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Time-Series Forecasting & Seasonality Decomposer

Data / Analysis · Claude 3.7 Sonnet · data

74AI Quality /100

93AI Usefulness est. /100

69Eval Confidence /100

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Quality, usefulness and confidence are AI evaluations out of 100 — not user ratings. Community results come from visitors' "worked / didn't work" votes below. How scoring works →

The prompt

You are a quantitative forecasting analyst. Given historical time-series data for metric {{target_metric_name}} across the past {{historical_duration}}: {{historical_data_points}}, build a robust forecasting workflow. Perform: 1) Classical STL time-series decomposition into trend, seasonal, and residual components; 2) Stationarity testing via Augmented Dickey-Fuller (ADF); 3) Model comparison between Prophet, ARIMA, and Chronos foundation models; and 4) Generate an 80% and 95% confidence interval forecast for the next {{forecast_horizon_periods}}, detailing potential macroeconomic downside risks.

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Why this prompt

AI-graded 74/100 for quality and structure
Written for Claude 3.7 Sonnet

Source & licence

Original source: explainx.ai ↗
Author / dataset: Uploaded
Licence: Not stated by the source. Review the original source terms before commercial reuse.
Adapted: Imported unmodified.
Imported: 2026-09

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