioritize predictive accuracy with results up to 20 × better than traditional benchmarks.
We can do this by leveraging our proprietary climate risk metrics as climate becomes a leading price driver + our cutting-edge AI capabilities.
About our price forecasts:
Our cash price forecasts offer clear, reliable predictions for the next 12 months, with detailed explanations of what’s driving price changes. They are updated weekly.
Our historical prices start in 2020 and are updated daily, ensuring they are as up to date as possible.
Our price forecasting models have accuracy ratings up to 98.2% and vary by price series.
Prices are sourced mainly from public sources and various government websites, such as the USDA or the EU Commission.
When a dataset isn’t large enough for a precise numerical forecast, we provide a pricing outlook that highlights price trends alongside growing season and climate data.
Key inputs into our forecasting:
Climate data and forecasts - Our ensemble models that track and forecast weather combine 10 years of climate trends, seasonality, and forecasted weather data to predict supply and yield — key drivers of price.
Historical pricing trends - Our forecasting models integrate extensive historical pricing data to identify and analyze recurring market patterns and correlations with climate-related disruptions.
Supply and demand data - We monitor global production volumes, inventory, trade flows, and demand patterns to spot imbalances and anticipate their impact on commodity prices.
News monitoring - Every 15 minutes, we ingest and analyze 250,000 global news sources to track geopolitical, supply, and trade shifts—factoring emerging risks directly into our forecasts.
AI Techniques Utilized:
We utilize various AI techniques to generate our price predictions, including:
Time series analysis
Our platform applies time series analysis, a statistical technique used to analyze data points collected over time to identify patterns, to historical climate, yield, and trade data, enabling us to detect recurring cycles and long-term shifts in agricultural production. By decomposing time series data into trend, seasonal, and irregular components, we can anticipate changes in crop yields based on past weather anomalies, temperature fluctuations, and precipitation patterns. This allows us to make more accurate predictions about future agricultural supply, providing stakeholders with insights into potential price movements before they occur.
Deep learning models
We leverage deep learning to analyze climate variables, soil conditions, and historical yield patterns, identifying correlations that traditional statistical models might miss. Deep learning models such as recurrent neural networks (RNNs), are designed to recognize complex, nonlinear relationships in large datasets. These models can process vast amounts of real-time weather and satellite data, allowing us to detect hidden signals that indicate upcoming agricultural disruptions. By capturing both short-term weather fluctuations and long-term climatic shifts, deep learning enhances our ability to predict yield variability and its impact on prices.
Anomaly detection algorithms
Anomaly detection algorithms identify deviations from expected patterns in climate, trade, and supply chain data. We use these algorithms to monitor global agricultural conditions in real-time, flagging unexpected weather events, disease outbreaks, or supply chain disruptions that could affect food production.
Ensemble forecasting techniques
Ensemble forecasting combines multiple models to improve prediction accuracy and robustness. Our platform integrates diverse forecasting approaches, including statistical models, deep learning, and climate simulations, to produce more reliable agricultural price predictions. Ensemble techniques reduce the risk of overfitting to any single data source or methodology. This multi-model approach enhances the stability and reliability of our forecasts.
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