Data AnalystForecastingIntermediateSingle prompt

Prophet Forecast with Seasonality AI Prompt

Prophet Forecast with Seasonality is a intermediate prompt for forecasting. This prompt focuses on projecting future outcomes based on historical patterns in the data. It guides the AI to compare methods, state assumptions, and present forecasts with appropriate context and uncertainty. Use it when you need forward-looking estimates for planning, monitoring, or scenario analysis. It is best suited for direct execution against a real dataset. The requested output can include more technical detail, prioritization, and interpretation while still staying practical.

Prompt text
Build a time series forecast using Facebook Prophet on this dataset.

1. Prepare the data: rename the date column to 'ds' and the target column to 'y'
2. Configure Prophet with:
   - Yearly seasonality: auto-detect
   - Weekly seasonality: enabled if data frequency is daily
   - Country holidays: {{country_code}} if applicable
3. Split: use the last 20% of data as a test set
4. Fit the model on the training set and evaluate on the test set: report MAPE, MAE, and RMSE
5. Generate a forecast for the next {{forecast_horizon}} days with 80% and 95% uncertainty intervals
6. Plot: actual vs forecast, trend component, and seasonality components separately

When to use this prompt

Use case 01

When you need to estimate future values for a key metric.

Use case 02

When planning targets, capacity, budgets, or scenario ranges.

Use case 03

When comparing simple and advanced forecasting approaches on the same data.

Use case 04

When you need forecast assumptions, uncertainty, and commentary alongside the numbers.

What the AI should return

The AI should return forecast outputs in a structured format that includes method, assumptions, projected values, and a short interpretation of the trend. It should compare models or scenarios when requested, and include accuracy metrics or uncertainty intervals where possible. Charts and tables should support the explanation rather than replace it. The final answer should help the user understand both the forecast itself and how much confidence to place in it.

How to use this prompt

1

Open your data context

Load your dataset, notebook, or working environment so the AI can operate on the actual project context.

2

Copy the prompt text

Use the copy button above and paste the prompt into the AI assistant or prompt input area.

3

Review the output critically

Check whether the result matches your data, assumptions, and desired format before moving on.

4

Chain into the next prompt

Once you have the first result, continue deeper with related prompts in Forecasting.

Frequently asked questions

What does the Prophet Forecast with Seasonality prompt do?+

It gives you a structured forecasting starting point for data analyst work and helps you move faster without starting from a blank page.

Who is this prompt for?+

It is designed for data analyst workflows and marked as intermediate, so it works well as a guided starting point for that level of experience.

What type of prompt is this?+

Prophet Forecast with Seasonality is a single prompt. You can copy it as-is, adapt it, or use it as one step inside a larger workflow.

Can I use this outside MLJAR Studio?+

Yes. The prompt text works in other AI tools too, but MLJAR Studio is the best fit when you want local execution, visible Python code, and reusable notebooks.

What should I open next?+

Natural next steps from here are Demand Forecast with External Factors, Full Forecast Benchmark Chain, Growth Rate Analysis.