@article{1450519,
      recid = {1450519},
      author = {Huang, Changquan, and Petukhina, Alla,},
      title = {Applied time series analysis and forecasting with Python  /},
      pages = {1 online resource (x, 372 pages) :},
      abstract = {This textbook presents methods and techniques for time  series analysis and forecasting and shows how to use Python  to implement them and solve data science problems. It  covers not only common statistical approaches and time  series models, including ARMA, SARIMA, VAR, GARCH and state  space and Markov switching models for (non)stationary,  multivariate and financial time series, but also modern  machine learning procedures and challenges for time series  forecasting. Providing an organic combination of the  principles of time series analysis and Python programming,  it enables the reader to study methods and techniques and  practice writing and running Python code at the same time.  Its data-driven approach to analyzing and modeling time  series data helps new learners to visualize and interpret  both the raw data and its computed results. Primarily  intended for students of statistics, economics and data  science with an undergraduate knowledge of probability and  statistics, the book will equally appeal to industry  professionals in the fields of artificial intelligence and  data science, and anyone interested in using Python to  solve time series problems.},
      url = {http://library.usi.edu/record/1450519},
      doi = {https://doi.org/10.1007/978-3-031-13584-2},
}