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.
Apply seasonal patterns only when conditions are met (e.g., weekday vs weekend patterns).
Independent holiday priors with customizable scales. Different effects for different events.
Auto-detects binary vs continuous regressors. Standardizes appropriately for optimal results.
Logistic growth with both upper (cap) and lower (floor) bounds for saturating forecasts.
Save and load trained models as JSON for deployment and reproducibility.
Automatically uses all CPU cores for faster model fitting. Scales with your hardware.
| 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 |
| Conditional Seasonality | Fully supported | Fully supported |
| Floor Parameter | Full support (logistic growth) | Full support |
| Regressor Standardization | Auto-detects binary/continuous | Manual configuration |
| Holiday Priors | Independent per-holiday scales | Independent per-holiday scales |
| Deployment | Minimal dependencies | Requires Stan, PyStan |
| API | Scikit-learn-like, Prophet-compatible | Scikit-learn-like |
# From PyPI
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)Different patterns for different conditions
from farseer import Farseer
m = Farseer()
# Add conditional seasonality
m.add_seasonality(
name='weekly_on_weekday',
period=7,
fourier_order=3,
condition_name='is_weekday'
)
# Add condition column
df['is_weekday'] = (df['ds'].dt.weekday < 5)
m.fit(df)Add additional variables to improve forecasts
from farseer import Farseer
m = Farseer()
# Auto-detects binary vs continuous
m.add_regressor('temperature', standardize='auto')
m.add_regressor('is_weekend', standardize='auto')
m.add_regressor('promo', prior_scale=5.0)
m.fit(train_df)
forecast = m.predict(test_df)Saturating growth with upper and lower bounds
from farseer import Farseer
# Logistic growth with floor
m = Farseer(growth='logistic')
df['floor'] = 1.5 # Minimum value
df['cap'] = 10.0 # Maximum value
m.fit(df)
future['floor'] = 1.5
future['cap'] = 10.0
forecast = m.predict(future)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 with independent priors
from farseer import Farseer
m = Farseer()
# Different priors for different holidays
m.add_holidays(
'christmas',
dates=['2020-12-25', '2021-12-25'],
prior_scale=20.0 # Strong effect
)
m.add_holidays(
'minor_event',
dates=['2020-03-17'],
prior_scale=5.0 # Weak effect
)
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
Optimize your forecasting workflow
Start forecasting with the speed and reliability of Rust