
Forecasting: Principles and Practice - yarapavan
https://otexts.org/fpp2/
======
asavinov
The book focuses on classical (statistical) methods of forecasting. In this
sense, it provides the fundamental notions needed to deal with practical
problems. Real world problems are much more complicated and first of all
because of the natural of source data which is not limited by univariate
numeric data. In most practical cases the success depends on the ability to
extract (manually or automatically) good features from heterogeneous data
sources. There exist the following frameworks for that purpose:

o [https://github.com/asavinov/lambdo](https://github.com/asavinov/lambdo) \-
Combines feature engineering and data mining with strong focus on time series
analysis

o [https://github.com/blue-yonder/tsfresh](https://github.com/blue-
yonder/tsfresh) \- Automatically extract informative features (also from time
series)

~~~
natalyarostova
The feature stuff works for time series classification. But still doesn't help
with forecasting more than one step ahead.

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ryanmonroe
FYI the authors of this book are authors of the "forecast" R package (used in
the book, but also popular in general)

[https://github.com/robjhyndman/forecast](https://github.com/robjhyndman/forecast)

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jdewsnip
If you are interested in forecasting then the Makridakis competitions are
worth taking a look at.

* [https://en.wikipedia.org/wiki/Makridakis_Competitions](https://en.wikipedia.org/wiki/Makridakis_Competitions)

* [https://www.researchgate.net/publication/325901666/download](https://www.researchgate.net/publication/325901666/download)

* [https://github.com/M4Competition/M4-methods](https://github.com/M4Competition/M4-methods)

* [https://journals.plos.org/plosone/article/file?type=printabl...](https://journals.plos.org/plosone/article/file?type=printable&id=10.1371/journal.pone.0194889)

* [https://eng.uber.com/m4-forecasting-competition/](https://eng.uber.com/m4-forecasting-competition/)

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Ftuuky
What would you all recommend as a good book in forecasting and time series
analysis using Python?

~~~
rogue7
Not a book but a nice article:
[https://tomaugspurger.github.io/modern-7-timeseries](https://tomaugspurger.github.io/modern-7-timeseries)

He points to books (including this one) as well as python libraries in the
"Resources" section at the end.

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knbknb
One of the authors, Prof. Hyndman, was also a moderator on
stats.stackexchange.com .

See

[https://stats.stackexchange.com/users/159/rob-
hyndman](https://stats.stackexchange.com/users/159/rob-hyndman)

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monkeydust
Do people rate the Facebook prophet library for time series forecasting?

~~~
amrrs
`prophet` is a really good library for time series forecasting. It's
especially useful when speed matters where you create an Rshiny forecasting
tool. It's not always the best result though. Sometimes a simple exponential
smoothing could give a better result too.

