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Analyze another fileDecompose time series using Prophet's additive model with piecewise linear trend, automatic changepoint detection, Fourier-based seasonality (weekly/yearly), and uncertainty quantification for interpretable forecasting.
Use this when you need prophet-style trend and seasonality decomposition on your data.
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Built for: Analyst, data scientist, business user
Typical data source: CSV with relevant columns
Decompose time series using Prophet's additive model with piecewise linear trend, automatic changepoint detection, Fourier-based seasonality (weekly/yearly), and uncertainty quantification for interpretable forecasting.
Historical fit and future forecast with uncertainty intervals
Decomposed trend component with changepoints
Day-of-week seasonal effects
Annual seasonal effects across the year
Dates where the trend growth rate significantly changed
MAPE, MAE, RMSE, and other accuracy metrics
Per-category forecast comparison
Forecast accuracy and trend comparison across categories
Detailed forecast values with uncertainty bounds
Prophet model parameters and settings
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
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