High-performance time series forecasting with Prophet-like capabilities.
Built in Rust for maximum speed, with a familiar Python API.
Rust-powered performance with automatic multithreading. Get forecasts in seconds, 5-10x faster than Prophet.
Bayesian approach with uncertainty intervals. Handles missing data, outliers, and trend shifts automatically.
Native support for observation weights. Emphasize recent data or downweight outliers with ease.
Works with both Polars (recommended, 5-10x faster) and Pandas DataFrames for backward compatibility.
Multiple trend types, custom seasonality, holiday effects, and regressors. Add your domain knowledge.
Nearly identical API to Prophet. Migrate your existing code with minimal changes.
| Feature | Farseer | Prophet |
|---|---|---|
| Performance | Rust-powered, 5-10x faster | Python/Stan |
| Multithreading | Automatic parallel optimization | Single-threaded by default |
| Weighted Data | Native support | Not directly supported |
| DataFrames | Polars + Pandas | Pandas only |
| Deployment | Minimal dependencies | Requires Stan, PyStan |
| API | Scikit-learn-like, Prophet-compatible | Scikit-learn-like |
# From PyPI (when published)
pip install farseer
# Development install from source
git clone https://github.com/ryanbieber/seer
cd seer
export PYO3_USE_ABI3_FORWARD_COMPATIBILITY=1 # For Python 3.13+
maturin develop --releasefrom farseer import Farseer
import polars as pl
from datetime import datetime
# Create data
df = pl.DataFrame({
'ds': pl.date_range(
datetime(2020, 1, 1),
periods=100,
interval='1d',
eager=True
),
'y': range(100)
})
# Fit and forecast
m = Farseer()
m.fit(df)
future = m.make_future_dataframe(periods=30)
forecast = m.predict(future)
print(forecast.select(['ds', 'yhat', 'yhat_lower', 'yhat_upper']).tail())from farseer import Farseer
import pandas as pd
# Create data
df = pd.DataFrame({
'ds': pd.date_range('2020-01-01', periods=100),
'y': range(100)
})
# Fit and forecast - same API!
m = Farseer()
m.fit(df)
future = m.make_future_dataframe(periods=30)
forecast = m.predict(future)
print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail())Give more importance to recent or reliable data
import polars as pl
import numpy as np
from farseer import Farseer
df = pl.DataFrame({
'ds': pl.date_range(...),
'y': np.random.randn(100).cumsum() + 50,
'weight': [2.0 if i < 50 else 1.0 for i in range(100)]
})
# Weights automatically detected!
m = Farseer()
m.fit(df)Add additional variables to improve forecasts
from farseer import Farseer
m = Farseer()
# Add custom regressors
m.add_regressor('temperature', prior_scale=10.0)
m.add_regressor('is_weekend', mode='additive')
m.add_regressor('promo', prior_scale=5.0)
m.fit(train_df)
forecast = m.predict(test_df)Specify known trend changes in your data
from farseer import Farseer
# Specify exact changepoint dates
changepoints = ['2021-01-01', '2022-01-01']
m = Farseer(
changepoints=changepoints,
yearly_seasonality=True
)
m.fit(df)
forecast = m.predict(future)Model special events and holidays
import polars as pl
from farseer import Farseer
holidays = pl.DataFrame({
'ds': pl.date_range(...),
'holiday': ['Christmas', 'New Year', ...],
'lower_window': 0,
'upper_window': 1
})
m = Farseer(holidays=holidays)
m.fit(df)Get started with Farseer on your system
Learn the basics in 5 minutes
Complete API documentation
Learn from real-world examples
Switch from Prophet to Farseer
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Start forecasting with the speed and reliability of Rust