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| courses:psaw:lab_timeseries [2023/01/25 10:23] – [Materials] kkt | courses:psaw:lab_timeseries [2025/04/08 08:18] (current) – [Learn more!] jeremi |
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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 | * 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 |