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--- courses:psaw:lab_timeseries [2023/01/25 10:20] – [Materials] kkt
+++ courses:psaw:lab_timeseries [2025/04/08 08:18] (current) – [Learn more!] jeremi
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 ==== Materials ====
-  - Instead of a lecture, we go straight to the Q&A session with a short series of questions to "warm up" (based on the textbook). Also,  in the hands-on session, the most important content is summarized alongside the practical activities.
   - Q&A Session:
     - How time series data differ from other data?
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   * R.J. Hyndman & G. Athanasopoulos, G. -- [[https://otexts.com/fpp3/|Forecasting: principles and practice]] (OTexts, 2021) -- interesting book, full text online, code in R
+  * Brockwell, P.J., Davis, R.A. (Eds.), 2002. Introduction to Time Series and Forecasting, Springer Texts in Statistics. Springer New York, New York, NY. [[https://doi.org/10.1007/b97391|DOI:10.1007/b97391]], [[http://home.iitj.ac.in/~parmod/document/introduction%20time%20series.pdf| full text pdf]] -- a standard reference textbook; old but gold
+  * Aileen Nielsen, tutoriale video: [[https://youtu.be/zmfe2RaX-14|Time Series Analysis – PyCon 2017]], [[https://youtu.be/v5ijNXvlC5A|Modern Time Series Analysis – SciPy 2019]]
+  * [[https://cran.r-project.org/web/views/TimeSeries.html| CRAN Task View: Time Series Analysis]] -- description of almost all R packages for time series analysis
+  * [[https://github.com/timeseriesAI/tsai|State-of-the-art Deep Learning library for Time Series and Sequences]] -- one of Python libraries collecting neural network models for time series classification