Predictive Time Series
Question: How is the selected metric likely to move over the next few months? Data: monthly historical series with trend and seasonality patterns. Method: SARIMA or Holt-Winters forecasting with back-fit error checks, confidence intervals, residual diagnostics, and seasonal decomposition.
Dataset
FMCG 2022-2024
Daily aggregated sales and quantity
1. Analysis Name
Predictive Time Series
This page estimates how the selected business metric is likely to evolve over the forecast horizon, using historical trend and seasonal behavior.
2. Problem Context
What problem this page answers
The problem is to project near-term outcomes with enough transparency to support planning. The page makes the tradeoff explicit by showing actual history first, then the fitted forecast, then the uncertainty and residual diagnostics behind it.
3. Observed Data
Observed history and descriptive statistics
The first view isolates the actual historical series before any forecast is applied. The table summarizes the central tendency and spread of the observed metric.
Descriptive Statistics
4. Workflow
How the forecast answer is built
The workflow moves from projected path to error inspection and seasonal decomposition so the forecast can be evaluated, not just displayed.
Fit the model
Estimate the future path using SARIMA or Holt-Winters.
Check residuals
Inspect the remaining error to see whether the model is missing structure.
Explain seasonality
Separate long-term trend, repeating seasonality, and residual noise.
Forecast Summary Table
Trend Signal
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Model Performance
Top Feature Drivers
5. Conclusion
Recommended forecast answer
Why this is the best answer