prophet

$npx mdskill add mkurman/zorai/prophet

Forecast time series data with seasonality and trend.

  • Predicts future values using yearly, weekly, and daily patterns.
  • Detects changepoints and adjusts for holidays automatically.
  • Handles missing data and outliers without manual cleaning.
  • Delivers interpretable plots showing trend and seasonal components.
SKILL.md
.github/skills/prophetView on GitHub ↗
---
name: prophet
description: "Meta Prophet — forecasting at scale. Additive model with yearly/weekly/daily seasonality, holiday effects, changepoints, and trend decomposition. Handles missing data and outliers automatically."
tags: [prophet, forecasting, time-series, seasonality, trend, meta, zorai]
---
## Overview

Meta Prophet forecasts time series data with additive seasonality (yearly, weekly, daily), holiday effects, changepoint detection, and trend decomposition. Handles missing data and outliers automatically. Designed for business forecasting with human-interpretable components.

## Installation

```bash
uv pip install prophet
```

## Forecast

```python
import pandas as pd
from prophet import Prophet
import numpy as np

df = pd.DataFrame({
    "ds": pd.date_range("2023-01-01", periods=365, freq="D"),
    "y": [100 + i*0.5 + 10*(i%7==0) + np.random.normal(0, 5) for i in range(365)],
})

model = Prophet(yearly_seasonality=True, weekly_seasonality=True)
model.fit(df)

future = model.make_future_dataframe(periods=90)
forecast = model.predict(future)

model.plot(forecast)
model.plot_components(forecast)
```

## Holidays

```python
model = Prophet()
model.add_country_holidays("US")
model.fit(df)
```

## References
- [Prophet docs](https://facebook.github.io/prophet/)
- [Prophet GitHub](https://github.com/facebook/prophet)
More from mkurman/zorai