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Darts: Time Series Analysis made easy

 

Dart: Sci-kit learn for Time Series Analysis

It comprises all the API required for Time series analysis and its primary goal is to simplify the time series machine learning experience.

Darts in not inbuilt, so install it using "pip install u8darts"

In [4]:
import pandas as pd
from darts import TimeSeries

Like pandas has DataFrame, Similarly, Darts has TimeSeries

In [7]:
df = pd.read_csv('../input/air-passengers/AirPassengers.csv')

Series = TimeSeries.from_dataframe(df, 'Month', '#Passengers')

This timeseries is univariate, containing only one variable.

Splitting the series into training and validation TimeSeries

In [9]:
train, val = Series.split_before(pd.Timestamp('19580101'))

Models

All the time series models:

  • Exponential smoothing,
  • ARIMA & auto-ARIMA,
  • Facebook Prophet,
  • Theta method,
  • FFT (Fast Fourier Transform),
  • Recurrent neural networks (vanilla RNNs, GRU, and LSTM variants),
  • Temporal Convolutional Networks (TCN),
  • and several naive baselines.
In [10]:
from darts.models import ExponentialSmoothing

model = ExponentialSmoothing()
model.fit(train)
prediction = model.predict(len(val))

/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
In [11]:
import matplotlib.pyplot as plt

Series.plot(label = 'actual')
prediction.plot(label = 'forecast', lw = 3)
plt.legend()
Out[11]:
<matplotlib.legend.Legend at 0x7f72cde0c950>
In [12]:
from darts.models import AutoARIMA

model_aarima = AutoARIMA()
model_aarima.fit(train)
prediction_aarima = model_aarima.predict(len(val))

/opt/conda/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  "Check mle_retvals", ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/statespace/sarimax.py:975: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
/opt/conda/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  "Check mle_retvals", ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  "Check mle_retvals", ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  "Check mle_retvals", ConvergenceWarning)
In [13]:
Series.plot(label = 'actual')
prediction_aarima.plot(label = 'forecast_aarima', lw = 3)
plt.legend()
Out[13]:
<matplotlib.legend.Legend at 0x7f72ce41d810>
In [17]:
from darts.models import FFT

model_fft = FFT()
model_fft.fit(train)
prediction_fft = model_fft.predict(len(val))

/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/stattools.py:572: FutureWarning: fft=True will become the default in a future version of statsmodels. To suppress this warning, explicitly set fft=False.
  FutureWarning
In [18]:
Series.plot(label = 'actual')
prediction_fft.plot(label = 'forecast-fft', lw = 3)
plt.legend()
Out[18]:
<matplotlib.legend.Legend at 0x7f72cd726a50>
In [27]:
# facebook prophet model
from darts.models import Prophet

model_prophet = Prophet()
model_prophet.fit(train)
prediction_prophet = model_prophet.predict(len(val))
In [20]:
Series.plot(label = 'actual')
prediction_prophet.plot(label = 'forecast-prophet', lw = 3)
plt.legend()
Out[20]:
<matplotlib.legend.Legend at 0x7f72cdded9d0>

Backtesting

In [22]:
from darts.backtesting import backtest_forecasting

models = [ExponentialSmoothing(), Prophet()]

backtests = [backtest_forecasting(Series,
                                 model,
                                 pd.Timestamp('19550101'),
                                 fcast_horizon_n=3)
            for model in models]

/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)
/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  ConvergenceWarning)

Evaluation

In [26]:
from darts.metrics import mape

Series.plot(label='actual')
for i, m in enumerate(models):
    err = mape(backtests[i], Series)
    backtests[i].plot(lw = 3, label = '{}, MAPE = {:.2f}%'.format(m, err))
    
plt.title('Backtest with 3-months forecast horizon')
plt.legend()
Out[26]:
<matplotlib.legend.Legend at 0x7f72c227fa50>

Exponential smoothing is doing good with less error as compared to Prophet

Features

Currently, the library contains the following features:

Forecasting Models: A large collection of forecasting models; from statistical models (such as ARIMA) to deep learning models (such as N-BEATS). See table of models below.

Data processing: Tools to easily apply (and revert) common transformations on time series data (scaling, boxcox, …)

Metrics: A variety of metrics for evaluating time series' goodness of fit; from R2-scores to Mean Absolute Scaled Error.

Backtesting: Utilities for simulating historical forecasts, using moving time windows.

Regression Models: Possibility to predict a time series from lagged versions of itself and of some external covariate series, using arbitrary regression models (e.g. scikit-learn models).

Multiple series training: All neural networks, as well as RegressionModels (incl. LinearRegressionModel and RandomForest) support being trained on multiple series.

Past and Future Covariates support: Some models support past-observed and/or future-known covariate time series as inputs for producing forecasts.

Multivariate Support: Tools to create, manipulate and forecast multivariate time series.

Probabilistic Support: TimeSeries objects can (optionally) represent stochastic time series; this can for instance be used to get confidence intervals.

Filtering Models: Darts offers three filtering models: KalmanFilter, GaussianProcessFilter, and MovingAverage, which allow to filter time series, and in some cases obtain probabilistic inferences of the underlying states/values.

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