Nixtla    

Statistical ⚡️ Forecast

Lightning fast forecasting with statistical and econometric models

StatsForecast offers a collection of widely used univariate time series forecasting models, including exponential smoothing and automatic ARIMA modeling optimized for high performance using numba.

🔥 Features

• Fastest and most accurate auto_arima in Python and R.
• New!: Good Ol' sklearn syntax with AutoARIMA().fit(y).predict(h=7).
• New!: Inclusion of exogenous variables.
• New!: Inclusion of prediction intervals.
• Out of the box implementation of other classical models and benchmarks like exponential smoothing, croston, sesonal naive, random walk with drift and tbs.
• 20x faster than pmdarima.
• 1.5x faster than R.
• 500x faster than Prophet.
• Compiled to high performance machine code through numba.

Missing something? Please open an issue or write us in

📖 Why?

Current Python alternatives for statistical models are slow and inaccurate. So we created a library that can be used to forecast in production environments or as benchmarks. StatsForecast includes an extensive battery of models that can efficiently fit thousands of time series.

🔬 Accuracy

We compared accuracy and speed against: pmdarima, Rob Hyndman's forecast package and Facebook's Prophet. We used the Daily, Hourly and Weekly data from the M4 competition.

The following table summarizes the results. As can be seen, our auto_arima is the best model in accuracy (measured by the MASE loss) and time, even compared with the original implementation in R.

[1] The model auto_arima from pmdarima had problems with Hourly data. An issue was opened in their repo.

The following table summarizes the data details.

⏲ Computational efficiency

We measured the computational time against the number of time series. The following graph shows the results. As we can see, the fastest model is our auto_arima.

Nixtla vs Prophet

You can reproduce the results here.

External regressors

Results with external regressors are qualitatively similar to the reported before. You can find the complete experiments here.

👾 Less code

📖 Documentation

Here is a link to the documentation.

🧬 Getting Started

Example Jupyter Notebook

💻 Installation

pip install statsforecast

conda install -c conda-forge statsforecast

git clone https://github.com/Nixtla/statsforecast.git
cd statsforecast
pip install -e .

🧬 How to use

import numpy as np
import pandas as pd
from IPython.display import display, Markdown

import matplotlib.pyplot as plt
from statsforecast import StatsForecast
from statsforecast.models import seasonal_naive, auto_arima
from statsforecast.utils import AirPassengers

horizon = 12
ap_train = AirPassengers[:-horizon]
ap_test = AirPassengers[-horizon:]

series_train = pd.DataFrame(
    {
        'ds': pd.date_range(start='1949-01-01', periods=ap_train.size, freq='M'),
        'y': ap_train
    },
    index=pd.Index([0] * ap_train.size, name='unique_id')
)

fcst = StatsForecast(
    series_train, 
    models=[(auto_arima, 12), (seasonal_naive, 12)], 
    freq='M', 
    n_jobs=1
)
forecasts = fcst.forecast(12, level=(80, 95))

forecasts['y_test'] = ap_test

fig, ax = plt.subplots(1, 1, figsize = (20, 7))
df_plot = pd.concat([series_train, forecasts]).set_index('ds')
df_plot[['y', 'y_test', 'auto_arima_season_length-12_mean', 'seasonal_naive_season_length-12']].plot(ax=ax, linewidth=2)
ax.fill_between(df_plot.index, 
                df_plot['auto_arima_season_length-12_lo-80'], 
                df_plot['auto_arima_season_length-12_hi-80'],
                alpha=.35,
                color='green',
                label='auto_arima_level_80')
ax.fill_between(df_plot.index, 
                df_plot['auto_arima_season_length-12_lo-95'], 
                df_plot['auto_arima_season_length-12_hi-95'],
                alpha=.2,
                color='green',
                label='auto_arima_level_95')
ax.set_title('AirPassengers Forecast', fontsize=22)
ax.set_ylabel('Monthly Passengers', fontsize=20)
ax.set_xlabel('Timestamp [t]', fontsize=20)
ax.legend(prop={'size': 15})
ax.grid()
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
    label.set_fontsize(20)

Adding external regressors

series_train['trend'] = np.arange(1, ap_train.size + 1)
series_train['intercept'] = np.ones(ap_train.size)
series_train['month'] = series_train['ds'].dt.month
series_train = pd.get_dummies(series_train, columns=['month'], drop_first=True)

display_df(series_train.head())

xreg_test = pd.DataFrame(
    {
        'ds': pd.date_range(start='1960-01-01', periods=ap_test.size, freq='M')
    },
    index=pd.Index([0] * ap_test.size, name='unique_id')
)

xreg_test['trend'] = np.arange(133, ap_test.size + 133)
xreg_test['intercept'] = np.ones(ap_test.size)
xreg_test['month'] = xreg_test['ds'].dt.month
xreg_test = pd.get_dummies(xreg_test, columns=['month'], drop_first=True)

fcst = StatsForecast(
    series_train, 
    models=[(auto_arima, 12), (seasonal_naive, 12)], 
    freq='M', 
    n_jobs=1
)
forecasts = fcst.forecast(12, xreg=xreg_test, level=(80, 95))

forecasts['y_test'] = ap_test

🔨 How to contribute

See CONTRIBUTING.md.

📃 References

• The auto_arima model is based (translated) from the R implementation included in the forecast package developed by Rob Hyndman.

Contributors ✨

Thanks goes to these wonderful people (emoji key):

fede 💻	José Morales 💻 🚧	Sugato Ray 💻	Jeff Tackes 🐛	darinkist 🤔	Alec Helyar 💬	Dave Hirschfeld 💬
mergenthaler 💻	Kin 💻	Yasslight90 🤔	asinig 🤔

This project follows the all-contributors specification. Contributions of any kind welcome!