A common workflow for time series forecasting in Consensus involves exporting data to a spreadsheet for manipulation before modeling. I've conducted a comparative analysis of this method versus using the platform's native forecasting tools, focusing on reproducibility, error propagation, and statistical integrity.
**Native Forecasting (Consensus)**
* **Controlled Environment:** All transformations (e.g., differencing, scaling) are defined within the pipeline configuration. This creates a single, reviewable source of truth for the data preprocessing applied to both historical data and future forecasts.
* **Automated Backtesting:** The platform's structure inherently links the model specification to its evaluation metrics. This minimizes the risk of accidentally using different data versions for training and validation.
* **Audit Trail:** Changes to the model or data pipeline are typically versioned, which is critical for diagnosing performance drift or replicating a specific forecast.
**Spreadsheet-Based Forecasting**
* **Manual Intervention Risk:** Each step—cleaning, aggregation, feature calculation—is a manual cell operation. This introduces a high probability of hidden errors, such as incorrect cell references or misapplied formulas, which are difficult to audit systematically.
* **Decoupled Validation:** The model (often a simple Excel trendline or an external library's output) is separated from the data preparation. It's common to see inconsistencies where the validation period uses a differently calculated feature than the training period.
* **Reproducibility Challenge:** Sharing a forecast requires sharing the entire spreadsheet and documenting every manual step. Even with careful notes, replicating the exact sequence of operations is error-prone.
The core issue is one of *guaranteed consistency*. Native tools enforce a consistent application of logic across the data lifecycle. The spreadsheet method, while flexible, relies on perfect manual execution, which statistical practice shows is a significant source of error. For any forecast that requires review or iteration, the native approach provides a more rigorous foundation.
Has anyone else performed a formal error analysis between these workflows? I'm particularly interested in cases where the results diverged significantly and the root cause was traced to a procedural discrepancy in the spreadsheet.
prove it with data