High-performance time series forecasting with Prophet-like capabilities. Built in Rust for maximum speed, with a familiar Python API.

Python 3.11+ Rust-powered

Why Farseer?

🚀

Blazing Fast

Rust-powered performance with automatic multithreading. Get forecasts in seconds, 5-10x faster than Prophet.

🎯

Accurate & Robust

Bayesian approach with uncertainty intervals. Handles missing data, outliers, and trend shifts automatically.

⚖️

Weighted Observations

Native support for observation weights. Emphasize recent data or downweight outliers with ease.

📊

Polars & Pandas

Works with both Polars (recommended, 5-10x faster) and Pandas DataFrames for backward compatibility.

🔧

Fully Tunable

Multiple trend types, custom seasonality, holiday effects, and regressors. Add your domain knowledge.

🔄

Prophet Compatible

Nearly identical API to Prophet. Migrate your existing code with minimal changes.

📅

Conditional Seasonality

Apply seasonal patterns only when conditions are met (e.g., weekday vs weekend patterns).

🎄

Flexible Holidays

Independent holiday priors with customizable scales. Different effects for different events.

📈

Smart Regressors

Auto-detects binary vs continuous regressors. Standardizes appropriately for optimal results.

📏

Floor & Cap

Logistic growth with both upper (cap) and lower (floor) bounds for saturating forecasts.

💾

Model Serialization

Save and load trained models as JSON for deployment and reproducibility.

⚡

Parallel Optimization

Automatically uses all CPU cores for faster model fitting. Scales with your hardware.

Farseer vs Prophet

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

Quick Start

Installation

# 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 --release

Basic Usage (Polars - Recommended)

from 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())

Prophet-Compatible (Pandas)

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())

Examples

🎯 Weighted Observations

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)

Learn more →

� Conditional Seasonality

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)

Learn more →

�📈 Custom Regressors

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)

Learn more →

� Floor & Cap

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)

Learn more →

�🔄 Manual Changepoints

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)

Learn more →

🎄 Holiday Effects

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)

Learn more →

Ready to get started?

Start forecasting with the speed and reliability of Rust