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Methodology
The foundation of our forecasting methodology lies in the careful selection of data that significantly influences the commodity under analysis. Data refers to both statistical data—including quantitative economic and market indicators—and news-based insights, which we monitor to conduct sentiment analysis. Together, these provide a comprehensive view of the factors influencing commodity prices.
Once the key drivers are finalized, the next step is to gather relevant and credible data aligned with each driver to support accurate and robust analysis. We primarily source data from BigMint’s proprietary database, government publications, and industry association reports. These datasets are compiled and verified by our in-house team to ensure validated and up-to-date figures essential for forecast analysis.
Once the relevant data is collected, the next critical step is Database Creation. This stage transforms raw, unstructured datasets into a clean, consistent, and analysis-ready format. Preprocessing addresses noise, inconsistencies, and missing values; visualization identifies patterns and outliers; and descriptive analysis extracts meaningful insights. As part of this step, we also prepare the data environment to incorporate sentiment analysis, bridging the gap between structured market data and qualitative news insights.
With a clean, structured, and enriched dataset in place—including both statistical drivers and sentiment-derived inputs—the next phase focuses on deriving actionable forecasts. Feature engineering strengthens the model’s ability to detect trends and cycles, while model selection identifies the most suitable forecasting algorithms. NeuralForecast models capture complex non-linear relationships, StatsForecast models offer transparency, and tree-based regression models flexibly incorporate engineered covariates, including sentiment scores.
| Metrics | Evaluation Metrics Value | Billet Raipur | HR Plate Mumbai | Rebar IF Route Mumbai | Chhattisgarh Fines | Odisha Index | Pellet | Rebar BF Route Mumbai | Rebar IF Route Raipur | Wire Rod Raipur | CRC | GP | GI | HMS 80:20 Turkey | HMS 80:20 Mumbai2 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Accuracy Range 1M | High price-level accuracy Min 95% | >95% | >98% | >95% | >95% | >95% | >95% | >95% | >95% | >95% | >98% | >98% | >98% | >98% | >98% |
| Accuracy Range 2M,3M | High price-level accuracy Min 95% | >92% | >96% | >92% | >92% | >92% | >92% | >92% | >92% | >92% | >92% | >96% | >96% | >96% | >96% |
| Delta Range in % | 2 to 3 % | ±3% | ±3% | ±3% | ±3% | ±3% | ±3% | ±3% | ±3% | ±3% | ±3% | ±3% | ±3% | ±2% | ±4% |
| Mean Absolute Percentage Error (MAPE) | MAPE ≤ 2%, which is typically acceptable | ≈2% | ≈2% | ≈2% | ≈2% | ≈2% | ≈2% | ≈2% | ≈2% | ≈2% | ≈0.61% | ≈0.61% | ≈0.70% | ≈2% | ≈2% |
The final step is forecast validation. The evaluation process follows a robust out-of-sample validation approach, where the most recent portion of the dataset is held out as a test set. Standard metrics such as RMSE and MAPE are computed, complemented by visual inspection of predicted versus actual prices. If evaluation results are unsatisfactory, the model development loop is revisited with alternative feature sets, model architectures, or ensemble strategies. Once validated, forecasts are translated into intuitive, decision-friendly formats to communicate projected trends and drivers to stakeholders.
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Disclaimer:
BigMint has prepared this information with care and provides it for general reference only. It should not be used as the sole basis for trading, investment, or other critical decisions. The views expressed may come from various contributors and do not necessarily represent those of BigMint or its management.
BigMint, including its employees, directors, and aliates, is not responsible for any losses, damages, or actions resulting from the use or reliance on this information. Price forecasts are generated using a defined methodology based on selected factors from available data. Some relevant factors may not be included due to data availability or suitability, and the model does not account for unexpected events, market disruptions, or sentiment-driven changes
Users should keep these limitations in mind when interpreting the forecasts.