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Time series forecasting suite using statistical models

Project description

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Statistical ⚡️ Forecast

Lightning fast forecasting with statistical and econometric models

CI Python PyPi conda-nixtla License: GPLv3 docs

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.

Getting startedInstallation

🔥 Features

  • Fastest and most accurate auto_arima in Python and R (for the moment...).
  • 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.

📖 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.

dataset metric nixtla pmdarima [1] auto_arima_r prophet
M4-Daily MASE 3.26 3.35 4.46 14.26
M4-Daily time 1.41 27.61 1.81 514.33
M4-Hourly MASE 0.92 --- 1.02 1.78
M4-Hourly time 12.92 --- 23.95 17.27
M4-Weekly MASE 2.34 2.47 2.58 7.29
M4-Weekly time 0.42 2.92 0.22 19.82

[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.

group n_series mean_length std_length min_length max_length
Daily 4,227 2,371 1,756 107 9,933
Hourly 414 901 127 748 1,008
Weekly 359 1,035 707 93 2,610

⏲ 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.

📖 Documentation

Here is a link to the documentation.

🧬 Getting Started Open In Colab

Example Jupyter Notebook

💻 Installation


You can install the released version of StatsForecast from the Python package index with:

pip install statsforecast

(Installing inside a python virtualenvironment or a conda environment is recommended.)


Also you can install the released version of StatsForecast from conda with:

conda install -c nixtla statsforecast

(Installing inside a python virtualenvironment or a conda environment is recommended.)

Dev Mode If you want to make some modifications to the code and see the effects in real time (without reinstalling), follow the steps below:
git clone
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': np.arange(1, ap_train.size + 1),
        'y': ap_train
    index=pd.Index([0] * ap_train.size, name='unique_id')
def display_df(df):
fcst = StatsForecast(
    models=[(auto_arima, 12), (seasonal_naive, 12)], 
forecasts = fcst.forecast(12)
unique_id ds auto_arima_season_length-12 seasonal_naive_season_length-12
0 133 424.16 360
0 134 407.082 342
0 135 470.861 406
0 136 460.914 396
0 137 484.901 420
0 138 536.904 472
0 139 612.903 548
0 140 623.903 559
0 141 527.903 463
0 142 471.903 407
0 143 426.903 362
0 144 469.903 405
forecasts['y_test'] = ap_test
fig, ax = plt.subplots(1, 1, figsize = (20, 7))
pd.concat([series_train, forecasts]).set_index('ds').plot(ax=ax, linewidth=2)
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})
for label in (ax.get_xticklabels() + ax.get_yticklabels()):


🔨 How to contribute


📃 References

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

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