Agricultural commodity price forecasting
A forecasting system that compares time-series methods and presents uncertainty for selected Indian agricultural commodities and markets using public historical data.
Project definition
Problem statement
Agricultural commodity prices vary across markets and seasons, while public records may contain gaps, duplicates, unit differences, and sudden changes. A single forecast without data checks or uncertainty can be misleading.
The engineering problem is to build a reproducible forecasting pipeline that validates market data, compares simple and advanced methods using time-ordered tests, and communicates the range and limitations of each forecast.
Project objectives
- Prepare reliable historical series for selected commodities, grades, units, and markets.
- Identify seasonality, trends, missing periods, and unusual price movements.
- Compare naive, statistical, and machine-learning forecasting methods.
- Evaluate forecasts through rolling-origin backtests without future-data leakage.
- Display prediction intervals, data quality, and model limitations alongside each forecast.
System design
System modules
Data importer
Loads approved public market-price records and preserves source date, market, commodity, variety, grade, unit, and price fields.
Quality processor
Detects duplicates, missing dates, invalid ranges, unit conflicts, and structural gaps before a series can be modelled.
Feature pipeline
Creates lag, rolling, calendar, seasonal, arrival-volume, and selected external features using time-safe transformations.
Forecast engine
Runs naive, seasonal, statistical, and selected machine-learning models under a common interface.
Backtesting module
Uses rolling cut-off dates to compare horizons, errors, interval coverage, and performance stability.
Forecast dashboard
Shows observed prices, forecasts, uncertainty bands, model comparisons, data-quality warnings, and downloadable results.
Methodology
System workflow
- 01Choose series
The user selects a prepared commodity, variety, grade, market, price field, and forecast horizon.
- 02Validate data
The system checks units, missing periods, duplicates, and outliers and displays the accepted preparation rules.
- 03Build models
Selected models train only on data available before each backtest cut-off.
- 04Compare forecasts
Errors and interval coverage are calculated across dates, horizons, markets, and models.
- 05Publish result
The dashboard presents the selected forecast with its history, range, accuracy record, and limitations.
Demonstration scenario
A user selects the modal price of a prepared commodity in one market. The dashboard shows cleaned historical data and seasonal patterns, then compares a seasonal baseline, a statistical model, and a machine-learning model. A rolling backtest identifies the best configuration and displays its next-period forecast with an uncertainty band.
Engineering
Technical architecture
- Web application
- Nuxt and Vue for market selection, historical charts, forecast comparison, quality warnings, and result export.
- Forecast API
- FastAPI for prepared datasets, model execution, backtests, forecast retrieval, and experiment records.
- Data layer
- PostgreSQL for source records, cleaned series, features, model versions, backtests, forecasts, and update logs.
- Modelling pipeline
- Python, Pandas, statistical models, and scikit-learn with time-aware features and replaceable forecast adapters.
- Uncertainty
- Model-specific or residual-based prediction intervals with measured empirical coverage in rolling backtests.
- Delivery
- Docker-based setup with prepared sample series, fixed experiment configurations, and reproducible evaluation commands.
Testing
Evaluation
Evaluation measures
- Mean absolute error, root mean squared error, and symmetric mean absolute percentage error
- Accuracy relative to naive and seasonal-naive baselines
- Prediction-interval coverage and average interval width
- Performance across commodities, markets, horizons, and seasons
- Sensitivity to missing records and unusual price movements
- Data refresh, model execution, and dashboard response time
System boundaries
- The prototype covers only selected commodities, markets, price fields, and forecast horizons.
- Forecasts are experimental estimates and not trading, procurement, or farming advice.
- Unexpected policy, weather, supply, or market events may fall outside the historical patterns.
- The source date and last successful data update are shown with every result.
Included
- 01Commodity, market, and historical-price dashboard
- 02Data-quality, feature, forecasting, and backtesting pipelines
- 03Model comparison and prediction-interval visualisations
- 04Prepared public data, forecast experiments, and accuracy results
- 05Complete source code in a private GitHub repository
- 06Complete project documentation with synopsis, abstract, methodology, data-flow diagrams, forecast results, screenshots, and conclusion
- 07Setup and usage guide
Project record
No buyer information is collected on this page.
- Permanent project ID
- GP-DA-01YEK2O
- Catalogued
- 21 Aug 2026
- Completed
- Pending
- Verified
- Pending
- Demonstration
- Added when ready
Handover
After purchase
- 01Payment is confirmed
The project is marked unavailable and cannot be purchased again.
- 02Repository access is granted
The buyer's submitted GitHub account receives access to the private repository.
- 03The purchase record is delivered
The certification sheet is prepared from the reviewed buyer details and sent privately by email.