The document provides an overview of time series forecasting and compares two popular tools for forecasting: Facebook Prophet and DeepAR. It discusses what time series are, the challenges of time series forecasting, and how Prophet and DeepAR make forecasting easier. It also presents a use case predicting French energy consumption using both tools and adding weather data as a covariate. Key differences between the two tools are summarized in a table at the end.

1. Time Series made easy
Sameh Ben Fredj & Vincent Villet
November 28th, 2019
@Xebiconfr - #Xebicon19

2. Who are we ?
Sameh Ben Fredj Vincent Villet
2
Data Scientist Data Scientist

3. Agenda
● What's time series ?
● Forecasting made easy
○ Facebook Prophet
○ DeepAR
● Use Case
3

4. What’s time series?
4

5. A Series of data indexed in time order
5
Nbr of monthly airline passengers
Forecasting

6. Time Series Everywhere
6

7. Main challenges with forecasting
● Managing multiple time series models representing different problems
● Parameters difficult to interpret
● Difficulty to tune time series models (ex: ARIMA*)
● Companies lack experts in time series
* ARIMA: stands for Autoregressive Integrated Moving Average models.
Univariate ARIMA is a forecasting technique that projects the future values of a series based entirely on its own inertia.
7

8. Forecasting made easy
8

9. Facebook Prophet
For business time series
9

10. History & implementation
● One tool that can solve most of forecasting problems
● Open source
● First version 0.1 released on February 2017
● Last version 0.5 released on May 2019
● Paper ‘Forecasting at Scale’ published in 2018
● Python & R packages
● Core procedure implemented in Stan *
● pip install fbprophet
● > 9000 Github stars
* Stan: a probabilistic programming language in C++
*https://peerj.com/preprints/3190.pdf
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11. Suitable for ‘business’ time series
Main Characteristics:
● Changes in trend
● Seasonalities
● Outliers
11
*source: ‘Forecasting at scale’ paper

12. Some insights about the model
&
&
tendance saisonnalité vacances bruit
12
● Additive model
● Regressor like model
● Simple model
● Curve fitting
● Interpretable & intuitive variables

13. Easy API: Scikit-learn like
from fbprophet import Prophet
# model instantiation
m = Prophet()
# model fitting
m.fit(df_data)
# create future dates dataframe
future = m.make_future_dataframe(periods=365)
# predict on future dates
forecast = m.predict(future)
13

14. DeepAR
Time Series forecasting at scale
14

15. DeepAR history
● Paper published in April 2017 by Amazon*
● Open sourced within the package gluonTS in June 2019
● pip install gluonts
● Available on Amazon forecast.
*https://arxiv.org/abs/1704.04110
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16. DeepAR key features
● Train one global model for a set of related time series.
● Native modeling of covariates.
● Predictions as forecast distributions.
*https://arxiv.org/abs/1704.04110
16

17. Under the hood
RNN
Learning of the
probability
distribution
Target values covariates
+ Handling the training of
time series with different
scales
17

18. Very simple API to get started quickly
estimator = DeepAREstimator(freq=data_freq,
prediction_length=7*24,
trainer=Trainer(epochs=30, learning_rate=0.0001))
training_data = ListDataset(
[{"item_id":...,
"start": ...,
"target": ...}],
freq = "H")
estimator.train(training_data)
18
DeepA
R

19. from gluonts.evaluation.backtest import make_evaluation_predictions
test_data = ListDataset(
[{"item_id": ...,
"start": ...,
"target": ...}],
freq = "H")
forecast_it, ts_it = make_evaluation_predictions(test_data, predictor=predictor, num_eval_samples=100)
Very simple API to get started quickly
19
DeepA
R

20. Use Case
French energy forecasting
20

21. Prediction of energy consumption of French regions
*Source: https://opendata.reseaux-energies.fr/explore/dataset/eco2mix-regional-cons-def/information/?disjunctive.libelle_region&disjunctive.nature
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*Github: https://github.com/xebia-france/Xebicon19-time-series-made-easy

22. Prediction for Île-de-France
(29 epochs, LR=0.0001)
DeepA
R
22(Training on 2017 and 2018 data)

23. Prediction for Île-de-France trained with all
regions (29 epochs, LR=0.0001)
DeepA
R
23(Training on 2017 and 2018 data)

24. Prediction for Ile de France
24(Training on 2017 and 2018 data)

25. 25
SARIMA: Seasonal Autoregressive Integrated Moving Average
*Train on 2 months and prediction for 2 weeks*
Prophet vs SARIMA

26. Prophet vs SARIMA
26
*Train on 1 month and prediction for 3 days*

27. Adding weather data as covariate
27

28. estimator = DeepAREstimator(freq=data_freq,
prediction_length=7*24,
trainer=Trainer(epochs=30, learning_rate = 0.0001),
use_feat_dynamic_real=True)
training_data = ListDataset(
[{"item_id": ...,
"start": ...,
"target": ...,
"feat_dynamic_real": ...}],
freq = "H")
estimator.train(training_data)
Adding weather data as covariate
28
DeepA
R

29. Prediction with weather as covariate (29 epochs, LR=0.0001)
DeepA
R
29(Training on 2017 and 2018 data)

30. Adding weather covariate to Prophet model
from fbprophet import Prophet
# model instantiation
m = Prophet()
# add regressor
m.add_regressor(‘temp’)
# model fitting
m.fit(df_data)
30
e

31. Prediction with weather as covariate
31(Training on 2017 and 2018 data)

32. Conclusion
32

33. Comparative table
Prophet DeepAR
Installation - +
Documentation + -
Multiple time series - +
Complex seasonality + +
Probability distributions + +
Interpretability + -
Result stability + -
33

34. Thank you !
@Xebiconfr - #Xebicon19
Q&A
Feedbacks
34
Github: https://github.com/xebia-france/Xebicon19-time-series-
made-easy

35. Appendices
35

36. Prediction stability study
36
Example with a model trained on 20 epochs:

37. Beware: training results can be unstable
37
Example with 100 epochs:
DeepA
R
(Training on 2017 and 2018 data)

38. Beware: training results can be unstable
38
Example with 100 epochs:
DeepA
R
(Training on 2017 and 2018 data)

39. Beware: training results can be unstable
39
Example with 100 epochs:
DeepA
R
(Training on 2017 and 2018 data)

40. Beware: training results can be unstable
40
Example with 100 epochs:
DeepA
R
(Training on 2017 and 2018 data)

41. Beware: training results can be unstable
41
Example with 100 epochs:Example with 100 epochs:
DeepA
R
(Training on 2017 and 2018 data)

42. Beware: training results can be unstable
42
Example with 100 epochs:Example with 100 epochs:
DeepA
R
(Training on 2017 and 2018 data)

43. Beware: training results can be unstable
43
Example with 100 epochs:Example with 100 epochs:
DeepA
R
(Training on 2017 and 2018 data)

44. Deepar stability analysis
44(Training on 2017 and 2018 data)

45. Others Appendices
45

46. 46
SARIMA: Seasonal Autoregressive Integrated Moving Average
Train on 2 months and prediction for 2 weeks
Prophet vs SARIMA

47. Prophet vs SARIMA
47
Train on 1 months and prediction for 3 days

48. Easy API
● 1 Class to create a forcaster object: Prophet()
● Business params : growth, yearly_seasonality, holidays, etc.
● 4 main sub-packages:
○ fbprophet.diagnostics: evaluating model
○ fbprophet.make_holidays: adding holidays
○ fbprophet.models: importing prophet_stan_model
○ fbprophet.plot: plotting graphs
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