Price Forecasts & Historical Prices

With 400+ price forecasts that are up to 5x more accurate than existing solutions, plus historical price series, our pricing tools give you real answers, not black-box predictions.

Helios Price Forecasting

Helios AI’s price forecasts offer clear, reliable predictions for the next 12 months, with detailed explanations of what’s driving price changes. Plan smarter, negotiate better, and manage risk with confidence.

12-Month Forecasts & Deep Historical Context

Get monthly price predictions up to a year in advance and view detailed price histories back to 2020 to spot patterns, benchmark suppliers, and inform smarter decisions. Updated weekly.

Superior Accuracy

Our advanced machine learning models deliver predictions up to five times more accurate than standard industry methods, giving you greater confidence in every decision.

Detailed Price Explanations

Every price series is paired with an explanation to help you understand not just where prices are going, but why, with clear breakdowns of drivers like climate risks and supply trends.

Granular Coverage

Access over 400 detailed price series broken down by commodity, country, region, and variety, giving you precise, relevant forecasts to support smarter sourcing and planning.

Our Methodology

Helios AI’s price forecasts are built on a robust, multi-layered modeling approach designed for the complexities of global agricultural markets.

We combine weather data and forecasts, crop-specific machine learning models, historical pricing trends, detailed supply-and-demand information, and real-time geopolitical risk monitoring. Our proprietary classification system uses 81 distinct climate archetypes, while advanced techniques like time series analysis, deep learning, and convolutional neural networks deliver highly accurate predictions — on average 108% more accurate than standard baseline models. Our pricing data currently comes from USDA and other trusted public sources, with plans to expand to additional private sources soon.

Have more questions? Check out our FAQ section below.

Designed for our agricultural commodity procurement teams

Key features of CommodiTrack’s Price Tab include:

  • Switch units easily: KG, LB, or MT.

  • View prices globally, by country of origin, or even by region

  • Drill into varieties: Compare specific types of apples, grapes, avocados, and more.

  • Analyze historical trends back to 2020.

  • See forecasts by month for the next 12 months.

  • Read price explanations that go into the drivers behind the forecast and how it relates to seasonal trends.

  • Download data and export charts and tables for your own analysis

  • Overlay climate risk factors to see how weather events and climate risk might impact prices.

Calling All Distributors, Wholesalers, and Retailers

Our price series & forecasts are for procurement teams that buy, sell, or plan around agricultural commodity prices:

  • Manufacturers securing inputs and controlling production costs

  • Distributors/wholesalers negotiating contracts and managing supplier relationships

  • Retailers planning promotions, stocking strategies, and pricing for end customers are just a few examples

How they will make your life easier:

Negotiate with confidence: Use real, granular price data, forecasts and explanations to justify contract terms.

01

Plan smarter & manage risk: Spot upcoming price volatility early so you can hedge, diversify suppliers, or adjust plans. Time purchases for maximum savings.

02

Benchmark supplier bids: Instantly compare what others are paying and hold your vendors to account.

03

Forecast demand impacts: Anticipate how price changes will affect your end-product pricing and customer demand.

04

FAQs

  • Our initial launch is covering fruits, vegetables, and other critical commodities. Examples include:

    • Apples (Gala, Honeycrisp, Granny Smith, Red Delicious, by country and region)

    • Avocados (Hass, Greenskin types, from Mexico, Peru, US, and others)

    • Bell peppers, watermelons, mangoes, onions, potatoes, and dozens more

    However, in the next few months, we expect to increase our coverage dramatically, including:

    • Expanding from 400+ to over 2,500 forecasts, covering more crops, countries, and varieties.

    • Exchange-traded commodity cash prices and basis prices.

    • Processed foods and byproducts like oils, meals, and powders.

    • More price types, including other incoterms beyond FOB (like wholesale and farmgate prices).

  • Our price predictions are up to 5x more accurate than industry standards, such as the naive basis, which is based on seasonality and basic supply/demand inputs. On average, our forecasts perform 108% better than standard models.

  • Our prices are predominantly sourced from government reporting agencies and other public sources such as the United States Department of Agriculture (USDA). We do plan on expanding out to include additional public and private sources, such as other price reporting agencies.

  • We evaluate forecast accuracy using standardized error metrics, primarily focusing on Mean Absolute Error (MAE) adjusted for commodity price levels. Each forecast is benchmarked against conventional prediction methods to verify performance advantages. We conduct error analysis across different market conditions to ensure consistent reliability.

    • Weather data and forecasts: ensemble modeling approach to track and forecast weather combines 10 years of historical climate risk trends, seasonality factors, and forecasted weather data (including forecasted climate risk trends)

    • Crop-specific machine learning models: based on our proprietary classification system composed of 81 distinct climate archetypes

    • Historical price trends: Our forecasting models integrate extensive historical commodity pricing data to identify and analyze recurring market patterns and correlations with climate-related disruptions

    • Supply and demand information: We continuously gather and analyze detailed global agricultural data, including production volumes, inventory statuses, trade flows, and consumer demand patterns.

    • Geopolitical risk monitoring: we monitor geopolitical developments by analyzing data scraped from 250,000 global news sources every 15 minutes, enabling us to identify and factor geopolitical risks into our forecasts.

    • Time series analysis: a statistical technique used to analyze data points collected over time to identify patterns

    • Deep learning models: deep learning to analyze climate variables, soil conditions, and historical yield patterns, identifying correlations that traditional statistical models might miss

    • Anomaly detection models: these identify deviations from expected patterns in climate, trade, and supply chain data.

    • Ensemble forecasting techniques: Ensemble forecasting combines multiple models to improve prediction accuracy and robustness.

  • Our historical price series, forecasts and explanations get updated weekly overnight on Sundays.