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
.
- Fastest and most accurate
auto_arima
inPython
andR
. - New!: Replace FB-Prophet in two lines of code and gain speed and accuracy. Check the experiments here.
- New!: Distributed computation in clusters with ray. (Forecast 1M series in 30min)
- New!: Good Ol' sklearn syntax with
AutoARIMA().fit(y).predict(h=7)
.
- Inclusion of
exogenous variables
andprediction intervals
. - Out of the box implementation of
exponential smoothing
,croston
,seasonal naive
,random walk with drift
andtbs
. - 20x faster than
pmdarima
. - 1.5x faster than
R
. - 500x faster than
Prophet
. - Compiled to high performance machine code through
numba
. - 1,000,000 series in 30 min with ray.
Missing something? Please open an issue or write us in
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.
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 |
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
.
You can reproduce the results here.
Results with external regressors are qualitatively similar to the reported before. You can find the complete experiments here.
Here is a link to the documentation.
PyPI
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.)
Conda
Also you can install the released version of StatsForecast
from conda with:
conda install -c conda-forge 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 https://github.com/Nixtla/statsforecast.git
cd statsforecast
pip install -e .
The following example needs ipython
and matplotlib
as additional packages. If not installed, install it via your preferred method, e.g. pip install ipython matplotlib
.
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)
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())
unique_id | ds | y | trend | intercept | month_2 | month_3 | month_4 | month_5 | month_6 | month_7 | month_8 | month_9 | month_10 | month_11 | month_12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1949-01-31 00:00:00 | 112 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
0 | 1949-02-28 00:00:00 | 118 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
0 | 1949-03-31 00:00:00 | 132 | 3 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
0 | 1949-04-30 00:00:00 | 129 | 4 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
0 | 1949-05-31 00:00:00 | 121 | 5 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
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
See CONTRIBUTING.md.
- The
auto_arima
model is based (translated) from the R implementation included in the forecast package developed by Rob Hyndman.
Thanks goes to these wonderful people (emoji key):
fede 💻 |
José Morales 💻 🚧 |
Sugato Ray 💻 |
Jeff Tackes 🐛 |
darinkist 🤔 |
Alec Helyar 💬 |
Dave Hirschfeld 💬 |
mergenthaler 💻 |
Kin 💻 |
Yasslight90 🤔 |
asinig 🤔 |
Philip Gillißen 💻 |
Sebastian Hagn 🐛 |
This project follows the all-contributors specification. Contributions of any kind welcome!