# References / Books

Last but not least - we would like to mention some very good book references. It is not required or requested to buy any of these books. If you are enrolled as a student at CAU, FH Kiel or another university you might have access to these books via the SpringerLink of your home institution. They cover the course material quite well and provide GitHub-Repositories for the codes which where used throughout the book. Unfortunately there is no 1:1-Coursera course matching these books perfectly - but we will do our best ;-)

{% embed url="<https://link.springer.com/book/10.1007/978-1-4842-7150-6>" %}
<https://github.com/Apress/advanced-forecasting-python>
{% endembed %}

{% embed url="<https://link.springer.com/book/10.1007/978-1-4842-8978-5>" %}
<https://github.com/Apress/time-series-algorithm-recipes>
{% endembed %}

{% embed url="<https://link.springer.com/book/10.1007/978-1-4842-5992-4>" %}
<https://github.com/Apress/hands-on-time-series-analylsis-python>
{% endembed %}

{% embed url="<https://www.packtpub.com/product/machine-learning-for-time-series-with-python/9781801819626>" %}
<https://github.com/PacktPublishing/Machine-Learning-for-Time-Series-with-Python>
{% endembed %}

{% embed url="<https://www.packtpub.com/product/modern-time-series-forecasting-with-python/9781803246802?utm_source=github&utm_medium=repository&utm_campaign=9781803246802>" %}
<https://github.com/PacktPublishing/Modern-Time-Series-Forecasting-with-Python>
{% endembed %}

{% embed url="<https://www.packtpub.com/product/time-series-analysis-with-python-cookbook/9781801075541>" %}
<https://github.com/PacktPublishing/Time-Series-Analysis-with-Python-Cookbook>
{% endembed %}

{% embed url="<https://in.bpbonline.com/products/time-series-forecasting-using-deep-learning?_pos=1&_sid=670c171d5&_ss=r>" %}
<https://github.com/bpbpublications/Time-Series-Forecasting-using-Deep-Learning>
{% endembed %}

{% embed url="<https://link.springer.com/book/10.1007/978-3-031-13584-2>" %}
<https://github.com/QuantLet/pyTSA/>
{% endembed %}

{% embed url="<https://www.manning.com/books/time-series-forecasting-in-python-book?utm_source=marcopeix&utm_medium=affiliate&utm_campaign=book_peixeiro_time_10_21_21&a_aid=marcopeix&a_bid=8db7704f>" %}
<https://github.com/marcopeix/TimeSeriesForecastingInPython/tree/master>
{% endembed %}

Some additional course material and MOOCs if you want you expand your knowledge at your own pace:

{% embed url="<https://www.coursera.org/learn/practical-time-series-analysis>" %}
This is a nice time series course with a focus on R/RStudio. It goes slightly deeper then our first course but lacks machine learning techniques. Nevertheless a very good ranked course and worth doing it - if you like to deepen your knowledge.&#x20;
{% endembed %}

{% embed url="<https://otexts.com/fpp2/>" %}
This book is freely available and provides additional information. Focus on R/RStudio.
{% endembed %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://opencampus.gitbook.io/opencampus-machine-learning-program/courses/archive/time-series-prediction/references-books.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.