smooth 1.0.0

Python version of the smooth forecasting library

smooth (Python)

Python implementation of the smooth package for time series forecasting using Single Source of Error (SSOE) state-space models.

Installation

From PyPI (recommended):

pip install smooth

From source (development):

pip install "git+https://github.com/config-i1/smooth.git@master#subdirectory=python"

See the Installation Guide for platform-specific instructions.

System Requirements

If installing from source, this package requires compilation of C++ extensions. Before installing, ensure you have:

• C++ compiler (g++, clang++, or MSVC)
• CMake >= 3.25
• Armadillo linear algebra library

Quick Example

import numpy as np
from smooth import ADAM

# Sample data
y = np.array([10, 12, 15, 13, 16, 18, 20, 19, 22, 25, 28, 30,
              11, 13, 16, 14, 17, 19, 21, 20, 23, 26, 29, 31])

# Fit ADAM model with additive error, no trend, no seasonality
model = ADAM(model="ANN")
model.fit(y)

# Generate forecasts
forecasts = model.predict(h=12)

# With seasonal component (monthly data, annual seasonality)
model = ADAM(model="ANA", lags=[1, 12])
model.fit(y)
forecasts = model.predict(h=12)

Documentation

• GitHub Wiki - Full documentation
• ADAM - Main unified ETS/ARIMA framework
• Installation Guide - Dependencies and troubleshooting

Book: Svetunkov, I. (2023). Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM). Chapman and Hall/CRC. Online: https://openforecast.org/adam/

See Also

• R package on CRAN - Production-ready R implementation